CN117098906A

CN117098906A - Adaptive Drill String Condition Determination

Info

Publication number: CN117098906A
Application number: CN202180083805.5A
Authority: CN
Inventors: P·古塔罗夫; L·瓦雷; B·L·谢瓦利尔
Original assignee: Schlumberger Technology Corp
Current assignee: Schlumberger Technology Corp
Priority date: 2020-10-16
Filing date: 2021-10-15
Publication date: 2023-11-21
Also published as: WO2022082227A1; CA3198834A1; US20220120176A1; EP4229268A1

Abstract

A method may include identifying a threshold for determination of a drill string off-bottom condition; filtering the time series surface data of the post-connection drilling state of the drill string using a threshold to generate filtered time series surface data; and statistically determining a drill string off-bottom condition value using the filtered time-series surface data.

Description

Adaptive drill string condition determination

Related applications

The present application claims priority and benefit from U.S. provisional application serial No. 63/093,022, filed on 10/16 of 2020, which is hereby incorporated by reference.

Background

The resource farm can be an aggregate, pool, or pool group of one or more resources (e.g., oil, gas, oil, and gas) in a subsurface environment. The resource field may include at least one reservoir. The reservoir may be shaped in a manner that is capable of capturing hydrocarbons and may be covered by impermeable or sealed rock. A borehole may be drilled in an environment where it may be used to form a well that may be used to produce hydrocarbons from a reservoir.

The drilling rig may be a system of components that may be operated to form a borehole in an environment, transport equipment into and out of the borehole in the environment, and the like. As an example, a drilling rig may include a system that may be used to drill holes and obtain information about the environment, about drilling, and so forth. The resource field may be a land field, an offshore field, or both land and offshore fields. The drilling rig may comprise means for performing onshore and/or offshore operations. The drilling rig may be, for example, vessel-based, offshore platform-based, onshore, etc.

Oilfield planning may occur at one or more stages, which may include exploration stages intended to identify and evaluate an environment (e.g., foreground zone, a reservoir formation (play), etc.), which may include drilling one or more boreholes (e.g., one or more exploration wells, etc.). Other stages may include evaluation, development, and production stages.

Disclosure of Invention

A method may include identifying a threshold for determination of a drill string off-bottom condition; filtering the time series surface data of the post-connection drilling state of the drill string using a threshold to generate filtered time series surface data; and statistically determining a drill string off-bottom condition value using the filtered time-series surface data. A system may include a processor; a memory accessible to the processor; processor executable instructions stored in memory and executable to instruct the system to identify a threshold value for determination of a drill string bottoming condition; filtering the time series surface data of the post-connection drilling state of the drill string using a threshold to generate filtered time series surface data; and statistically determining a drill string off-bottom condition value using the filtered time-series surface data. The one or more computer-readable storage media may include processor-executable instructions to instruct a computing system to identify a threshold value for determination of a drill string bottoming condition; filtering the time series surface data of the post-connection drilling state of the drill string using a threshold to generate filtered time series surface data; and statistically determining a drill string off-bottom condition value using the filtered time-series surface data. Various other devices, systems, methods, etc. are also disclosed.

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

Drawings

The features and advantages of the described embodiments may be more readily understood by reference to the following description taken in conjunction with the accompanying drawings.

FIG. 1 illustrates an example of a device in a geological environment;

FIG. 2 shows an example of a device and an example of a hole type;

FIG. 3 shows an example of a system;

FIG. 4 shows an example of a system;

FIG. 5 illustrates an example of a graphical user interface;

FIG. 6 shows an example of a graphical user interface;

FIG. 7 shows an example of a system;

FIG. 8 shows an example of a method and an example of a graph;

FIG. 9 shows an example of a graph that references the graph of FIG. 8;

FIG. 10 shows an example of a graph of various tracks including time series data and other information;

FIG. 11 shows an example of a graph relating to time series data;

FIG. 12 shows an example of a graph relating to time series data;

FIG. 13 shows an example of a graph relating to time series data;

FIG. 14 illustrates an example of a method and an example of a system;

FIG. 15 shows an example of a method;

FIG. 16 shows an example of a system;

FIG. 17 illustrates an example of a graphical user interface;

FIG. 18 illustrates an example of a graphical user interface;

FIG. 19 shows an example of a graphical user interface;

FIG. 20 illustrates an example of a graphical user interface;

FIG. 21 illustrates an example of a graphical user interface;

FIG. 22 illustrates an example of a graphical user interface;

FIG. 23 shows an example of a graphical user interface;

FIG. 24 shows an example of a graphical user interface;

FIG. 25 shows an example of a data table;

FIG. 26 shows an example of a data table;

FIG. 27 shows an example of a method;

FIG. 28 illustrates an example of a well construction ecosystem including one or more machine learning model systems;

FIG. 29 illustrates an example of a computing system; and

FIG. 30 illustrates example components of a system and networking system.

Detailed Description

The following description includes the best mode presently contemplated for practicing the described embodiments. The description is not to be taken in a limiting sense, but is made merely for the purpose of describing the general principles of the embodiments. The scope of the described embodiments should be determined with reference to the claims that issue.

Fig. 1 illustrates an example of a geological environment 120. In fig. 1, the geological environment 120 may be a sedimentary basin including a layer (e.g., a hierarchy) that includes a reservoir 121 and may be intersected, for example, by a fault 123 (e.g., a fault or faults). As an example, the geological environment 120 may be equipped with various sensors, detectors, actuators, and the like. As an example, the device 122 may include communication circuitry to receive and transmit information about one or more networks 125. Such information may include information associated with the downhole device 124, which downhole device 124 may be a device that obtains information, facilitates resource recovery, and the like. Other devices 126 may be remote from the wellsite and include sensing, detection, transmission, or other circuitry. Such devices may include storage and communication circuitry to store and communicate data, instructions, and the like. As an example, one or more pieces of equipment may provide measurement, collection, communication, storage, analysis, etc. of data (e.g., for one or more produced resources, etc.). As an example, one or more satellites may be provided for communication, data acquisition, and the like. As an example, fig. 1 shows a satellite in communication with a network 125, which may be configured for communication, note that the satellite may additionally or alternatively include circuitry for imaging (e.g., spatial, spectral, temporal, radiometric, etc.).

FIG. 1 also shows a geological environment 120 optionally including devices 127 and 128 associated with a well that includes a substantially horizontal portion that may intersect one or more fractures 129. As an example, consider a well in a shale formation, which may include natural fractures, artificial fractures (e.g., hydraulic fractures), or a combination of natural and artificial fractures. As an example, a well may be drilled for a laterally expanding reservoir. In such examples, lateral variations in characteristics, stresses, etc. may exist, where evaluation of such variations may facilitate planning, operation, etc. to develop the reservoir (e.g., by fracturing, injection, extraction, etc.). As an example, the devices 127 and/or 128 may include components, systems, etc. for fracturing, seismic sensing, seismic data analysis, evaluation of one or more fractures, injection, production, etc. As an example, devices 127 and/or 128 may provide data, such as measurements, collection, communication, storage, analysis, etc., of production data (e.g., for one or more produced resources). By way of example, one or more satellites may be provided for communication, data acquisition, and the like.

Fig. 1 also shows an example of device 170 and an example of device 180. Such a device may be a system of components that may be suitable for use in the geological environment 120. Although the devices 170 and 180 are illustrated as land-based, the various components may be adapted for use in an offshore system.

The apparatus 170 includes a platform 171, a derrick 172, an overhead crane 173, a rope (line) 174, a trolley assembly 175, a winch 176, and a landing stage 177 (e.g., a racking stage). As an example, the rope 174 may be controlled, at least in part, by a winch 176 such that the carriage assembly 175 travels in a vertical direction relative to the platform 171. For example, by retracting the rope 174, the winch 176 may pass the rope 174 through the overhead crane 173 and lift the trolley assembly 175 off the platform 171 toward the sky; however, by allowing the rope 174 to extend, the winch 176 may pass the rope 174 through the overhead traveling crane 173 and lower the cruise carriage assembly 175 toward the platform 171. Where the rover assembly 175 carries a pipe (e.g., a cannula, etc.), the motion tracking of the rover 175 may provide an indication of how much pipe has been deployed.

The derrick may be a structure for supporting a crown block and a traveling block operably coupled to the crown block at least in part by a rope. The derrick may be pyramid-shaped and provide a suitable strength to weight ratio. The derrick may be moved (e.g., assembled and disassembled) as a unit or in a piece-by-piece manner.

As an example, the winch may include a reel, a brake, a power source, and associated auxiliary equipment. The winch may controllably pay out and retract the rope. The rope may be wound on a crown block and coupled to the balance block to obtain mechanical advantage in the form of a "pulley block" or "pulley". The payout and retraction of the rope may cause the rover (e.g., and anything suspended below it) to be lowered into or lifted from the borehole. The payout ropes may be driven by gravity and the retraction ropes may be driven by a motor, engine, etc. (e.g., electric motor, diesel engine, etc.).

As an example, the crown block may include a set of pulleys (e.g., sheaves) that may be at or near the top of the derrick or mast through which the rope passes. The trolley may comprise a set of sheaves which are movable up and down in the derrick or mast via ropes threaded into the set of sheaves of the trolley and the set of sheaves of the crown block. Crown blocks, traveling blocks, and ropes may form a pulley system for a derrick or mast that may enable handling heavy loads (e.g., drill string, pipe, casing, liner, etc.) to be lifted from or placed into a borehole. By way of example, the rope may be about one centimeter to about five centimeters in diameter, such as a steel cable. By using a set of sheaves, such ropes can carry a heavier load than the rope can support as a single strand.

As an example, a derrick man may be a member of a rig crew working on a platform attached to a derrick or mast. The derrick may include a landing stage upon which a derrick man may stand. As an example, such landing stage may be about 10 meters or more above the drill floor (rig floor). In an operation known as tripping out of the wellbore (TOH), a derrick man may wear a safety belt that enables it to be tripped out of a work landing (e.g., a racking platform) to reach a pipe at or near the center of the derrick or mast, and to cast a line around the pipe and pull it back to its storage location (e.g., a fingerboard), for example, until such time as it may be desired to put the pipe back into the borehole. As an example, a drilling rig may include automatic pipe handling equipment such that a derrick man controls the machine rather than physically handling the pipe.

As an example, tripping may refer to the act of pulling the device out of the borehole and/or placing the device into the borehole. As an example, the apparatus may include a drill string that may be pulled from and/or placed or replaced in a hole (hole). As an example, tubing tripping may be performed in the event that the drill bit has become dull or has stopped actively drilling and needs replacement.

Fig. 2 shows an example of a wellsite system 200 (e.g., at a wellsite that may be onshore or offshore). As shown, wellsite system 200 may include a mud pot 201 for containing mud and other materials (e.g., where the mud may be drilling fluid), a suction line 203 that serves as an inlet for a mud pump 204 to pump the mud from mud pot 201 to flow the mud to a vibration hose 206, a winch 207 for pulling one or more drilling lines 212 with a winch, a riser 208 that receives the mud from vibration hose 206, a kelly hose 209 that receives the mud from riser 208, one or more goosenecks 210, a trolley 211, a crown 213 for carrying trolley 211 via one or more drilling lines 212 (see, e.g., crown 173 of fig. 1), a derrick 214 (see, e.g., derrick 172 of fig. 1), a kelly 218 or top drive 240, a kelly drive sleeve 219, a rotary table 220, a rig 221, a bell nipple 222, one or more blowout preventers (BOPs) 223, a drill string 225, a drill bit 226, a casing head 227, and a flowtube 228 that carries the mud and other materials to, e.g., mud pot 201.

In the example system of fig. 2, a borehole 232 is formed in a subterranean formation 230 by rotary drilling; note that various example embodiments may also use directional drilling.

As shown in the example of fig. 2, a drill string 225 is suspended within the borehole 232 and has a drill string assembly 250 that includes a drill bit 226 at a lower end thereof. By way of example, the drill string assembly 250 may be a Bottom Hole Assembly (BHA).

The wellsite system 200 may provide for operation of the drill string 225 and other operations. As shown, wellsite system 200 includes a platform 211 and a derrick 214 positioned over a borehole 232. As mentioned, wellsite system 200 may include rotary table 220 with drill string 225 passing through an opening in rotary table 220.

As shown in the example of fig. 2, the wellsite system 200 may include a kelly 218 and associated components, etc., or a top drive 240 and associated components. With respect to the kelly example, the kelly 218 may be a square or hexagonal metal/alloy rod with holes drilled therein for use as a slurry flow path. The kelly 218 may be used to transfer rotational motion from the rotary table 220 to the drill string 225 via the kelly drive sleeve 219 while allowing the drill string 225 to be lowered or raised during rotation. The kelly 218 may pass through a kelly drive sleeve 219, which may be driven by a rotary table 220. As an example, the rotary table 220 may include a main bushing that is operably coupled to the kelly drive bushing 219 such that rotation of the rotary table 220 may rotate the kelly drive bushing 219 and thus the kelly 218. The kelly drive sleeve 219 may include an inner profile (e.g., square, hexagonal, etc.) of the kelly 218 that matches an outer profile; but slightly oversized so that the kelly 218 is free to move up and down within the kelly drive sleeve 219.

Regarding an example of a top drive, the top drive 240 may provide the functions performed by the kelly and the rotary table. The top drive assembly 240 may rotate the drill string 225. As an example, the top drive 240 may comprise one or more motors (e.g., electric and/or hydraulic motors) connected by suitable gearing to a small length of tubing called a quill, which in turn may be screwed into the guard sub or the drill string 225 itself. The top drive 240 may be suspended from the trolley 211 so that the rotary mechanism is free to move up and down on the derrick 214. As an example, the top drive 240 may allow drilling with more joint columns (stands) than the drill pipe/rotary table method.

In the example of fig. 2, a mud tank 201 may hold mud, which may be one or more types of drilling fluids. As an example, a wellbore may be drilled to produce fluids, inject fluids, or both (e.g., hydrocarbons, minerals, water, etc.).

In the example of fig. 2, the drill string 225 (e.g., comprising one or more downhole tools) may be comprised of a series of pipes threaded together to form a long pipe with the drill bit 226 at its lower end. When the drill string 225 is advanced into the wellbore for drilling, at some point prior to or concurrent with drilling, mud may be pumped by the pump 204 from the mud tank 201 (e.g., or other source) via lines 206, 208, and 209 to ports of the kelly 218, or, for example, to ports of the top drive 240. The mud may then flow out of ports located on the drill bit 226 (see, e.g., directional arrows) via channels (e.g., multiple channels) in the drill string 225. As the mud exits the drill string 225 via ports in the drill bit 226, it may then circulate upward through an annular region between the outer surface of the drill string 225 and the surrounding wall (e.g., open hole, casing, etc.), as indicated by the directional arrows. In this manner, the mud lubricates the drill bit 226 and carries thermal energy (e.g., friction or other energy) and formation cuttings to the surface where the mud (e.g., and cuttings) may be returned to the mud tank 201, for example, for recirculation (e.g., for treatment to remove cuttings, etc.).

The mud pumped by the pump 204 into the drill string 225 may form a mud cake lining the wellbore after exiting the drill string 225, which may be particularly functional to reduce friction between the drill string 225 and surrounding walls (e.g., borehole, casing, etc.). The reduction in friction may assist in advancing or retracting the drill string 225. During drilling operations, the entire drill string 225 may be pulled out of the wellbore and optionally replaced with, for example, a new or sharp drill bit, a smaller diameter drill string, or the like. As mentioned, the act of pulling or setting the drill string out of the hole back into the hole is referred to as tripping. Depending on the tripping direction, tripping may be referred to as tripping up or tripping out, or tripping down or tripping in.

As an example, consider a down-hole wherein, when the drill bit 226 of the drill string 225 reaches the bottom of the wellbore, pumping of mud begins to lubricate the drill bit 226 in order to drill an enlarged wellbore. As mentioned, mud may be pumped by pump 204 into the passage of drill string 225, and as the passage is filled, the mud may be used as a transmission medium for transmitting energy, e.g., energy that may encode information as in mud pulse telemetry.

For example, a mud pulse telemetry device may include a downhole device configured to affect pressure changes in the mud to generate acoustic waves or waves that may modulate information. In such examples, information from downhole equipment (e.g., one or more modules of drill string 225) may be transmitted uphole to an uphole device, which may relay such information to other equipment for processing, control, and the like.

As an example, the telemetry device may operate by energy transfer through the drill string 225 itself. As an example, consider a signal generator that delivers an encoded energy signal to the drill string 225 and a repeater that can receive and repeat such energy to further transmit the encoded energy signal (e.g., information, etc.).

By way of example, the drill string 225 may be equipped with a telemetry device 252, the telemetry device 252 comprising a rotatable drive shaft; a turbine wheel mechanically coupled to the drive shaft such that the slurry may cause the turbine wheel to rotate; a modulator rotor mechanically coupled to the drive shaft such that rotation of the turbine wheel causes the modulator rotor to rotate; a modulator stator mounted adjacent or near the modulator rotor such that rotation of the modulator rotor relative to the modulator stator generates pressure pulses in the mud; and a controllable actuator for selectively braking rotation of the modulator rotor to modulate the pressure pulses. In such an example, an alternator may be coupled to the aforementioned drive shaft, wherein the alternator includes at least one stator winding electrically coupled to the control circuit to selectively short the at least one stator winding to electromagnetically brake the alternator to selectively brake rotation of the modulator rotor to modulate pressure pulses in the slurry.

In the example of fig. 2, the uphole control and/or data acquisition system 262 may include circuitry to sense pressure pulses generated by the telemetry device 252 and, for example, to communicate the sensed pressure pulses or information derived therefrom for processing, control, and the like.

The assembly 250 of the illustrated example includes a Logging While Drilling (LWD) module 254, a Measurement While Drilling (MWD) module 256, an optional module 258, a Rotary Steerable System (RSS) and/or motor 260, and a drill bit 226. Such components or modules may be referred to as tools, wherein the drill string may comprise a plurality of tools.

As far as RSS is concerned, it relates to techniques for directional drilling. Directional drilling involves drilling into the earth to form a deviated borehole such that the trajectory of the borehole is not vertical; instead, the trajectory deviates from the vertical along one or more portions of the borehole. As an example, consider a target that is located at a lateral distance from the ground location where the rig may be located. In such examples, the drilling may begin at the vertical portion and then deviate from the vertical so that the borehole is aimed at the target and eventually reaches the target. Where the target is inaccessible from a vertical position at the ground surface of the earth, where there are materials in the earth that may obstruct drilling or otherwise be detrimental (e.g., considering salt domes, etc.), where the formation extends laterally (e.g., considering relatively thin but laterally extending reservoirs), where multiple boreholes are to be drilled from a single ground surface, where relief wells are required, etc., directional drilling may be implemented.

One method of directional drilling involves a mud motor; however, mud motors may face challenges depending on factors such as rate of penetration (ROP), weight transfer to the drill bit due to friction (e.g., weight on bit, WOB), and so forth. The mud motor may be a Positive Displacement Motor (PDM) that operates to drive the drill bit (e.g., during directional drilling, etc.). As drilling fluid is pumped through the PDM, the PDM operates, wherein the PDM converts hydraulic power of the drilling fluid to mechanical power to rotate the drill bit.

As an example, the PDM may operate in a combined rotation mode, wherein the surface equipment is used to rotate the drill bit (e.g., rotary table, top drive, etc.) of the drill string by rotating the entire drill string, and wherein the drill bit of the drill string is rotated with drilling fluid. In such examples, the Surface RPM (SRPM) may be determined using surface equipment, and the downhole RPM of the mud motor may be determined using various factors related to the flow of drilling fluid, the mud motor type, and the like. As an example, in a combined rotation mode, assuming that the SRPM and the mud motor RPM are in the same direction, the bit RPM may be determined or estimated as the sum of the SRPM and the mud motor RPM.

As an example, the PDM mud motor may be operated in a so-called slip mode when the drill string is not rotating from the surface. In such examples, the bit RPM may be determined or estimated based on the RPM of the mud motor.

RSS can orient drilling where rotation from surface equipment continues, which can mitigate slippage of steerable motors (e.g., PDMs). RSS can be deployed when drilling directional wells (e.g., deviated, horizontal, or large displacement wells). RSS can minimize interactions with the borehole wall, which helps to maintain borehole quality. RSS may be intended to apply a relatively consistent lateral force, similar to a stabilizer rotating with a drill string, or to orient the drill bit in a desired direction while continuously rotating at the same rpm as the drill string.

The LWD module 254 may be housed in a suitable type of drill collar and may contain one or more selected types of logging tools. It should also be appreciated that more than one LWD and/or MWD module may be employed, for example, as shown by module 256 of drill string assembly 250. Where reference is made to the location of an LWD module, it may refer to a module located at the LWD module 254, module 256, etc., as examples. The LWD module may include the capability to measure, process, and store information, as well as the capability to communicate with surface equipment. In the example shown, the LWD module 254 may include a seismic survey apparatus.

The MWD module 256 may be housed in a suitable type of drill collar and may contain one or more devices for measuring characteristics of the drill string 225 and drill bit 226. By way of example, the MWD tool 254 may include equipment for generating electrical power, for example, powering various components of the drill string 225. By way of example, MWD tool 254 may include telemetry device 252, for example, where a turbine wheel may generate electricity through the flow of mud; it should be appreciated that other power sources and/or battery systems may be employed to power the various components. By way of example, the MWD module 256 may include one or more of the following types of measurement devices, weight on bit measurement devices, torque measurement devices, vibration measurement devices, shock measurement devices, stick-slip measurement devices, direction measurement devices, and inclination measurement devices.

Fig. 2 also shows some examples of the types of holes that may be drilled. As examples, consider inclined holes 272, S-shaped holes 274, deep inclined holes 276, and horizontal holes 278.

As an example, the drilling operation may include directional drilling, wherein, for example, at least a portion of the well includes a curved axis. As an example, consider defining a radius of curvature, wherein the inclination with respect to the vertical may vary until an angle between about 30 degrees and about 60 degrees is reached, or for example an angle of about 90 degrees or possibly more than about 90 degrees is reached.

As an example, a directional well may include several shapes, each of which is intended to meet specific operational requirements. As an example, when information is communicated to a drilling engineer, a drilling process may be performed based on the information. As an example, the inclination and/or direction may be modified based on information received during drilling.

As an example, deflection of the borehole may be achieved in part by using one or more of RSS, downhole motor, and/or turbine. For example, for motors, the drill string may include a Positive Displacement Motor (PDM).

As an example, the system may be a steerable system and include devices that perform methods such as geosteering. As an example, the steerable system may include a PDM or turbine located in a lower portion of the drill string, over which a bent sub may be installed. As an example, above the PDM, MWD equipment and/or LWD equipment that provide real-time or near real-time data of interest (e.g., inclination, direction, pressure, temperature, actual weight on bit, torque stress, etc.) may be installed. For the latter, the LWD device may send various types of data of interest to the surface, including, for example, geological data (e.g., gamma ray logging, resistivity, density, sonic logging, etc.).

Coupling of sensors providing information about the path of the wellbore trajectory in real-time or near real-time with one or more logs characterizing the formation, e.g., from a geological standpoint, may allow geosteering methods to be implemented. Such methods may include navigating the subsurface environment, e.g., along a desired route to a desired target or targets.

As an example, the drill string may include an Azimuthal Density Neutron (ADN) tool for measuring density and porosity; MWD tools for measuring inclination, azimuth and vibration; a Compensating Dual Resistivity (CDR) tool for measuring resistivity and gamma ray related phenomena; one or more variable thickness stabilizers; one or more curved sub; and a geosteering tool, which may include a motor and optional equipment, for measuring and/or responding to one or more of inclination, resistivity, and gamma ray related phenomena.

As an example, geosteering may include intentional directional control of a wellbore based on the results of downhole geologic logging measurements in a manner that aims to maintain a directional wellbore within a desired area, zone (e.g., producing reservoir), etc. As an example, geosteering may include guiding a wellbore to maintain the wellbore in a particular portion of a reservoir, e.g., to minimize gas and/or water breakthrough, and, e.g., to maximize economic production from a well including the wellbore.

Referring again to fig. 2, wellsite system 200 may include one or more sensors 264 operably coupled to control and/or data acquisition system 262. As an example, one or more sensors may be located at a ground location. As an example, one or more sensors may be located at a downhole location. As an example, one or more sensors may be located at one or more remote locations that are not within a distance on the order of about 100 meters from wellsite system 200. As an example, one or more sensors may be located at a offset wellsite (offset wellsite), where wellsite system 200 and the offset wellsite are located in a common field (e.g., an oil and/or gas field).

As an example, one or more sensors 264 may be provided to track tubing, track movement of at least a portion of a drill string, and the like.

As an example, the system 200 may include one or more sensors 266, and the sensors 266 may sense and/or transmit signals to a fluid conduit, such as a drilling fluid conduit (e.g., a drilling mud conduit). As an example, in system 200, one or more sensors 266 may be operably coupled to a portion of riser 208 through which mud flows. As an example, the downhole tool may generate pulses that may pass through the mud and be sensed by one or more of the one or more sensors 266. In such examples, the downhole tool may include associated circuitry, such as encoding circuitry, that may encode signals, for example, to reduce the need for transmission. As an example, the surface circuitry may include decoding circuitry to decode at least a portion of the encoded information transmitted via mud pulse telemetry. As an example, the surface circuitry may include encoder circuitry and/or decoder circuitry, and the downhole circuitry may include encoder circuitry and/or decoder circuitry. As an example, the system 200 may include a transmitter capable of generating a signal that may be transmitted downhole via mud (e.g., drilling fluid) as a transmission medium.

As an example, one or more portions of the drill string may become stuck. The term "stuck" may refer to one or more of varying degrees of inability to move or remove the drill string from the borehole. As an example, in a stuck situation the drill rod may be rotated or lowered back into the borehole, or, for example, in a stuck situation the drill string may not be axially moved in the borehole, although there may be some degree of rotation. As an example, in a stuck condition, at least a portion of the drill string may not be axially and rotationally movable.

By the term "stuck pipe" it may be meant that a portion of the drill string cannot rotate or move axially. As an example, a condition known as "differential pressure stuck" may be a condition in which the drill string is unable to move (e.g., rotate or reciprocate) along the axis of the borehole. Differential sticking can occur when high contact forces caused by low reservoir pressure, high wellbore pressure, or both are applied over a sufficiently large drill string area. Differential pressure sticking can be time and capital consuming.

As an example, the stuck force may be the product of the pressure differential between the wellbore and the reservoir and the area over which the pressure differential acts. This means that applying a relatively low pressure difference (Δp) over a large working area is as effective as applying a higher pressure difference over a small area.

As an example, a condition known as "mechanical stuck" may be one in which drill string movement is limited or prevented by a mechanism other than the occurrence of a differential pressure stuck. Mechanical sticking may be caused by, for example, one or more of debris in the hole, wellbore geometry anomalies, cuttings build-up in the cement, key ways, or annulus.

FIG. 3 illustrates an example of a system 300 that includes various devices for evaluating 310, planning 320, engineering 330, and operations 340. For example, the drilling workflow framework 301, the seismic simulation framework 302, the technical data framework 303, and the drilling framework 304 may be implemented to perform one or more processes, such as evaluating the formation 314, evaluating the process 318, generating the trajectory 324, verifying the trajectory 328, formulating the constraints 334, designing equipment and/or processes based at least in part on the constraints 338, performing the drilling 344, and evaluating the drilling and/or the formation 348.

In the example of fig. 3, the seismic simulation framework 302 may be, for example, a PETREL framework (Schlumberger, houston, tx), and the technical data framework 303 may be, for example, a TECHLOG framework (Schlumberger, houston, tx).

As an example, the frame may include entities, which may include land entities, geological objects, or other objects, such as wells, the ground, reservoirs, and the like. An entity may comprise a virtual representation of an actual physical entity reconstructed for one or more of the purposes of evaluation, planning, engineering, operation, and the like.

The entities may include entities (e.g., seismic data and/or other information) based on data obtained by sensing, observation, etc. The entity may be characterized by one or more characteristics (e.g., a geometric columnar grid entity of the earth model may be characterized by porosity characteristics). Such characteristics may represent one or more measurements (e.g., acquired data), calculations, and the like.

The frame may be an object-based frame. In such a framework, the entities may include entities based on predefined categories, e.g., to facilitate modeling, analysis, simulation, etc. An example of an object-based framework is the MICROSOFT. NET framework (Redmond, washington) which provides a set of extensible object classes. In the NET framework, object classes encapsulate modules of reusable code and related data structures. The object class may be used to instantiate an object instance for use by a program, script, or the like. For example, a borehole class may define an object for representing a borehole based on well data.

As an example, the framework may include an analysis component that can allow interaction with the model or model-based results (e.g., simulation results, etc.). As for simulation, the framework may be operably linked to or include a simulator, such as an ECLIPSE reservoir simulator (Schlumberger, houston, texas), an intersec reservoir simulator (Schlumberger, houston, texas), or the like.

The PETREL framework described above provides an assembly that allows for optimization of exploration and development operations. The PETREL framework includes seismic simulation software components that can output information for improving reservoir performance, for example, by improving the productivity of an asset team. By using such a framework, various professionals (e.g., geologists, well engineers, reservoir engineers, etc.) can develop collaborative workflows and integrate operations to simplify the flow. Such a framework may be considered an application and may be considered a data-driven application (e.g., where data is input for modeling, simulation, etc.).

As an example, one or more of the frames may be interoperable and/or run on one or another frame. As an example, consider a framework environment sold as OCEAN framework environment (Schlumberger, houston, tx) that allows for the integration of add-on components (or plug-ins) into PETREL framework workflow. The OCEAN framework environment utilizes NET tools (Microsoft corporation, redmond, washington) to provide a stable, user-friendly interface for efficient development. In an example embodiment, the various components may be implemented as additional components (or plug-ins) that conform to and operate in accordance with a specification of the framework environment (e.g., in accordance with an Application Programming Interface (API) specification, etc.).

As an example, the framework may include a model simulation layer, a framework services layer, a framework core layer, and a module layer. The framework may include an OCEAN framework, wherein the model simulation layer may include or be operably linked to a PETREL model-centric software package that hosts the OCEAN framework application. In an example embodiment, PETREL software may be considered a data driven application. PETREL software may include a framework for model construction and visualization. Such a model may include one or more grids.

As an example, the model simulation layer may provide domain objects, act as data sources, provide rendering, and provide various user interfaces. The presentation may provide a graphical environment in which applications may display their data, while the user interface may provide a common look and feel to application user interface components.

As an example, the domain objects may include entity objects, property objects, and optionally other objects. The physical objects may be used to geometrically represent wells, surfaces, reservoirs, etc., while the property objects may be used to provide property values as well as data versions and display parameters. As an example, the entity object may represent a well, where the property object provides logging information as well as version information and display information (e.g., the well is shown as part of a model).

As an example, data may be stored in one or more data sources (or data stores, typically physical data storage devices), which may be located at the same or different physical sites, and may be accessed through one or more networks. As an example, the model simulation layer may be configured to model items. In this way, specific items may be stored, where the stored item information may include inputs, models, results, and situations. Thus, upon completion of the modeling session, the user may store the item. Later, the items can be accessed and restored using the model simulation layer, which can recreate instances of related domain objects.

As an example, the system 300 may be used to perform one or more workflows. A workflow may be a process that includes many work steps. The work steps may operate on the data, e.g., create new data, update existing data, etc. As an example, the workflow may operate on one or more inputs and create one or more results, for example, based on one or more algorithms. As an example, the system may include a workflow editor for creation, editing, execution, etc. of a workflow. In such examples, the workflow editor may provide for selection of one or more predefined work steps, one or more custom work steps, and the like. By way of example, the workflow may be a workflow that is at least partially implementable in PETREL software, e.g., that operates on seismic data, seismic attributes, and the like.

By way of example, the seismic data may be data obtained by a seismic survey in which sources and receivers are placed in a geological environment to transmit and receive seismic energy, at least a portion of which may be reflected from subsurface structures. As an example, a seismic data analysis framework or frameworks (e.g., an OMEGA framework considered to be sold by Schlumberger, houston, texas) may be used to determine depth, extent, characteristics, etc. of the subsurface structure. As an example, the seismic data analysis may include forward modeling and/or inversion, for example, to iteratively model a subsurface region of a geological environment. As an example, the seismic data analysis frame may be part of or operatively coupled to a seismic simulation frame (e.g., PETREL frame, etc.).

As an example, a workflow may be a process that is at least partially implementable in the OCEAN framework. By way of example, a workflow may include one or more work steps of accessing a module such as a plug-in (e.g., external executable code, etc.).

As an example, the framework may provide modeling of petroleum systems. For example, modeling block racks sold as PETROMOD framework (Schlumberger, houston, tx) include features for inputting various types of information (e.g., seismic, well, geological, etc.) to model the evolution of sedimentary basins. The PETROMOD framework provides petroleum system modeling by inputting various data (e.g., seismic data, well data, and other geological data), such as modeling the evolution of sedimentary basins. The PETROMOD framework can predict whether and how the reservoir is filled with hydrocarbons, including, for example, the source and time of hydrocarbon generation, migration routes, quantity, pore pressure, and hydrocarbon type under subsurface or surface conditions. In conjunction with a framework such as a PETREL framework, a workflow can be constructed to provide a basin-to-perspective scale exploration solution. Data exchange between frameworks can facilitate model construction, analysis of data (e.g., PETROMOD framework data using PETREL framework capability analysis), and joining of workflows.

As mentioned, the drill string may include various tools that may make measurements. As an example, measurements may be made using a cable tool or other type of tool. As an example, the tool may be configured to acquire an electrical drill hole image. As an example, a full borehole Formation Microimager (FMI) tool (Schlumberger, houston, tx) may acquire borehole image data. The data acquisition sequence of such a tool may include feeding the tool into the borehole with the acquisition pad closed, opening the acquisition pad and pressing the acquisition pad against the borehole wall, translating the tool in the borehole while delivering an electrical current into the material defining the borehole, and remotely sensing the electrical current, which is altered by interaction with the material.

Analysis of formation information may reveal features such as cavities, dissolving surfaces (e.g., dissolving along a bedding plane), stress-related features, dip events, and the like. As an example, the tool may obtain information that helps characterize the reservoir, alternatively the reservoir is a fractured reservoir where the fracture may be natural and/or artificial (e.g., hydraulic fracture). As an example, a framework, such as a techolog framework, may be used to analyze information acquired by one or more tools. As an example, the techolog framework may interoperate with one or more other frameworks (e.g., PETREL frameworks).

As examples, various aspects of the workflow may be accomplished automatically, may be accomplished partially automatically, or may be accomplished manually, such as by a human user interacting with a software application. As an example, the workflow may be cyclical and may include four phases such as an evaluation phase (see, e.g., evaluation device 310), a planning phase (see, e.g., planning device 320), an engineering phase (see, e.g., engineering device 330), and an execution phase (see, e.g., operation device 340), as examples. As an example, the workflow may begin at one or more stages, which may proceed to one or more other stages (e.g., in a serial manner, a parallel manner, a loop manner, etc.).

As an example, the workflow may begin with an evaluation phase, which may include a geological service provider evaluating the formation (e.g., see evaluation block 314). As an example, a geological service provider may use a computing system executing a software package tailored to such activity to conduct formation evaluation; alternatively, for example, one or more other suitable geologic platforms may be employed (e.g., alternatively or additionally). As an example, a geological service provider may evaluate the formation, for example, using a land model, a land physical model, a basin model, a petroleum technology model, combinations thereof, and the like. Such models may take into account a variety of different inputs including offset well data, seismic data, guide well data, other geological data, and the like. The models and/or inputs may be stored in a database maintained by a server and accessed by a geological service provider.

As an example, the workflow may proceed to a geological and ground physical ("G & G") service provider, which may generate a well trajectory (e.g., see generation block 324), which may involve execution of one or more G & G software packages. Examples of such software packages include PETREL frameworks. As an example, the G & G service provider may determine the well trajectory or a portion thereof based on one or more models provided by, for example, formation evaluation (e.g., according to evaluation block 314), and/or other data accessed, for example, from one or more databases (e.g., maintained by one or more servers, etc.). As an example, the well trajectory may take into account various "design base" (BOD) constraints, such as general surface location, target (e.g., reservoir) location, etc. As an example, the trajectory may contain information about the tool, bottom hole assembly, casing size, etc., which may be used to drill the well. The determination of the wellbore trajectory may take into account various other parameters including risk tolerance, fluid weight and/or plan, bottom hole pressure, drilling time, etc.

As an example, the workflow may proceed to a first engineering service provider (e.g., one or more processors associated therewith) that may verify the well trajectory and, for example, the relief well design (e.g., see verification block 328). Such verification processes may include evaluating physical characteristics, calculations, risk tolerances, integration with other aspects of the workflow, and the like. As an example, one or more parameters for such determination may be maintained by the server and/or the first engineering service provider; note that one or more models, well trajectories, etc. may be maintained by the server and accessed by the first engineering service provider. For example, the first engineering service provider may include one or more computing systems executing one or more software packages. As an example, where the first engineering service provider refuses or suggests an adjustment to the wellbore trajectory, the wellbore trajectory may be adjusted or a message or other notification requesting such modification may be sent to the G & G service provider.

As an example, one or more engineering service providers (e.g., first, second, etc.) may provide casing designs, bottom Hole Assembly (BHA) designs, fluid designs, and/or the like to implement wellbore trajectories (e.g., see design block 338). In some embodiments, the second engineering service provider may use one or more software applications to perform such design. Such designs may be stored in one or more databases maintained by one or more servers, which may, for example, employ a STUDIO framework tool, and may be accessed by one or more other service providers in the workflow.

As an example, the second engineering service provider may seek approval of the one or more designs established with the well trajectory by the third engineering service provider. In such examples, the third engineering service provider may consider various factors regarding whether the well engineering plan is acceptable, such as economic variables (e.g., oil production predictions, cost per barrel, risk, drilling time, etc.), and may request a payout grant, such as from a representative of the operating company, a representative of the oil well owner, etc. (see, e.g., formulation block 334). As an example, at least some of the data on which such a determination is based may be stored in one or more databases maintained by one or more servers. As an example, the first, second, and/or third engineering service provider may be provided by a single team of engineers or even a single engineer, and thus may or may not be a separate entity.

As an example, in the event that economics may be unacceptable or that an authorization is denied, the engineering service provider may recommend changing casing, bottom hole assembly, and/or fluid designs, or otherwise notify and/or return control to a different engineering service provider so that adjustments may be made to the casing, bottom hole assembly, and/or fluid designs. Where modifying one or more such designs is not feasible within well constraints, trajectories, etc., the engineering service provider may suggest adjustments to the well trajectory, and/or the workflow may return or otherwise inform the initial engineering service provider and/or G & G service provider so that either or both may modify the well trajectory.

As an example, the workflow may include consideration of well trajectories, including accepted well engineering plans and formation evaluations. Such a workflow may then pass control to a drilling service provider, which may implement a well engineering plan, establish safe and effective drilling, maintain well integrity, and report progress and operational parameters (see, e.g., blocks 344 and 348). As an example, the operating parameters, the formations encountered, the data collected while drilling (e.g., using logging while drilling or measurement while drilling techniques) may be returned to the geological service provider for evaluation. As an example, the geological service provider may then re-evaluate one or more other aspects of the well trajectory or well project plan, and in some cases, and possibly within predetermined constraints, may adjust the well project plan according to the actual drilling parameters (e.g., based on field obtained data, etc.).

Depending on the particular embodiment, the workflow may proceed to a post-inspection (see, e.g., evaluation block 318) whether the well is fully drilled or a portion thereof is completed. As an example, post-inspection may include inspecting drilling performance. As an example, post-inspection may also include reporting drilling performance (e.g., to one or more associated engineering, geological, or G & G service providers).

The various activities of the workflow may be performed continuously and/or may be performed out of order (e.g., based in part on information from templates, nearby wells, etc., filling in a gap in information to be provided by another service provider). As an example, performing one activity may affect the outcome or basis of another activity, and thus may require changes in one or more workflow activities, work products, etc., manually or automatically. As an example, the server may allow information to be stored on a central database accessible to various service providers, where changes may be sought by communicating with the appropriate service provider, may be made automatically, or may otherwise appear as suggestions to the relevant service provider. Such a method may be considered an overall method of well workflow, as compared to a sequential, fragmented method.

As an example, various actions of the workflow may be repeated multiple times during drilling of the wellbore. For example, in one or more automated systems, feedback from drilling service providers may be provided in real-time or near real-time, and data acquired during drilling may be fed to one or more other service providers, which may adjust one of their workflows accordingly. Such adjustments may be infiltrated into the workflow, for example, in an automated fashion, as dependencies may exist in other areas of the workflow. In some embodiments, the cycling process may additionally or alternatively be performed after a certain drilling objective is reached, such as completing a section of the wellbore, and/or after drilling a complete wellbore, or daily, weekly, monthly, etc.

Well planning may include determining a path of a well that may extend into a reservoir, for example, to economically produce fluids such as hydrocarbons from the reservoir. The well plan may include selecting drilling and/or completion components that may be used to implement the well plan. As an example, as part of a well plan, various constraints may be imposed that may affect the design of the well. As an example, such constraints may be imposed based at least in part on information about known geology of a subsurface region within an area (e.g., considering crashes), the presence of one or more other wells (e.g., actual and/or planned, etc.), and so on. As an example, one or more constraints may be imposed based at least in part on the characteristics of one or more tools, components, etc. As an example, the one or more constraints may be based at least in part on factors associated with drilling time and/or risk tolerance.

As an example, the system may allow for reduced waste, e.g., may be defined in terms of the ean. In the context of LEAN, consider one or more types of wastage, such as transportation (e.g., unnecessary movement of items, whether physical or data); inventory (e.g., components, whether physical or informative, as work-in-process, and as raw finished goods); motion (e.g., a person or device unnecessarily moves or walks to perform a desired process); waiting (e.g., waiting for information, production interruption during shift change, etc.); overproduction (e.g., production lead demand for materials, information, equipment, etc.); overtaching (e.g., caused by bad tooling or product design creation activities); and defects (e.g., work involved in inspecting and repairing defects, whether in planning, data, equipment, etc.). As an example, a system that allows actions (e.g., methods, workflows, etc.) to be performed in a collaborative manner may help reduce one or more types of wastage.

As an example, a system may be utilized to implement a method for facilitating distributed well engineering, planning, and/or drilling system design across multiple computing devices, where collaboration may be performed between various different users (e.g., some local, some remote, some mobile, etc.). In such a system, various users via appropriate devices may be operably coupled via one or more networks (e.g., local and/or wide area networks, public and/or private networks, land-based, sea-based, and/or regional networks, etc.).

By way of example, the system may allow well engineering, planning, and/or drilling system design to proceed through a subsystem approach, wherein the wellsite system is comprised of various subsystems, which may include equipment subsystems and/or operational subsystems (e.g., control subsystems, etc.). By way of example, the computations may be performed using various computing platforms/devices operatively coupled via communication links (e.g., network links, etc.). As an example, one or more links may be operably coupled to a common database (e.g., server site, etc.). As an example, a particular one or more servers may manage the receipt of notifications from and/or the release of notifications to one or more devices. As an example, a system may be implemented for an item, where the system may output a well plan, e.g., as a digital well plan, a paper well plan, a digital and paper well plan, etc. Such well plans may be complete well engineering plans or designs for a particular project.

FIG. 4 illustrates an example of a system 400, the system 400 including various components that may be local to the wellsite and including various components that may be remote from the wellsite. As shown, system 400 includes a reconciliation block 402, an integration block 404, a core and service block 406, and a device block 408. These blocks may be labeled in one or more ways other than shown in the example of fig. 4. In the example of fig. 4, blocks 402, 404, 406, and 408 may be defined by one or more of operational characteristics, functions, relationships in an architecture, and the like.

As an example, blocks 402, 404, 406, and 408 may be described as a pyramid architecture, where from vertex to bottom, a pyramid includes a reconciliation block 402, an integration block 404, a core and service block 406, and a device block 408.

As an example, the reconciliation box 402 may be associated with a well management layer (e.g., well planning and/or reconciliation) and may be associated with a rig management layer (e.g., rig dynamics planning and/or reconciliation). As an example, the integration block 404 may be associated with a process management layer (e.g., a rig integration execution). As an example, the core and service box 406 may be associated with a data management layer (e.g., sensors, instrumentation, inventory, etc.). As an example, the equipment block 408 may be associated with a wellsite equipment layer (e.g., a wellsite subsystem, etc.).

As an example, the coordination block 402 may receive information from a drilling workflow framework and/or one or more other sources, which may be remote from the wellsite.

In the example of fig. 4, coordination block 402 includes a plan/reschedule block 422, a coordination/arbitration block 424, and a local resource management block 426. In the example of fig. 4, the integration block 404 includes an integration execution block 444, which integration execution block 444 may include or be operably coupled to blocks of various subsystems of the wellsite, such as a drilling subsystem, a mud management subsystem (e.g., a hydraulic subsystem), a casing subsystem (e.g., a casing and/or completion subsystem), and, for example, one or more other subsystems. In the example of fig. 4, core and service box 406 includes a data management and real-time service box 464 (e.g., real-time or near real-time services) and a rig and cloud security box 468 (e.g., regarding provisioning and various types of security measures, etc.). In the example of fig. 4, device box 408 is shown as being capable of providing various types of information to core and service box 406. For example, consider information from rig floor sensors, LWD/MWD sensors, mud logging sensors, rig control systems, rig equipment, personnel, materials, and the like. In the example of fig. 4, block 470 may provide one or more of data visualization, automatic alarm, automatic reporting, and the like. As an example, block 470 may be operably coupled to core and service block 406 and/or one or more other blocks.

As mentioned, a portion of the system 400 may be remote from the wellsite. For example, a teleoperational command center box 492, a database box 493, a drilling workflow box 494, an SAP/ERP box 495, and a field service delivery box 496 appear on one side of the dashed line. The various blocks, which may be remote, may be operatively coupled to one or more blocks local to the wellsite system. By way of example, a communication link 412 is shown in the example of fig. 4 that may operatively couple blocks 406 and 492 (e.g., with respect to monitoring, remote control, etc.), while another communication link 414 is shown in the example of fig. 4 that may operatively couple blocks 406 and 496 (e.g., with respect to device delivery, device service, etc.). Various other examples of possible communication links are also shown in the example of fig. 4.

As an example, the system 400 of fig. 4 may be a field management tool. As an example, the system 400 of fig. 4 may include a drilling frame (see, e.g., drilling frame 304). As an example, blocks in the system 400 of fig. 4 may be remote from the wellsite.

As an example, drilling may be performed according to a drilling plan established prior to drilling. Such a drilling plan may be a drilling plan or a portion thereof, and equipment, pressures, trajectories, and/or other parameters defining a wellsite drilling process may be proposed. As an example, drilling operations may then be performed according to a drilling plan (e.g., a well plan). As an example, as information is collected, drilling operations may deviate from a drilling plan. In addition, as drilling or other operations proceed, subsurface conditions may change. Specifically, as new information is collected, the sensors may transmit data to one or more surface units. As an example, the surface unit may automatically use such data to update the drilling plan (e.g., local and/or remote).

As an example, the drilling workflow framework 494 may be or include a G & G system and a well planning system. As an example, the G & G system corresponds to hardware, software, firmware, or a combination thereof that provides support for geology and land physics. In other words, geologist knowing the reservoir may use a G & G system to decide where to drill a well, which creates a three-dimensional model of the subsurface formation and includes simulation tools. The G & G system may communicate the well trajectory and other information selected by the geologist to the well planning system. The well plan system corresponds to hardware, software, firmware, or a combination thereof that generates a well plan. In other words, the well plan may be an advanced drilling program for the well. The well plan system may also be referred to as a well plan generator.

In the example of fig. 4, the various blocks may be components corresponding to one or more software modules, hardware infrastructure, firmware, devices, or any combination thereof. Communication between components may be local or remote, direct or indirect, via application programming interfaces and procedure calls, or through one or more communication channels.

As an example, the various blocks in the system 400 of fig. 4 may correspond to a level of granularity to control operations associated with equipment and/or personnel in a field. As shown in fig. 4, system 400 may include a reconciliation block 402 (e.g., for well plan execution), an integration block 404 (e.g., a set of process managers), a core and service block 406, and a device block 408.

The reconciliation block 402 may be referred to as a well plan execution system. For example, a well plan execution system corresponds to hardware, software, firmware, or a combination thereof that performs overall coordination of well construction processes, such as coordination of drilling rigs and management of drilling rigs and rig equipment. The well plan execution system may be configured to obtain an overall well plan from the well plan system and convert the overall well plan to a detailed well plan. The detailed well plan may include descriptions of activities involved in performing actions in the overall well plan, dates and/or times of performing the activities, various resources to perform the activities, and other information.

As an example, the well plan execution system may further include functionality to monitor execution of the well plan to track progress and dynamically adjust the plan. Further, the well plan execution system may be configured to handle logistics and resources on and off the rig. As an example, a well plan execution system may include a plurality of sub-components, such as a detail descriptor configured to describe well plan system plans in detail, a monitor configured to monitor execution of the plans, a plan manager configured to perform dynamic plan management, and a logistics and resource manager to control logistics and resources of the wells. In one or more embodiments, the well plan execution system may be configured to coordinate between different processes managed by the set of process managers (see, e.g., integration block 404). In other words, the well plan execution system may communicate and manage resource sharing among processes in a set of process managers while operating at a higher level of granularity than the set of process managers, for example.

As for the integration block 404, as mentioned, it may be referred to as a set of process managers. In one or more embodiments, the set of process managers may include functionality to perform individual process management for individual domains of an oilfield (e.g., a drilling rig). For example, different activities may be performed while drilling. Each activity may be controlled by a single process manager in the set of process managers. The set of process managers may include multiple process managers whereby each process manager controls a different activity (e.g., rig related activity). In other words, each process manager may have a set of tasks defined for the process manager that are specific to the physical type involved in the activity. For example, drilling may use drilling mud, which is a fluid pumped into a well to extract cuttings from the well. The drilling mud process manager may be present in a process manager suite that manages mixing of drilling mud, composition, testing of drilling mud properties, determining whether pressure is accurate, and performing other such tasks. The drilling mud process manager may be separate from the process manager controlling movement of the drill pipe from the well. Thus, a set of process managers may divide an activity into several different domains and manage each domain individually. Among other possible process managers, the set of process managers may include, for example, a drilling process manager, a mud preparation and management process manager, a casing running process manager, a cementing process manager, a rig equipment process manager, and other process managers. Further, the set of process managers may provide direct control or advice regarding the above components. As an example, coordination between process managers in a set of process managers may be performed by a well plan execution system.

As for the core and service box 406 (e.g., CS box), it may include functionality to manage the various pieces of equipment and/or equipment subsystems. As an example, the CS box may include the functions of processing basic data structures (e.g., drilling rigs) of the oilfield, obtaining metric data, generating reports, and managing human and material resources. As an example, the CS box may include a data acquirer and aggregator, a rig state identifier, a real-time (RT) drilling service (e.g., near real-time), a reporter, a cloud, and an inventory manager.

As an example, the data acquirer and aggregator may include functionality to interface with various device components and sensors and acquire data. As an example, the data acquirer and aggregator may further include functionality to interface with sensors located at the oilfield.

As an example, the rig state identifier may include functionality to obtain data from the data acquirer and aggregator and convert the data into state information. As an example, the status information may include the health and operability of the drilling rig as well as information about the particular tasks performed by the device.

As an example, an RT drilling service may include functionality to transmit and present information to individuals. In particular, the RT drilling service may include functionality to transmit information to the involved individuals depending on the role and, for example, the device type of each individual (e.g., mobile device, desktop, etc.). In one or more embodiments, the information presented by the RT drilling service may be context-specific and may include a dynamic display of the information so that a human user may view details about the item of interest.

As an example, in one or more embodiments, the reporter may include functionality to generate reports. As an example, reports may be generated based on requests and/or automatically, and may provide information regarding the status of devices and/or persons.

As an example, the wellsite "cloud" framework may correspond to an information technology infrastructure local to an oilfield, such as a single rig in an oilfield. In such examples, the wellsite "cloud" framework may be an "internet of things" (IoT) framework. As an example, the wellsite "cloud" framework may be the cloud (e.g., a network of multiple networks) or an edge of a private network.

By way of example, the inventory manager may be a box that includes functionality to manage the material, such as a list and number of each resource on the rig.

In the example of fig. 4, the equipment block 408 may correspond to various controllers, control units, control devices, etc., which may be operably coupled to and/or embedded in physical equipment at the wellsite, such as drilling rig equipment. For example, the equipment box 408 may correspond to the software and control system of various items on the rig. As an example, the equipment block 408 may provide monitoring of sensors from multiple subsystems of the rig and provide control commands to the multiple subsystems of the rig so that sensor data from the multiple subsystems may be used to provide control commands to different subsystems of the rig and/or other equipment, etc. For example, the system may collect surface data and downhole data aligned in time and depth from the rig and transmit the collected data to data acquisitors and aggregators in the core service, which may store the collected data for access at the rig site or off-site through a computing resource environment.

As mentioned, the system 400 of fig. 4 may be associated with a plan, wherein, for example, the plan/reschedule block 422 may provide for planning and/or rescheduling of one or more operations, and the like.

FIG. 5 illustrates an example of a Graphical User Interface (GUI) 500 that includes information associated with a well plan. Specifically, GUI 500 includes a face plate 510 in which surface representations 512 and 514 are presented along with a well trajectory, wherein a position 516 may represent a position of a drill string 517 along the well trajectory. GUI 500 may include one or more editing features, such as a set of editing well plan features 530.GUI 500 may include information regarding individuals who are involved in, have involved in, and/or will be involved in team 540 of one or more operations. GUI 500 may include information regarding one or more activities 550. As shown in the example of fig. 5, GUI 500 may include graphical control of drill string 560, wherein, for example, various portions of drill string 560 may be selected to exhibit one or more relevant parameters (e.g., equipment type, equipment specifications, operational history, etc.). Fig. 5 also shows a table 570 as a dot electronic watch of information specifying a plurality of wells.

Fig. 6 shows an example of a Graphical User Interface (GUI) 600 that includes a calendar with dates of various operations that may be part of a plan. By way of example, GUI 600 illustrates rig up, casing down, cementing, drilling, and rig down operations that may occur at different time periods. Such a GUI may be edited by selecting one or more graphical controls.

The various types of data associated with the field operation may be one-dimensional series of data. For example, consider data of one or more of drilling system, downhole conditions, formation properties, and surface mechanics measured as single-channel or multi-channel time series data.

Fig. 7 shows an example of various components of a lift system 700 that includes a cable 701, a winch 710, a trolley 711, a hook 712, a crown block 713, a top drive 714, a cable dead-line tie-down anchor 720, a cable supply spool 730, one or more sensors 740, and an electrical circuit 750 operably coupled to the one or more sensors 740. In the example of fig. 7, the lift-up system 700 may include various sensors, which may include one or more load sensors, displacement sensors, accelerometers, and the like. As an example, cable tie-down anchor 720 may be equipped with a load cell (e.g., a load sensor).

The lift system 700 may be part of a wellsite system (e.g., see fig. 1 and 2). In such a system, the measurement channel may be a vehicle position measurement channel, referred to as BPOS, that provides a measurement of the height of the ride vehicle, which may be defined near a dead point (e.g., zero point), and may be offset from the dead point in a positive and/or negative direction. For example, consider a recreational vehicle that can move in the range of about-5 meters to +45 meters, with a total travel of about 50 meters. As an example, zero or dead point may be defined as having a scale of either positive, negative or both positive and negative. In such examples, the rig height may be greater than about 50 meters (e.g., the crown block may be disposed at a height greater than about 50 meters from the ground or drilling mast). While various examples of land-based field operations (e.g., stationary, truck-based, etc.) are given, various methods may be applicable to offshore operations (e.g., marine-based rigs, platform rigs, etc.).

BPOS is a real-time channel reflecting the mechanical properties of the rig floor. Another example of a channel is a hook load, which may be referred to as HKLD. HKLD may be a one-dimensional series of measurements of hook load. Regarding the derivatives, the first derivative may be the load speed and the second derivative may be the load acceleration. Such data channels may be used to infer and monitor various operations and/or conditions. In some examples, the rig may be represented as being in one or more states, which may be referred to as rig states.

As for the HKLD channel, it may help detect if the rig is "in slips", while the BPOS channel may be the primary channel for depth tracking during drilling. As an example, BPOS may be used to determine a measured depth in a geological environment (e.g., a borehole being drilled, etc.). As for the condition or state "inside the slips", the value of HKLD is much lower than the condition or state "outside the slips".

The term slips refers to devices or assemblies that may be used to grip a drill string (e.g., drill collar, drill pipe, etc.) and suspend it on a rotary table in a relatively non-damaging manner. The slips may include three or more steel wedges hinged together to form a near-circular shape around the drill pipe. On the drill pipe side (in the ground) the slips are equipped with exchangeable hardened tool steel teeth, which are slightly embedded in the drill pipe side. The outside of the slips is tapered to match the taper of the turntable. After the driller places slips around the drill pipe and in the rotary table, the driller can control the driller to slowly lower the drill string. When the teeth inside the slips grip the drill pipe, the slips are pulled down. This downward force pulls the outer wedge downward, providing an inward compressive force on the drill pipe and effectively locking the assembly together. The driller can then unscrew the upper portion of the drill string (e.g., kelly, saver sub, joint, or drill string) while hanging the lower portion. After some other components are screwed into the lower portion of the drill string, the driller lifts the drill string to unlock the gripping action of the slips, and the driller can remove the slips from the rotary table.

The hook load sensor may be used to measure the load weight on the drill string and may be used to detect whether the drill string is inside or outside a slip. When the drill string is in slips, movement from the cart or motion compensator has no effect on the bit depth at the end of the drill string (e.g., it will tend to remain stationary). In the case where the movement of the rover is through a winch encoder (DWE) that may be mounted on the winch axle, the acquired DWE information (e.g., BPOS) does not increase the recorded bit depth. The DWE information (e.g., BPOS) may increase the recorded bit depth when the drill string is outside the slips (e.g., drilling ahead). The difference in hook load weight (HKLD) inside and outside the slips is often distinguishable. For offshore operations, heave of the vessel can affect the bit depth, whether the drill string is inside or outside the slips. As an example, a vessel may include one or more heave sensors that may sense data that may be recorded as 1-D series data.

For offshore operations, the vessel may experience various types of motions, such as one or more of heave, sway, and surge. Heave is a linear vertical (up/down) motion, sway is a linear lateral (side-to-side or port-starboard) motion, surge is a linear longitudinal (fore/aft or bow/stern) motion caused by ocean conditions. As an example, the vessel may include one or more heave sensors, one or more sway sensors, and/or one or more surge sensors, each of which may sense data that may be recorded as 1-D series data.

As an example, the BPOS may be used alone or in combination with one or more other channels to detect whether the rig is "drilling at the bottom" or "tripping" or the like. The inferred state may be further consumed by one or more systems, such as an automatic drilling control system, which may be a dynamic field operating system or a portion thereof. In such examples, conditions, operations, states, etc., as discerned from the BPOS and/or other channel data, may be predicates that make one or more drilling decisions, which may include one or more control decisions (e.g., decisions of a controller operably coupled to one or more field devices, etc.).

A car (block) may be a set of pulleys that are used to gain mechanical advantage when lifting or dragging a weight. The drilling machine can be provided with two vehicles, a crown block and a traveling block. Each may comprise several sheaves equipped with steel drilling lines or ropes so that the trolley may be raised (or lowered) by winding (or paying out) a roll of drilling line on the winch. Thus, the vehicle position may refer to the position of the tourist vehicle, which may vary over time. Fig. 1 shows a rover assembly 175, fig. 2 shows a rover 211, and fig. 7 shows a rover 711.

The hooks may be high capacity J-shaped devices used to suspend various equipment such as swivel and kelly, elevator bails, or top drives. Fig. 7 shows that the hook 712 is operably coupled to the top drive 714. As shown in fig. 2, the hook may be attached to the bottom of the carriage 211 (e.g., a portion of the carriage assembly 175 of fig. 1). The hook may provide a means of lifting a weight with the trolley. The hook may be locked (e.g., normal) or free to rotate so that it may engage or disengage an item or the like located about the drilling string.

The hook load may be the total force carried by the ride vehicle pulling the hook downward. The total force includes the weight of the drill string, drill collar and auxiliary equipment in the air, minus the force tending to reduce that weight. Some of the forces that may reduce weight include friction along the borehole wall (especially in deviated wells) and buoyancy forces generated on the drill string due to the drill string being immersed in drilling fluid (e.g., and/or other fluids). If a blowout preventer (BOP) (e.g., or BOPs) is closed, pressure in the borehole acting on the cross section of the drill string in the BOP may also exert an upward force.

The riser may be a rigid metal conduit that provides a high pressure path for drilling fluid to travel up the derrick a distance of about one third of the way and where it is connected to a flexible high pressure hose (e.g., a kelly hose). Large drilling rigs may be equipped with more than one riser, so that if one riser requires maintenance, downtime will be kept to a minimum. Fig. 2 illustrates riser 208 as a conduit for drilling fluid (e.g., drilling mud, etc.). The fluid pressure within the riser 208 may be referred to as riser pressure.

As for the surface torque, such measurements may be provided by equipment on the rig site. As an example, surface torque may be measured using one or more sensors, which may provide direct and/or indirect measurement of surface torque associated with the drill string. As an example, the apparatus may include a drill pipe torque measurement and controller system having one or more of an analog frequency output and a digital output. As an example, a torque sensor may be associated with a coupling that includes a resilient element that operably couples an input element and an output element, wherein the resilient element allows the input element and the output element to twist relative to each other in response to torque transmitted through the torque sensor, wherein the twist may be measured and used to determine the transmitted torque. As an example, such a coupling may be located between the driver and the drill rod. As an example, torque may be determined by one or more inertial sensors. As an example, the equipment of the rig site may include one or more sensors for measuring and/or determining torque (e.g., in Nm, etc.).

By way of example, the apparatus may include a real-time drilling service system that may provide data, such as weight transfer information, torque transfer information, equivalent Circulating Density (ECD) information, downhole Mechanical Specific Energy (DMSE) information, motion information (e.g., stall, stick-slip, etc.), bending information, vibration amplitude information (e.g., axial, lateral, and/or torsional), rate of penetration (ROP) information, pressure information, differential pressure information, flow information, etc. As examples, the sensor information may include inclination, azimuth, total vertical depth, and the like. As an example, the system may provide information about whirl (e.g., backward whirl, etc.) and may optionally provide information such as one or more alarms (e.g., "severe backward whirl: stop and restart at lower ground RPM," etc.).

As an example, the drill string may include one or more tools including various sensors that may make various measurements. Consider, for example, the optiill tool (Schlumberger, houston, tx) which includes strain gauges, accelerometers, magnetometers, gyroscopes, and the like. For example, such a tool may use a strain gauge (e.g., a 10 second moving window with a bandwidth of 200 Hz) to obtain weight on bit measurements (WOB), a strain gauge (e.g., a 10 second moving window with a bandwidth of 200 Hz) to obtain torque measurements, a strain gauge (e.g., a 10 second moving window with a bandwidth of 200 Hz) to obtain bending moment measurements, one or more accelerometers (e.g., 30 seconds RMS with a bandwidth of 0.2 to 150 Hz) to obtain vibrations, a magnetometer and gyroscope (e.g., a 30 second moving window with a bandwidth of 4 Hz) to obtain rotational speeds, one or more strain gauges (e.g., a 1 second average with a bandwidth of 200 Hz) to obtain annulus and internal pressure, one or more temperature sensors (a 1 second average with a bandwidth of 10 Hz) to obtain annulus and internal temperature, and an accelerometer (30 second average with a bandwidth of 10 Hz) to obtain continuous tilt.

As mentioned, the channel of real-time drilling operation data may be received and characterized using generated synthetic data, which may be generated based at least in part on one or more operating parameters associated with the real-time drilling operation. Such real-time drilling operation data may include surface data and/or downhole data. As mentioned, the data availability may vary over time (e.g., frequency, gap, etc.) and/or other aspects (e.g., resolution, etc.). These data may differ in noise level and/or noise characteristics. While various types of sensors are mentioned, devices that do not include one or more types of downhole sensors may be used. In this case, a method that can determine one or more downhole values may be utilized.

Fig. 8 illustrates an example of a method 800 that includes various blocks that may receive data, perform one or more analyses, perform one or more decisions, etc., to determine one or more states. In the example of fig. 8, various examples of the states are shown with respect to colors. In fig. 8, example conditions include drilled, non-drilled, sub-hole (RIH), out-hole (POOH), pre-connection, post-connection, and absence.

Drilling is drilling that increases the depth of the wellbore. When no other activity is occurring (e.g., drilling, RIH, POOH, pre-connection, post-connection) and the end of the current drilling string has not been reached, it may be determined that no drilling activity is occurring. During non-drilling, the flow rate of fluid pumped into the drill string may be increased and/or decreased, the rotational rate of the drill string may be increased and/or decreased, the downhole tool (e.g., drill bit) may be moved up and/or down, or a combination thereof. The non-drilling activity may be or include a time when the drill bit is idle (e.g., not drilling) and the slip assembly is not engaged with the drill string.

The pre-connection may be where the downhole tool (e.g., drill bit) has completed the drilling operation of the current pipe section, but the slip assembly has not yet begun to move (e.g., radially inward) into engagement with the drill string. During the pre-connection, the flow rate of fluid pumped into the drill string may be increased and/or decreased, the rotational speed of the drill string may be increased and/or decreased, the downhole tool (e.g., drill bit) may be moved up and/or down, or a combination thereof.

The connection may be where the slip assembly engages and supports the drill string (e.g., where the drill string is "within a slip"). When connection occurs, a pipe segment (e.g., pipe, column, etc.) may be added to the drill string to increase the length of the drill string, or a pipe segment may be removed from the drill string to decrease the length of the drill string.

The back connection may be where the drill string is released by the slip assembly and the downhole tool (e.g., drill bit) is lowered to the bottom (e.g., downhole or BOH). During the post-connection, the flow rate of fluid pumped into the drill string may be increased/and/or decreased, the rotational rate of the drill string may be increased/and decreased, the downhole tool (e.g., drill bit) may be moved up and/or down, or a combination thereof.

As for the absence status, it may indicate a scenario of a situation where no data is received (e.g., at least one of the plurality of inputs is lost).

As an example, a method may be utilized to determine slip status. For example, the slip conditions may include one or more of within a slip, wherein a slip assembly engages and supports a drill string ("within the slip"); outside the slips, wherein the slip assembly is not engaged with the drill string and does not support the drill string; and absent, wherein no data is received (e.g., at least one input is lost).

The method 800 of fig. 8 may include various data acquisition or data reception blocks 802, 806, 808, etc. Various decision blocks 805, 807, 809, 813, 815, 817, and 843, detection blocks 812 and 842, and status blocks. The measurements may include (1) wellbore depth, (2) drill bit depth, (3) position of the rover, or a combination thereof. A set of measurements may or may not include weight on hook (e.g., HKLD) or weight on bit (e.g., WOB). Each set of measurements may be captured/received a predetermined amount of time after the previous set of measurements was captured/received. The predetermined amount of time may be, for example, about three seconds; however, the predetermined amount of time may be shorter or longer.

PCT publication WO 2017/221046A1 at 12, 28, 2017, which is incorporated herein by reference, is entitled "Automatic drilling activity detection" (' 046 publication). The' 046 publication describes a method of determining drilling activity that includes receiving a set of measurements at different times. The set of measurements may include wellbore depth, drill bit depth, and rover position. The method may further include identifying the connection by determining when the position of the rover is changed but the depth of the drill bit is unchanged. The method may further include determining when the wellbore depth between two different connections does not increase. The method may further comprise determining a direction in which the drill bit is moved between the two connections.

Fig. 9 illustrates an example of a graph 900, the graph 900 illustrating time intervals including drilling, pre-connection, post-connection, and non-drilling activities, according to an embodiment. Time is shown on the X-axis for a total of about 3 hours. The top quarter 910 of graph 900 shows wellbore depth versus time. The next quarter 920 of the graph 900 shows the position of the rover versus time. The next quarter 930 of the graph 900 shows the time interval during which the downhole tool (e.g., drill bit) is drilling, the time interval during which the pre-connection occurs, the time interval during which the post-connection occurs, and the time interval during which the non-drilling activity occurs. The bottom quarter 940 of the graph 900 shows the time interval during which the drill string is engaged with and supported by the slip assembly (within the slips) and the time interval during which the drill string is not engaged with and supported by the slip assembly (outside the slips). As can be seen, the trolley moves up during the connection and down during the drilling process. Furthermore, when the connection occurs, the drill string is inside the slips, and when the connection does not occur, the drill string is outside the slips.

Fig. 10 shows an example of a Graphical User Interface (GUI) 1000 that includes sets of data regarding time. In the example of fig. 10, GUI 1000 includes a drilling status trajectory utilizing a specific color scheme, where green corresponds to drilling (deepening the wellbore), red corresponds to a pre-connection status, black corresponds to a post-connection status, and gray corresponds to a connection status. As for time series data, BPOS, HKLD, and STOR are shown with respect to time. Specifically, BPOS is shown with respect to distance (e.g., 10 meters to 40 meters, etc.), HKLD is shown with respect to kN (e.g., 500kN to 1500 kN), and STOR is shown as torque loss in kn.m (e.g., 0kn.m to 50 kn.m). In the example of fig. 10, various values are labeled AC and various values are labeled RC. The value labeled RC is an improved value compared to the value labeled AC. As an example, a method may include machine learning based on surface sensors, detecting lifting (PU)/lowering (SO) weight and weight-on-bit (DWOB) and torque (TQLS, torque-on-bit (DTOR), etc.). Such methods may output values for various operational improvements, particularly in the case where the device may not have one or more types of downhole sensors. For example, consider the case where the operation is performed without a downhole torque sensor. In such examples, a method may implement a trained machine model to determine one or more downhole torque values.

As an example, a method may include an interface for receiving a call_state, a drilling status [ no units ]; BPOS, vehicle position [ m ]; RPM, revolutions per minute [ c/min ]; HKLD, hook load [ kN ]; and STOR, ground torque [ kN.m ]. Such a method may utilize such inputs to output the following outputs, HKLD_SO, hook load-drop [ kN ], the vehicle is descending; hkld_pu, hook load-lift [ kN ], vehicle ascending; hkld_fr, hook load-free rotation [ kN ]; DWOB, weight on bit downhole [ kN ]; TQLS, torque-loss [ kn.m ]; DTOR, torque-downhole [ kn.m ], dtor=stor-TQLS.

Referring again to GUI 1000 of FIG. 10, various inputs and outputs are shown. For example, inputs include DRILL_ STATE, BPOS, HKLD and STOR, and outputs include HKLD_SO, HKLD_PU, HKLD_FR and TQLS, which may be encoded (e.g., color, shading, hatching, etc.).

Information relating to the connection between the drilling columns may be utilized during the drilling process. Historically, drilling parameters at joints have been obtained on-site on a drilling rig, and there has been an inconsistency in these parameters due to variations in the personnel. To reduce the effects of artifacts and select measurement points in a more systematic way, various algorithms have been developed; however, such algorithms have limitations due to inconsistencies in driller practice and/or due to different processes applied by one drilling company to another.

As an example, the system may include one or more processors, memory, and instructions that may instruct the system to operate in a robust manner to retrieve off-bottom measurements, such as load, torque, and pressure. As an example, consider an algorithm for a mud logging system or an algorithm for a executive kit (PTK) with automatic calibration (Schlumberger, houston, texas). Such algorithms are operable to output values that can be used to determine hook loads at the connection for lifting (PU) and/or lowering (SO), as well as downhole drilling parameters for WOB, bit Torque (TAB), and bit Pressure (PAB). The calculated downhole drilling parameters may be used when no downhole measurements are made or when downhole measurements are not available. Such calculated values may be used, for example, in land rig operations, where the PU and SO values may be a first indication of a stuck pipe during drilling and/or tripping operations.

In the case of real-time monitoring and well data analysis, the calculation of these values can be used to display broom models as opposed to actual measurements, as well as for bottom hole efficiency analysis.

Well analysis software implemented as a computing framework may be faced with low quality real-time surface data in a vendor neutral environment. The data may be relatively low frequency data (e.g., taking into account a sampling rate of 0.1 Hz), and inconsistent drilling practices at the time of connection may render certain types of calculations unusable, which may affect the confidence of such software itself.

As described with respect to the example GUI 1000, a method may provide for determination of various phenomena associated with drilling operations. For example, torque loss and lift (PU)/drop (SO)/free spin (FR) weight on data (e.g., vendor free data, etc.) can be determined, even in poor quality cases. Such a method may be operated in an automated manner. Such methods may be used to estimate one or more operating coefficients of friction. As an example, a method may include determining one or more values that are closely related to a jam. As an example, a method may include determining a value indicative of risk (e.g., probability of a pipe being stuck). As an example, a method may be implemented as part of a control system that may operate to reduce the risk of pipe sticking and/or reduce the incidence of pipe sticking. As an example, a method may provide for detection of stuck pipes. As an example, a method may be implemented as part of a stuck pipe detection workflow. In such examples, the workflow may reduce the occurrence of stuck pipes and/or detect stuck pipes.

As an example, a method may provide for detecting one or more of torque loss and/or lift (PU), drop (SO), and free-spin (FR) weights in a time data sequence. For example, such methods may utilize a trained machine model, and may include training the machine model. As an example, machine learning techniques may replace manual input of one or more interpretation parameters. As an example, a method may select multiple channels, where the selected channels allow for reduction of user errors (e.g., error minimization, etc.) and/or data quality issues. As an example, for each individual output, a method may involve filtering data points with one or more criteria, where such criteria may include one or more criteria based on the physics of the process. In such a method, the final point of each individual column may be statistically taken as, for example, the median of the points when applied to the column of a drilling operation. Such a method may function to reduce the effects of noise in the data from one or more surface sensors.

By way of example, the stand-offs may be two or three single joint drill pipes or collars that remain threaded together during tripping operations. Various medium depth capacity drills may handle three joint columns, known as "triple" or "trigeminy". Some smaller drills have the ability to have double joint studs, known as "double knots". As an example, the operation may involve erecting a drill pipe or drill collar in a derrick and placing it into a fingerboard to keep it in order. Such methods tend to be a relatively efficient way to remove the drill string from the well when changing the drill bit or making adjustments to the Bottom Hole Assembly (BHA). As an example, one method may involve unscrewing a threaded connection. As an example, in some cases, a "stand" may be a single uncoupled section of the drill string. Although upright placement is mentioned, in some cases other orientations may be utilized. As an example, in operations involving unscrewing of a threaded connection, a pipe segment may be placed in a horizontal position.

Although threads are mentioned, various types of devices may be connected by unthreaded nipple or fittings. The connection may be a threaded nipple or joint or a non-threaded nipple or joint connecting two tubular assemblies. The connection may be an operation to add a section, for example, a nipple or a stand of drill pipe to the top of the drill string (e.g., "make connection"). The segments may be removed (e.g., disconnected, etc.) using the reverse operation.

As for the ground sensor measurements, the movements may not be very consistent for about the connect/disconnect operation during operation. For example, when tripping, the motion may slow down (e.g., slow down) and then speed up (e.g., accelerate). The motion may be more consistent between acceleration and deceleration. The ground sensor data may have a higher signal-to-noise ratio (SNR) in the case of more consistent motion than in the case of less consistent motion (e.g., deceleration and/or acceleration). As an example, a method may include processing sensor data to effectively select data points (e.g., samples) within a period of time (e.g., or multiple periods of time) where motion is more consistent. While such an approach may reduce the number of data points used, the data points used may have less noise (e.g., higher SNR, etc.). As an example, a method may involve detecting a connection time or connection times and selecting a time series window of data that is a time delta from the connection time or connection times. For example, consider time series data spanning a time period t-total from connection 1 to connection 2, where the window selected is less than t-total and does not include data points in time period t-1 after connection 1 nor data points in time period t-2 before connection 2. Such a method may select the window based on a percentage, a number of data points (e.g., a given sampling rate), using a speed-based criteria (e.g., average speed, etc.), using a total time-based criteria, using an acceleration criteria, using a deceleration criteria, etc.

As an example, consider a method that detects weight and torque using statistical methods based on previous column experience.

As an example, a method may implement one or more techniques to detect torque loss and lift/drop/free-wheeling weight in a time series of data. For example, consider implementing one or more machine learning techniques that may replace and/or enhance manual input and/or interpretation of parameters. As an example, one approach may aim to utilize a limited number of channels, which may allow for reduced user errors and/or data quality issues. As an example, for each output, the data points may be filtered, e.g., using one or more criteria, which may be based on the physical characteristics of the process, wherein, for example, the ending point of each pillar may be taken as the median of the points. Such an approach may ignore possible noise in one or more surface sensors.

As explained with respect to the system 1600 of fig. 16, machine Learning (ML) may be performed using one or more ML models. As an example, a method may include machine learning during a drilling phase for training an ML model to generate a trained ML model. As an example, a method may include performing machine learning on data in one or more databases, where such data may include offset well data.

As an example, a method may include identifying a threshold value for determination of a drill string off-bottom condition; filtering the time series surface data of the post-connection drilling state of the drill string using a threshold to generate filtered time series surface data; and statistically determining a drill string off-bottom condition value using the filtered time-series surface data. In such examples, filtering may be performed using one or more filter models that may include one or more parameters. In such examples, the one or more parameter values may be determined using data acquired during a drilling operation of the one or more wells. The machine model may utilize a filter model, wherein, for example, the filter model may be dynamically adjusted using one or more thresholds that may be determined during a drilling operation. As an example, consider a filter model that includes one or more parameter values that may be learned using offset well data, and include one or more dynamic thresholds that may be identified during drilling, and the like. Such a filter model may be applied to the acquired data (e.g., post-connection) to determine, for example, a drill string off-bottom condition value.

As an example, the inputs may be limited, e.g., limited, to a plurality of inputs selected from a group, e.g., including drilling status, vehicle position, rotational speed, hook load, surface torque, riser pressure, and flow (e.g., mud flow, etc.), as explained, the ML method may provide identification of one or more suitable filters for hook load, surface torque, and/or pressure, e.g., by looking at previous connections and previous wellbore intervals (intervals).

As an example, the method may operate in an automated fashion, which may be vendor-free, vendor-neutral, etc. Regarding data features, as an example, consider a data set with a sampling rate of less than about 10 seconds (e.g., samples acquired at intervals of 10 seconds or less).

As an example, a method may be implemented using autonomous computing at a server side and/or a client side. Such methods may be part of one or more workflows (e.g., torque and drag, tripping load, stuck pipe, etc.).

As an example, the computing engine may be adapted for use in or associated with one or more frames (e.g., a data frame such as a techolog frame, a well interpretation frame, etc.). As an example, the method and/or compute engine may be used in a dataku type data framework.

As explained, the method and/or calculation engine may provide determinations regarding one or more of the up/down weight and the weight on bit, torque and pressure based on machine learning of surface sensor data.

FIG. 11 illustrates an example of a GUI 1100 that includes a timeline with various states associated with operations in which there may be associated time-series data. In the example of fig. 11, statistical methods may be used for one or more of weight, torque, and pressure detection based on previous column experience. As shown, the method may include classifying the stud types into three categories RIH, drilling, POOH. In such examples, during various intervals of one or more columns, the method may include calculating various statistics that may be related to one or more conditions. As examples, consider torque, hook load, pressure, flow, etc.

In the example of fig. 11, various examples of intermediate values are shown, which may include, for example, one or more of an intermediate hook load during drilling (drhkldded), an intermediate surface torque during drilling (DrStorMed), an intermediate riser pressure during drilling (e.g., absolute pressure) (DrSppaMed), and an intermediate flow during drilling (DrFlwiMed). As an example, one or more such values may be identified and used as one or more thresholds.

As an example, during connection of a drilling riser, a method may include calculating a threshold value, where the threshold value may be used as a filter, e.g., in a filter model, to calculate one or more other values (e.g., weight, e.g., lifting, dropping, free rotation, torque, pressure, flow, etc., in such examples, the filter or filter model may include one or more other types of parameters, which may be determined, e.g., by learning from data in one or more databases.

As shown in fig. 11, the time series data may include surface torque time series data (STOR), hook load time series data (HKLD), riser time series data (SPPA), and flow time series data (FLWI) (e.g., mud flow, etc.).

As an example, during connection (e.g., connecting a wellbore section), one method may include calculating a median hook load value (conhkldded), which may be used as a filter to calculate the lifting weight and/or the lowering weight.

As an example, after one or more of DrStorMed, drHkldMed, conHkldMed, drSppaMed and DrFlwiMed thresholds have been identified, the method may continue with one or more detection processes, which may include filtering using one or more filter models, which may be machine models that may include one or more parameter values, which may be learned, for example, using offset well data, or the like. For example, the threshold may be considered a dynamic parameter, while one or more other parameters may be determined through learning using offset well data, or the like.

Fig. 12 shows an example of a GUI 1200 that includes a timeline with various states associated with operations in which there may be associated time-series data. GUI 1200 shows some examples of processes that may be detection processes, e.g., may operate using one or more thresholds. As shown in the example of fig. 12, GUI 1200 may include various portions of three columns, including RIH, driling, and POOH. GUI 1200 shows a method of detecting TLQS, HKLD_FR, HKLD_SO, HKLD_PU and OFBP (off-bottom pressure).

As an example, a method may include determining a weight value hkld_fr in a post-connection (e.g., post-connection) as follows:

A. collecting data points "during post-connection" (e.g., post-connection);

B. discard negative numbers and lost HKLD points;

C. discard points at RPM <20 c/min;

D. discarding the point when the bit is at the bottom (rig_state=0 or 1);

E. discarding points where vehicle speed >0.1 m/s;

F. discard the point of HKLD < drhkldded; and

G. the final hkld_fr result value is determined as the low median of the remaining points, where it can be assumed that the median is safe to look at the exact point chosen, since HKLD tends not to have much noise during this time.

As an example, one method may include determining a torque value TQLS after connection (e.g., a post connection) as follows:

A. collecting valid STOR data points during "post-connection";

B. discarding negative and missing STOR points;

C. discarding the point when the bit is at the bottom (rig_state=0 or 1);

D. discarding the points with rotation speed <20c/min or rotation speed <0.9 x max (RPM);

E. discarding the point where STOR > DrStorMed; and

F. the final TQLS result value is determined as the average of the remaining points at which it can be assumed that averaging is safer than median because of the large amount of STOR noise that often occurs during the post-connection process.

As an example, a method may include performing the following various calculations in the pre-connection:

A. calculating hkld_pu and hkld_so during the drilling phase, collecting points from the pre-connected intervals into two sets-one of lifting and one of lowering (e.g., different directions of drill string movement in the borehole);

B. for both, HKLD < conhkldded 1.1 filter was applied first;

C. for both, discarding the rotation point based on the rig_state input;

D. for the lifting, the point of BPOS increase is collected, filtering is carried out according to min (BPOS) +1< BPOS < max (BPOS) -1m, and finally HKLD_PU is taken as the high intermediate value of the collection; and

E. for the dropping, taking the point of BPOS reduction by the set, filtering according to min (BPOS) +1< BPOS < max (BPOS) -1, and finally taking HKLD_SO as the low median of the set; note that such an approach may be more efficient than calculating and comparing vehicle speeds from a vehicle position.

As an example, one method may include performing various calculations on RIH and/or POOH as follows:

A. in the RIH and POOH phases, pre-and post-connections are not defined because drilling does not occur;

B. during analysis, calculating min (BPOS) and max (BPOS), and taking 1/3 of the well section points between the min (BPOS) and the max (BPOS);

hkld_so during rih is taken as the low median point; and

Hkld_pu in pooh process takes a high intermediate value of point.

As an example, a method may include performing various calculations regarding off-bottom pressure (OFBP) and/or Differential Pressure (DPRES), which may involve, for example, operation of a downhole motor (e.g., mud motor, etc.) driven at least in part by a fluid flow to rotate a drill bit. For example, consider a method that can provide for determining the unseating pressure (OFBP) and/or Differential Pressure (DPRES) by:

A. learning SPPA (riser pressure) points during a previous drilling string (e.g., pre-connection), calculating DrSppaMed = median (SPPA);

B. learning FLWI (mud flow) points during a previous drilling stand (e.g., pre-connection), calculating DrFlwiMed = median value (FLWI);

C. during the next post-connection (e.g., after connection), SPPA/FLWI samples are taken;

D. removing points of SPPA > DrSppaMed;

E. removing the bottom point by letting rig state = unset;

F. remove FLWI <0.85 drflwimed points;

G. calculating the mean value (SPPA) of the reference OFBP = remaining points; and

H. for the next drilling point, dpres=sppa-OFBP is calculated.

In various examples, one or more learning techniques may be used to determine one or more parameters, which may be machine model-based learning techniques. As an example, data from offset wells may be analyzed. In such examples, various parameter values may be tested to determine appropriate parameter values for one or more methods. For example, consider the various values given above in relation to RPM, vehicle speed, flow, etc., which may be part of one or more filtering processes. One or more of such values may be determined using offset well data, where, for example, the value may be determined using a machine model that may be trained using the offset well data to obtain the value. Such methods may be directed to improving the accuracy and/or applicability (e.g., robustness, etc.) of one or more techniques described with respect to GUI 1200 of fig. 12. For example, a set of parameter values may be determined for a particular type of formation, a particular type of bottom hole assembly, a particular type of drilling fluid, and so forth. As an example, one or more parameter values may be updated, which may be done through a background process that may operate on one or more offset well data, target well data, and the like.

A. during the previous drilling leg (e.g., pre-connection), the SPPA (riser pressure) points are learned, and DrSppaMed = median (SPPA) is calculated;

B. during the previous drilling leg (e.g., pre-connection), learning FLWI (incoming mud flow) points, calculating DrFlwiMed = median value (FLWI);

D. removing points of SPPA > DrSppaMed;

E. removing the bottom point by letting rig state = unset;

F. remove FLWI <0.85 drflwimed points;

H. for the next drilling point, dpres=sppa-OFBP is calculated.

Referring again to GUI 1200 of fig. 12, some examples of parameter values may include "20c/min" (e.g., RPM <20 c/min), "0.1m/s" (e.g., vehicle speed >0.1 m/s), "20c/min" or "0.9" (e.g., RPM <20c/min or RPM <0.9 x max (RPM)), "1.1" (e.g., HKLD < conhkldded x 1.1), "0.85" (e.g., these values may be represented as parameters (e.g., parameter 1, parameter 2, parameter 3, parameter 4, etc.) using names such as "parameter" as explained, the threshold may be another type of parameter that may be dynamic, may be represented using names such as "thres" (e.g., thres1, thres2, thres3, etc.).

FIG. 13 illustrates an example of a GUI1300 having a timeline of various states associated with operations in which there may be associated time-series data. In the example of fig. 13, GUI1300 shows a portion of a method for column #2 and drilling, where min (BPOS) and max (BPOS) are shown for detecting a lift (PU) and drop (SO) point using BPOS. As mentioned, one method may include utilizing a technique that is statistically more efficient than calculating and comparing vehicle speeds from a vehicle position.

As explained, a method may be utilized to calculate one or more downhole values, where, for example, one or more corresponding sensors may not be available to measure such downhole values. In such examples, surface data, such as time-series surface data, acquired by one or more surface-based sensors (e.g., wellsite sensors, etc.), may be utilized.

FIG. 14 illustrates an example of a method 1400 that includes a receiving block 1410 for receiving time series data including downhole sensor data, wherein the time series data may be from a plurality of wells (e.g., consider ten or more wells); a performance block 1420 to perform learning to generate a trained machine model; a receiving block 1430 for receiving time series data of single well operations, which may or may not include one or more downhole sensors; an application block 1440 for applying the trained machine model to at least a portion of the received data of the receive block 1430 to calculate one or more values; and an optional issue block 1450 for issuing at least one control instruction for the at least one operation using at least one of the one or more values. Fig. 14 also illustrates an example of a system 1490 that can be used to implement one or more portions of the method 1400.

As shown, the method 1400 may include various components, such as a training component, an execution component, and a control component. As for training, consider accessing time series data for tens of wells (e.g., 50 wells, 100 wells, etc.), where the time series data may include data from downhole sensors. For example, various data sets of a well drilled using a drill string having one or more downhole sensors may be accessed for training purposes. In such examples, the training may train the machine model to reproduce the downhole sensor-based values using the input values (e.g., by matching the input-based output to the actual downhole sensor-based values). Such training may be referred to as machine learning, which may generate a trained machine model. As an example, such machine learning may provide an output of one or more parameter values, which may be applied to one or more filter models, which may be considered machine models.

As explained, in a method where downhole values that are not based on downhole sensor measurements may be calculated, a trained machine model may be utilized. As an example, the trained machine model may include adaptive features. For example, the trained machine model may be adjusted using time series data, which may include real-time data. As mentioned, the machine model may be utilized to determine one or more parameter values, which may be, for example, part of a filtering model that performs one or more filtering tasks with respect to the time series data, wherein the filtering model may include one or more thresholds. As an example, one or more of the methods described with respect to GUI 1200 of fig. 12 may be implemented using an overall machine model or multiple machine models that provide threshold identification, data filtering, and the like. Such one or more models may be operably coupled to one or more databases and/or real-time data sources.

As an example, the trained machine model may operate as one or more filters that may be applied to the time series data, e.g., on a drilling string-by-string basis. As an example, a method may include a decision tree structure that involves applying one or more filters to determine points that may be used as representative of particular aspects of one or more operations with respect to a drilling string.

As an example, the filter may be a "smart" filter that is obtained by training. For example, the trained machine model may be a filter model that is adaptable using inputs. By way of example, a method may be implemented in a suitable programming language, such as the PYTHON language, as instructions stored in a storage device operatively coupled to a processor, wherein such instructions are executable by the processor.

As an example, with respect to implementation, during operation, time series data may be acquired for a section of drill string (e.g., a column, etc.), wherein a particular portion of the time series data (e.g., a selected sample) may be used as input to determine (e.g., identify) one or more thresholds for a subsequent section of the drill string, e.g., to calculate a pull-up (PU) point and a pull-down (SO) point.

As mentioned, the inputs may include (i) drilling status (e.g., according to a method such as method 800 of fig. 8), (ii) BPOS, (iii) RPM, (iv) HKLD, and (v) STOR, and the outputs may include (i) hkld_so (the vehicle is descending), (ii) hkld_pu (the vehicle is ascending), (iii) hkld_fr (free rotation), (iv) DWOB (downhole value), (v) TQLS, and (vi) DTOR, which is downhole (e.g., dtor=stor-TQLS). In this example, the number of inputs may be selected in a limited manner, which helps limit the amount and/or type of noise that may be present and/or otherwise affect the output. As mentioned, the torque value may be used for one or more friction calculations. The friction may be wellbore friction that occurs during rotation of the drill string in the wellbore. As an example, the coefficient of friction may be calculated with respect to the drill string and the wellbore. As explained, the inputs may include SPPA and/or FLWI, which may be an alternative and/or supplement to one or more other inputs.

As for BPOS, it may be in a range that can be specified in meters (e.g., 0 meters to 40 meters) or feet. The sampling rate of the BPOS may vary from field device to field device. As an example, the sampling rate for BPOS versus time may be 1 second, 3 seconds, 5 seconds, 10 seconds, etc. As an example, a robust system may be configured to handle a variety of different sampling rates, which may be specific to the type of equipment, the entity performing the drilling, and so forth. Such time series data may include noise. As an example, to handle noise, one approach may utilize raw time series data of the BPOS and select data points (e.g., samples) therein for computation. Such methods may involve filtering to select such data points. Although BPOS is mentioned, such a method may be applied to HKLD and STOR, which may include noise, outliers, etc., which are not visible in BPOS. For example, HKLD and/or STOR may include spikes (e.g., transient with relatively extreme values). As an example, a method may be used in a scenario that includes one or more downhole sensors. For example, from the transmission of downhole sensor data to the surface location, an estimate may be available before receiving the actual downhole sensor values. As an example, in some scenarios, downhole sensor data may be stored in the device such that the data may be accessed after the device is initiated. In such an example, a comparison may be made between the actual data and the estimated value.

As explained, the machine model may be a filter (or filters) capable of operating on an input, which may be time series data associated with a section of drill string (e.g., a column, etc.). Such a method may be used to determine (e.g., identify) one or more thresholds that may be used for subsequent columns.

As an example, a method may automatically detect the weight and/or one or more pressures of torque loss, lifting (PU), lowering (SO), and free-spinning (FR) operations during drilling. Such methods may operate on inputs that may be limited to drilling status, vehicle position, rotational speed, hook load, and surface torque, and/or may optionally include riser pressure and/or flow.

As explained, a method may include implementing machine learning to identify appropriate filters for hook load and surface torque by looking at previous connections and previous wellbore sections (e.g., phases). Such an approach may reduce manual user intervention. For example, such a method may automatically extract a threshold from the time series data.

As an example, a method may operate in a manner that improves upon methods that utilize hook load thresholds that determine whether a drill string is within a slip. For example, one approach may operate in a manner that is more robust to noise in time series data (e.g., noise in HKLD).

As an example, one method may utilize a trained machine model, may utilize a limited number of inputs, and may utilize statistical and/or probabilistic methods to process data points (e.g., samples). Such a method is robust to noise and is applicable to various types of equipment that can provide a basic type of surface sensor.

As shown in method 1400 of fig. 14, a training phase may occur to generate a trained machine model. For example, consider training using time series data for 50 wells or more, with data from real downhole sensors. As indicated, the implementation phase may utilize a trained machine model. As an example, a method may include looking at a previous drilling string and sampling a threshold for a next drilling string. As an example, a method may be implemented locally and/or remotely. As an example, a computing framework (e.g., a TECHLOG framework) may include features for implementing one or more portions of a method (e.g., method 1400 of fig. 14). As an example, the method may be part of a workflow (or workflows) that may be a torque and drag workflow, a tripping load workflow, a stuck pipe workflow, a mud motor workflow, and so forth.

Fig. 14 also illustrates various computer-readable media (CRM) blocks 1411, 1421, 1431, 1441, and 1451. These blocks may include instructions executable by one or more processors, which may be one or more processors of a computing framework, system, computer, or the like. The computer readable medium may be a non-signal, a non-carrier wave, and a non-transitory computer readable storage medium. By way of example, a computer readable medium may be a physical memory component capable of storing information in a digital format.

In the example of fig. 14, the system 1490 includes one or more information storage devices 1491, one or more computers 1492, one or more networks 1495, and instructions 1496. With respect to the one or more computers 1492, each computer can include one or more processors (e.g., or processing cores) 1493 and a memory 1494 for storing instructions 1496 that can be executed by at least one of the one or more processors, for example. By way of example, the computer may include one or more network interfaces (e.g., wired or wireless), one or more graphics cards, a display interface (e.g., wired or wireless), and the like. The system 1490 may be particularly configured to perform one or more portions of the method 1400 of fig. 14.

FIG. 15 illustrates an example of a method 1500 that includes a partition block 1510 for partitioning time series data into RIH, driling, and POOH partitions; a calculation block 1520 for calculating a threshold for torque loss determination using a model in which data for the wellbore interval is utilized; a calculation block 1530 for calculating filter values for weight determination using a model that utilizes data of the connected intervals; a determination block 1540 for determining a torque loss value using the threshold and the data for the post-connection state; a determination block 1550 for determining a free-wheeling hook load value (e.g., weight) using the model filter and the data of the post-connection state; and a determination block 1560 for determining weights using the filter values and the model filters and the data of the pre-connection state, wherein the weights include one or more of hook load up values and hook load down values. In the example of fig. 15, the method 1500 includes an adaptive learning phase and a detection phase, wherein the detection provides a determination regarding a value, which may include a torque loss value that may be used to determine a downhole torque value. For example, downhole torque values may be utilized in one or more workflows, which may include control workflows intended to reduce stuck pipe accidents, and the like. As an example, the method 1500 may include one or more boxes relating to pressure, such as off-bottom pressure and/or pressure differential.

As an example, the trained machine model may be based on time series data including downhole sensor data. Such a trained model may be adaptive in its implementation in that various parameter values, which may be parameter values of a filter, may be threshold values and/or filter values, may be determined appropriately. Given such parameter values, one method may utilize an adjusted training model to detect data points that may be statistically processed to determine values, such as torque values, weight values, and/or pressure values.

The method 1500 may be implemented using statistical methods of weight, torque and/or pressure detection based on column experience. As indicated, one approach may divide the column type into zones (e.g., RIH, driling, and POOH). As mentioned, during a drilling interval of a drilling string, one method may calculate a statistical value, such as a medium to high value of surface torque (DrStorMed), which may be used as a threshold for torque loss detection. As mentioned, during connection of a wellbore section of a drilling riser, a method may calculate a minimum hook load value that may be used as a filtered value to calculate one or more weights. Such actions may be part of an adaptive process in which the model is utilized to "learn" the parameter values of the model for detection purposes. For example, consider learning parameter values for DrStorMed and/or ConHkldMin (e.g., connection hook load minimum) and/or conhkldmd (e.g., connection hook load median) and/or drhkldmd (e.g., drilling hook load median), and then detecting with one or more parameter values. Other values may include DrSppaMed and/or DrFlwiMed, etc. As an example, to calculate TQLS, one method may collect valid STOR data points (e.g., those STOR < DrStorMed) during the post connect state. In such an approach, the final TQLS value may be taken as the low median of the points. With respect to the determination of hkld_fr, one approach may collect data points during the post-connection state with active HKLD and RPM. In such a method, a model filter may be used to filter the points (e.g., RPM <0.7xmax (RPM), where "0.7" may be an appropriate parameter value). The final hkld_fr result value may be statistically taken as the low median of the points. As for the determination of hkld_pu and hkld_so values during the drilling phase, one method may collect points from the pre-connected state and classify the points into two groups, one being the lifting and the other being the lowering. As an example, for both, one method may first apply a HKLD < ConHkldMin filter (e.g., using the filter values of the adaptive portion). The set may be filtered by a model filter (e.g., RPM >1c/min condition, where "1" may be the appropriate parameter value). Then, for the set of the boosting, the method may take the data points for the BPOS increase, filtered by a model filter (e.g., 1.2×min (BPOS) < BPOS <0.8×max (BPOS), where "1.2" and "0.8" may be appropriate parameter values), and the final hkld_pu may be statistically determined, e.g., taken as the high median of the set. Similarly, for a drop-down group, the method may take BPOS reduced data points, filtered by a model filter (e.g., 1.2×min (BPOS) < BPOS <0.8×max (BPOS)), and the final hkld_so may be statistically determined, e.g., taken as the low median value of the group.

Regarding the RIH and POOH phases, as an example, pre-connection and post-connection states may not be defined, as no drilling occurs. In this case, hkld_so during RIH may be statistically determined as min (HKLD) when max (BPOS) -2 < BPOS < max (BPOS), where "2m" may be a suitable parameter value, and hkld_pu during POOH may be statistically determined as max (HKLD) when min (BPOS) < BPOS < min (BPOS) +2m, where "2m" may be a suitable parameter value.

As explained with reference to fig. 11, a method may include, for example, continuing one or more detection processes after one or more various thresholds (e.g., one or more of DrStorMed, drHkldMed, conHkldMed, drSppaMed, drFlwiMed, etc.) have been identified.

Fig. 16 illustrates an example of a system 1600 that includes various example inputs 1621-1627 and various example outputs 1681-1687 for a machine learning model (ML model) 1650 that may be generated using the ML model 1650 as a trained ML model. As shown, the inputs may include rig state 1621, drilling state 1622, vehicle position (BPOS) 1623, RPM 1624, hook load (HKLD) 1625, surface Torque (STOR) 1626, and one or more other inputs 1627 (e.g., taking into account one or more pressures (SPPA, etc.), flow rates (FLWI, etc.), etc., as shown, the outputs may include hook load drop (hkld_so) 1681, hook load lift (hkld_pu) 1682, hook load free rotation (hkld_fr) 1683, weight-on-bit (DWOB) 1684, torque loss (TQLS) 1685, downhole Torque (DTOR), and one or more other outputs 1687 (e.g., taking into account one or more pressures (OFBP, DPRES, etc.)).

As an example, system 1600 may be used in a method, such as, for example, method 1400 of fig. 14, which may include various portions, such as training, implementation, and control. By way of example, system 1600 may utilize one or more features of system 1490, which may be local, distributed, remote, both local and remote, and the like. As an example, system 1600 may be used with one or more aspects explained with respect to GUIs 1100, 1200, and 1300 of fig. 11, 12, and 13. As an example, a system such as system 1600 may be used to directly and/or indirectly determine one or more values that may be used in one or more methods.

Fig. 17 shows an example of a graphical user interface 1700 that includes a graphic of a system 1710, a graphic of an example of a drilling bit (or drill bit) 1711, and a graphic of a trajectory 1730, wherein the system 1710 may perform directional drilling to drill a borehole according to the trajectory 1730. As shown, trace 1730 includes a substantially vertical portion, a dog leg, and a substantially lateral portion (e.g., a substantially horizontal portion). The system 1710 may operate in various modes of operation, which may include, for example, rotary drilling and sliding. In the example of fig. 17, arrows illustrate the flow of drilling fluid (e.g., mud) through the openings of the drill bit 1711 (e.g., for lubrication, for carrying cuttings to the surface, etc.).

In the example of fig. 17, the longitudinal resistance along the drill string may be reduced from the surface to a maximum rocking depth at which friction and applied torque are in equilibrium. As an example, the drilling operation may include manipulating the surface torque oscillations such that the maximum rock depth may be moved deep enough to generate a significant reduction in drag. As an example, reaction torque from the drill bit can create vibrations that propagate back uphole, breaking friction and longitudinal resistance at the bottom of the drill string up to the point of interference where the torque is balanced by static friction. As shown in the example of fig. 17, the intermediate region may remain relatively unaffected by ground sway torque or reaction torque. In the example of fig. 17, the drilling operation may include monitoring torque, WOB, and ROP while sliding. As an example, such drilling operations may aim to minimize the length of the intermediate zone, thereby reducing the longitudinal resistance.

Drilling operations in a sliding mode involving manual adjustment to change and/or maintain tool face orientation can be challenging. As an example, drilling operations in a sliding mode may depend on the ability to transfer weight to the drill bit without stopping the mud motor, as well as the ability to reduce longitudinal resistance sufficiently to achieve and maintain a desired toolface angle. As an example, drilling operations in a sliding mode may be intended to achieve an acceptable ROP while taking into account one or more various other factors (e.g., equipment capacity, equipment conditions, tripping, etc.).

As an example, during drilling operations, the amount of surface torque (e.g., STOR) provided by the top drive may largely determine how far the downhole rocking motion may be transferred. As an example, the relationship between torque and rocking depth may be modeled using a torque and drag framework (e.g., a T & D framework). As an example, the system may include one or more T & D features.

As an example, the system may utilize inputs from surface hook load and riser pressure as well as downhole MWD toolface angle. In such examples, the system may automatically determine an amount of surface torque suitable for transferring downhole weight to the drill bit, which may allow operation off-bottom for toolface adjustments, which may result in more efficient drilling operations and reduced wear of downhole equipment. Such a system may be referred to as an automation assistance system.

As for the exemplary drill bit 1711, it may include various cutting structures (e.g., cutters) that may be numbered from 1 to N and are represented in a cross-sectional view that is a view showing cutter density and associated spatial information through placement of rotating cutting structures on a single radial plane. The drill bit 1711 may be, for example, a Polycrystalline Diamond Compact (PDC) bit, which may be a fixed-head bit that rotates as a unit, and does not include separate moving parts.

As shown in fig. 17, the drill bit may include blades 1712-1, 1712-2, … 1712-N, which may include, for example, primary blades and secondary blades. As an example, the blades may be part of the bit body and thus integral with the bit body. As shown, the blade may include a blade top for mounting a plurality of cutting structures (e.g., numbered 1 through N). As an example, the cutting structure may include a cutting surface, wherein the cutting structure is mounted in a pocket formed in the top of the blade. The cutting structures may be arranged adjacent to each other in radially extending rows near the leading edge of the blade. As an example, the cutting face may have an outermost cutting tip, which may be furthest from the top of the blade where the cutting structure is mounted. As shown in fig. 17, the bit body may include various passages that may allow drilling fluid to flow between blades 1712-1, 1712-2, … 1712-N and clean and cool the blades during drilling. As an example, the drill bit may be defined by a bit centerline and a bit face with blades extending radially along the bit face. As shown in fig. 17, each of blades 1712-1, 1712-2, 1712-N may extend outwardly a distance to define a channel between adjacent blades. Each blade includes a blade top, which may be defined by a blade height parameter. As mentioned, the cutting structure may be mounted on a blade, wherein drilling is "cutting" rock with the cutting structure. As an example, the cutting structure may extend outwardly beyond the top of the blade to which it is mounted. The cutting structure (e.g., cutting element) may be, for example, a PDC cutting structure, such that the drill bit may be referred to as a PDC drill bit. Shaping the PDC into a useful shape for cutting structures may involve placing diamond grits with their substrates in a pressure vessel and then sintering at high temperature and pressure. By way of example, the bit body may be considered a carrier for the cutting structure.

As an example, the drill bit may be a matrix bit (MBB) or a Steel Body Bit (SBB). The matrix may be a hard but somewhat brittle composite material that may include tungsten carbide grains metallurgically bonded with a softer, tougher metal binder. The matrix may be desirable as a bit material because its hardness may provide wear and erosion resistance. Matrix drill bits may be capable of withstanding relatively high compressive loads, but may have relatively low impact load capacity as compared to steel.

Since the matrix may be relatively heterogeneous, because it is a composite material, and since the size and location of the tungsten carbide particles, the matrix may vary (e.g., by design and environment) such that its physical properties may not be as predictable as steel.

Matrix drill bits may be manufactured by a molding process. For example, tungsten carbide and binder material may be placed in a mold and then placed in an oven for a period of time. The mold may then be cooled and released to remove the unfinished matrix drill bit.

As for steel, it can withstand high impact loads, but may be relatively soft and fail quickly due to wear and erosion without protective features. High quality steel tends to be homogenous and its structural limits tend to be predictable. The steel body may be manufactured by machining a steel bar according to a design.

The design features and manufacturing processes for different types of drill bits are different in terms of bit body construction due to the nature of the materials from which they are made. The lower impact toughness of the matrix limits some of the properties of matrix drill bits, such as blade height. In contrast, steel has ductility and toughness, and is able to withstand greater impact loads. This allows steel body PDC bits to be relatively larger than matrix bits and to incorporate greater heights into features such as blades.

Matrix PDC bits are often suitable for use in environments where matrix erosion may lead to bit failure. For diamond impregnated bits, a matrix structure may be used. The strength and ductility of the steel imparts a high impact load resistance to the steel bit body. Steel bodies tend to be stronger than matrix bodies. Due to the properties of steel materials, complex bit shapes and hydraulic designs can be manufactured on multi-axis computer numerical control milling machines. In the event that a worn or damaged cutter may be replaced, the steel drill bit may be changeable to be re-built multiple times, which is beneficial to operators in a low cost drilling environment.

The cutting structure or cutters of the drill bit may continue to be used throughout the life of the drill bit. To function properly, the cutters may be structurally supported and effectively oriented from the bit body features. The cutter orientation may be such that the cutter is loaded to a large extent (e.g., primarily) by compressive forces during operation. To prevent loss (e.g., detachment from the body), the cutter may be held, for example, by a braze material that has sufficient structural capability and has been properly deposited during the manufacturing process.

The cutter may be appropriately placed on the bit face (e.g., mounted on a blade) in an effort to ensure the desired amount of bottom hole coverage (e.g., complete bottom hole coverage). The term "cutter density" refers in part to the number of cutters used in a particular bit design. For example, PDC bit cutter density may be a function of profile shape and length, and cutter size, type, and number. If there is redundancy of cutters, the redundancy will typically increase in radius from the center of the bit to the outside because the need for work increases as the radial distance from the bit centerline increases. Cutters closer to gauge move farther, faster, and remove more rock than cutters closer to the centerline. As shown in fig. 17, cutter density may be represented by rotating the position of each cutter on a single radial plane. Such a representation may be referred to as a planar representation of cutter density, which is shown as increasing with radial position.

Reducing the number of cutters on the bit face tends to produce the result of an increased depth of cut (DOC); ROP increases; torque increases; the service life of the drill bit is shortened; however, increasing the cut density tends to result in a decrease in ROP; the cleaning efficiency of the cutting structure is reduced; and an increase in bit life.

In the example of fig. 17, for the depicted drill bit, cutter density may increase in a radial direction outward from the bit centerline, with the planar cutter striking a pattern scoring an image of the bit profile.

As mentioned, the system may provide information about the Mechanical Specific Energy (MSE), which may be or may include the Downhole Mechanical Specific Energy (DMSE).

MSE may be a measure of drilling efficiency. For example, MSE may represent the energy to remove a unit volume of rock. As an example, to obtain optimal drilling efficiency, the goal of the system may be to minimize MSE and maximize ROP. To control the MSE, various techniques may be utilized, which may include adjusting one or more control parameters, and the like. For example, the driller and/or system may control WOB, torque, ROP, and bit RPM in an effort to control MSE.

Rock processing may involve breaking fragments from a strong rock wall ground. Rock machining may involve pressing a tool into the rock surface, which may be characterized by surface hardness. The rock working process may be considered as a breaking process, since it breaks hard rock rather than cuts into small pieces of various sizes. As an example, the crushing process may be characterized by one or more energy/volume relationships. By way of example, specific energy may be defined as the energy per unit volume of rock excavated, which may be used as an indicator of the mechanical efficiency of the rock processing process. During various drilling processes, the minimum value may be approximately related to the crushing strength of the drilling medium for rotation, percussive rotation, and roller cone bit drilling.

As an example, the equation for MSE may be as follows:

where A is the cross-sectional area of the borehole, the units of MSE may have psi, ft-lb ft ³ Etc.

As an example, the bit efficiency value may be determined using the minimum MSE divided by the obtained MSE. As an example, MSE and ROP may be inversely proportional for a given rig power. In various drilling operations, rock broken into smaller than enough fragments to be evacuated may result in more energy consumption, while rock broken into fragments that are too large to be evacuated may require energy consumption in further braking (e.g., broken into smaller fragments).

As an example, parameter-dependent drilling may be characterized in terms of depth of cut (DOC), where for example small depth of cut may be associated with grinding and high friction, which may result in high MSE and low ROP, and where for example increased DOC may be converted from scratch and grinding to cracking and breaking of rock. For example, a higher DOC may result in spalling and breaking of larger pieces of material, while less reduction to smaller pieces by regrinding, which may result in lower MSE due to more efficient volumetric removal.

Although MSE may be a parameter used in control, as noted, the foregoing example MSE equations include WOB and RPM. As an example, the control process may utilize one or more of WOB and RPM, optionally in addition to one or more other parameters. As an example, the control process may include monitoring an MSE, which may be used for one or more purposes (e.g., control, diagnostics, etc.).

As an example, the well may be a large displacement well (ERW) to be drilled by large displacement-reach drilling (ERD). For example, ERW may be drilled using directional drilling, with the horizontal extent (HR) of drilling reaching a Total Depth (TD) greater than or equal to two times the True Vertical Depth (TVD). ERD is challenging in directional drilling and requires specialized planning to perform the drilling operations.

For example, ERD may be defined as including deep wells with a horizontal distance to depth or H: V ratio less than 2. As an example, ERD databases may classify wells as low, medium, large displacement wells and very large displacement wells as well construction complexity increases. Construction complexity may depend on various factors including, for example, water depth (offshore oil well), rig capacity, geological constraints, and overall TVD. For example, vertical wells with TVD greater than 7,620 meters (25,000 feet) may be considered large displacement wells. Furthermore, depending on the conditions, drilling in deep water or salt formations may be categorized as ERD even if the horizontal extent of the well does not exceed twice its TVD. As an example, ERD may be used to drill from another location that may be more advantageous than the target vertically above. For example, consider drilling from an onshore point to reach a target vertically below a body of water. In various circumstances, drilling from an onshore location may be more desirable than drilling from an offshore location (e.g., a platform, etc.).

FIG. 18 shows an example GUI 1800 including a graphical representation of a geological environment including 7 exploration wells and 6 development wells completed by 9 sidetrack drills. By way of example, a system such as system 1600 may be employed for one or more types of operation in such an environment. As an example, consider the use of system 1600 to drill one or more sections of one or more wells. In such examples, various conditions may exist, occur, etc., for example, consider a 12.25 inch segment (e.g., about 31.8 cm), where conditions of a pack-off event are observed.

As an example, consider a 17.5 inch section (e.g., about 44.5 cm) for each section that achieves a 50 degree incline for multiple wells, while a 12.25 inch section (e.g., about 31.8 cm) is to land to 90 degrees for multiple wells. As an example, an 8.5 inch section (e.g., about 21.6 cm) may be drilled substantially horizontally (e.g., a lateral section, etc.). As an example, the system may facilitate drilling of one or more sections experiencing one or more wellbore cleaning problems. For example, consider identifying a sensitive dip for borehole cleaning, which may be between about 30 degrees and about 70 degrees.

FIG. 19 illustrates an example GUI 1900 of bit depth as a function of measured depth versus time in days for drilling six wells. GUI 1900 provides data for understanding the performance of each well, particularly the daily schedule of 12.25 inch sections of the well. As can be seen in GUI 1900, optimal performance is achieved during drilling of well 15H, which reaches 2600m, while wells 11H and 14H face lower performing intervals. During well 14H, it took approximately 12 hours to drill two posts (each point representing approximately one post) at approximately 2000 meters. With respect to the performance of well 11H, the ROP is lower between 1400m and 1600m, but does not drop as abruptly as well 14H.

As explained, MSE may be a parameter that may be used to characterize drilling, such as drilling efficiency. In particular, MSE may serve as a good indicator of drilling efficiency. Although various equations for MSE are given above, consider the following MSE equation as another example:

MSE = input power/output ROP

The MSE concept is often more applicable to vertical sections when calculated using surface data, while the MSE concept is less reliable when using surface data in highly deviated wells, suggesting the use of downhole parameters to eliminate energy loss from the wellbore. As such, a system such as system 1600 may be used for various outputs as shown in fig. 16, which may be outputs of various downhole parameters. As an example, a method may include estimating various downhole parameters, wherein a Downhole MSE (DMSE) may be calculated. For example, consider the following example equation for DMSE:

DMSE＝480TORxTRPM/(ROPxD ² )+4DWOB/(πD ² )

wherein:

DMSE-mechanical specific energy downhole in MPa

TRPM, total revolutions per minute in c/min

ROP rate of penetration in m/h

DWOB-weight on bit downhole in kN

DTOR, downhole torque in kN.m

D, diameter of drill bit, unit is m

FIG. 20 shows an exemplary GUI 2000 of various outputs of six wells, where the lift and drop weights obtained during connection may be compared using a broom model. Fig. 20 also shows a particular portion of GUI 2000 as an example GUI 2100 (see, e.g., the enlarged version of fig. 21). As an example, the system may generate a GUI including a multi-well broom with automatically acquired lift and drop points. As an example, a system such as system 1600 of fig. 16 may be used to generate output for one or more wells, one or more segments of one or more wells, and the like.

Fig. 21 shows a GUI 2100, which is an enlarged view of the GUI 2100 labeled in fig. 20. In the example GUI 2000 of fig. 20, and in particular in the GUI 2100 shown in fig. 21, for the well 14H, when a cave is observed (e.g., highlighted by a rectangular box), the coefficient of friction appears to increase slightly during depth and further increases immediately before the hole (POOH) is pulled out. During the tripping process, an over-pulling force of 30 kgf was recorded, resulting in a scraper tripping down to better clean the wellbore and avoid sticking the tubing. As explained, stuck pipes can cause various problems, resource consumption, delays (e.g., non-productive time (NPT)), and the like. As noted, it takes about 200 hours to complete a 12.25 inch segment (e.g., about 31 cm), with the scraper taking about 8% (e.g., 16 hours) of the time it takes to drill down at this stage.

Fig. 22 shows an example GUI 2200 of a DMSE of 6 wells plotted against measured depth, noting that Total Vertical Depth (TVD) may additionally or alternatively be used. After processing the data using the automated state and reference connection technique, DMSE calculations were performed on the 6 wells. In GUI 2200, the results are displayed adjacent to each other along with the MSE calculated using the ground parameters alone. The BHA used for each well was similar, with no motor, nor downhole measurements. As shown in GUI 2200, the difference between DMSE and MSE is colored in green. As noted, DMSE is below MSE, and this difference increases with tilt angle.

MSE and DMSE increase with depth, e.g., as rock becomes stiffer, and/or as the tool wears (e.g., bit wears). As an example, a section of a well may be planned to be drilled using a single drill bit having sufficient characteristics for drilling the section. In the event that the bit wear exceeds the planned bit wear, it may be necessary to reconsider drilling of the section. For example, control parameters may be adjusted in an effort to drill the section without having to lift the drill string out of the hole (POOH) to account for bit wear, such as by changing the bit. In such examples, one or more adjustments may come at the cost of time and/or other resources (e.g., mud resources, energy resources, etc.).

As an example, some fluctuations and/or increases in MSE/DMSE may be observed when drilling heterogeneous and/or harder formations. For example, consider data about well 10H before returning to a less noisy signal. Wells 14H and 15H, on the other hand, show relatively high MSE and DMSE changes.

As explained, the Total Vertical Depth (TVD) may be related to rock hardness; thus, the system may generate a GUI that includes one or more representations of the TVD.

Fig. 23 illustrates an example of a graphical GUI 2300 including MSEs and DMSEs of TVDs, which may aid in interpretation, monitoring, control, etc. In FIG. 23, the multi-well analysis shows an increase in MSE/DMSE for 4 of the 6 wells at the end of formation G. The difference is that the well 14H has a long leg of high MSE/DMSE of 7GPa or more at the end of the formation G (see the top of the formation).

A multi-well pattern of a full well section drilled for a 12.25 inch section (e.g., about 31 cm) in a TVD provides identification of one or more abnormal increases in MSE. As an example, the region of the graph may be analyzed more fully, for example, by including the MSE and a plot of riser pressure and its model. As an example, a mud logging report may indicate the presence of a cavity in a formation (e.g., formation H) and subsequent loss. The presence of pockets in the annulus may be the cause of higher friction around the drill pipe, which may account for the large difference between MSE and DMSE. As an example, a possible increase of SPP may also show a correlation.

As an example, using real-time drilling data analysis in the context of monitoring, control, etc., a method may include generating and presenting a visualization of a broom model relative to actual measurements to one or more displays, and for example, for bottom-hole efficiency analysis, including one or more of drill bit wear prediction and ROP prediction. As an example, as explained, one or more types of MSE computation may be provided, which may be associated with and/or indicative of bit wear and/or ROP. As explained, the downhole MSE may be calculated (e.g., dmse=480 torxrtrpm/(ROPxD 2) +4dwob/(pi D2)).

As an example, the system may include one or more interfaces for receiving real-time ground data that may be of poor quality (e.g., in a vendor-neutral environment, etc.). In such examples, challenges faced may come from relatively low frequency data sampling (e.g., 0.1 Hz) and/or inconsistent drilling practices at the time of connection, making some calculations unusable, affecting confidence in such systems.

As an example, a system such as system 1600 of fig. 16 may provide for detecting one or more of torque loss and lift/drop/free-spin weight (e.g., hkld_pu, hkld_so, hkld_fr, etc.); support processing vendor-independent data (e.g., vendor-independent data, etc.); when data quality may be an issue, automation is supported; estimating one or more operating friction factors (e.g., facilitating stuck pipe detection workflow, etc.); helping to ensure that the drill string remains relatively free and/or that the casing can reach the total depth (e.g., the casing can be easily installed with acceptable friction so that the desired depth can be reached, etc.); supporting the operation of challenging ERD wells (ERWs) with particularly high coefficients of friction, and the like.

Fig. 24 shows an example of a GUI 2400 that includes examples of input channels, drilling status, output weight, output torque, and output pressure, wherein various information, status, etc. may be encoded (e.g., color, shading, hatching, etc. referring again to system 1600 of fig. 16, the inputs may include drilling status 1621 and drilling status 1622, for presentation to a display.

Fig. 25 shows an example table 2510 of examples of rig states and an example table 2530 of drilling states. As an example, one or more systems, subsystems, etc. may be used to determine rig state, drilling state, etc.

As explained, one or more methods, systems, etc. may utilize methods that include various input channels, states, weights, etc. For example, consider a method that utilizes information defined in GUI 2400 of FIG. 24 and/or one or more tables 2510 and 2530 of FIG. 25.

As an example, the system may provide for automatic calculation of rig operation activities and off-bottom references, which may be combined with one or more techniques to provide for analysis and interpretation of real-time drilling data. As mentioned, separate processing of surface data with suspicious data quality, which may be recorded at a relatively low frequency (e.g., 0.2Hz or higher), may present challenges. However, with a system such as system 1600 of fig. 16, machine learning may provide an output that allows for one or more well analyses of drilling performance, which may be used for monitoring, control, etc., for various purposes, which may include one or more improved efficiency gains and accuracies. As an example, a system such as system 1600 may automatically utilize machine learning for multi-well association, which may minimize the need for human interpretation.

As explained, a system such as system 1600 of fig. 16 may be used for computation, such as computation of MSE values (e.g., DMSE values), where these values may provide an indication of one or more changes in lithology while drilling. As explained, the downhole pressure calculation shows a first indicator of the cutting and cavity loads in the annulus, which results in increased non-production time (NPT) and risk of casing difficulties. Where the system is used in a drilling operation, such information may be part of a control loop, such that automated, semi-automated, etc. methods may help to alleviate problems and/or reduce NPT.

As an example, the system may provide an interpretation of real-time drilling events. As an example, ML model methods may be extended to empirical training that can predict various types of problem events that may hinder operation, which may help liberate labor to focus on other analysis and/or decision making (e.g., where manual evaluation may be required, etc.).

As explained, the system 1600 of fig. 16 may be used for one or more wells. As an example, the output of a system may be compared to the output of one or more other systems. Consider, for example, a comparison between a system such as system 1600 and a non-automated calibration engine system. Such non-automated calibration engine systems may need to perform various offline (e.g., non-real-time) tasks and/or perform various recalibrations.

FIG. 26 shows example table 2610 and example table 2630 with results for a non-automated calibration engine system (AC) and an ML model system (RC), which may include one or more ML models, wherein various tools are provided for actual measurements using the downhole measurement tool. For example, consider an optigrill system that utilizes downhole drilling mechanics and dynamic measurement subs (drill string components) to make measurements that can be used to identify the type and severity of one or more types of BHA motions, for example, to calculate a continuous borehole friction coefficient. For example, consider a 19-sensor sub that provides downhole measurements (e.g., force, pressure and temperature, rotational speed and vibration) as well as information regarding BHA motion and its severity, where the downhole data may be transmitted to the surface, integrated with the surface measurements, and displayed on the wellsite drilling dashboard.

In the example tables 2610 and 2630, various results are from 100 wells of data. Specifically, in table 2630, examples of data for 6 of 100 wells are shown, labeled A1, B1, C1, D1, E1, and F1, for simplicity. The data in tables 2610 and 2630 correspond to the differences summed with the actual measurements in 100 wells, with the ML model system providing better DTOR results in 70 of the 100 wells, better DWOB values in 94 of the 100 wells, more TQLS points in 54 of the 100 wells, and more hkld_fr points in 72 of the 100 wells. As mentioned, table 2630 shows data for 6 of the 100 wells of DTOR, TQLS, DWOB and hkld_fr. As explained, the ML model approach can be fully automated. For example, the system 1600 of fig. 16 may operate in a fully automated manner.

As an example, a framework such as ROPO framework (Schlumberger, houston, tx) provides rate of penetration optimization, and a calibration engine system such as the AC system shown in tables 2610 and 2630 of fig. 26 may be utilized. As an example, ML model systems (e.g., systems denoted RC, etc.) may be used as alternatives, e.g., using reference connections, which may provide improved reliability. As an example, a framework such as the PTK framework (Schlumberger, houston, texas) may include a rate of penetration optimization feature. In such examples, one or more ML models, ML model systems, etc. may be included, integrated, linked, etc. for improved drilling purposes.

As an example, the ML model system may provide bit wear and ROP predictions. For example, one or more outputs of system 1600 may be provided for predicting drill bit wear and/or ROP. As an example, a coefficient of friction may be estimated, which may be used in a drill string, a portion of a drill string, a drill bit, or the like.

As explained, a method may include controlling drilling to reduce the risk of sticking. As mentioned, in the event of a stuck, time and resources may be spent to resolve the stuck, which may lead to one or more problems, such as inability to release the drill string, damage to the borehole, etc. By way of example, where damage occurs, casing operations (e.g., completion operations, etc.) may be relatively complex.

As explained with respect to bit wear, the system may control drilling to achieve a desired drilling volume, which may be measured, for example, by measuring depth. For example, consider a section of a particular diameter in which a particular drill bit will be used to drill the entire section without having to pull out the drill string from the hole (POOH).

As explained, a system such as system 1600 of fig. 16 may be used to estimate the coefficient of friction during drilling. In such examples, the controller may invoke one or more operations when the coefficient of friction is not within a desired range or below a desired value. For example, consider adjusting one or more characteristics of the mud, one or more mud flow rates, utilizing a reaming process, and so forth. As an example, a cleaning process may be performed which aims at adjusting the borehole to achieve a suitable coefficient of friction. For example, the purging process may utilize a purge bit. As explained, ERD of ERW may cause friction problems due to large displacements.

As an example, the ML model may be a physics-based ML model and/or include one or more physics-based models. As an example, the ML model may be relatively lightweight, which may accelerate learning and/or reduce computational resource requirements for generating the trained one or more ML models.

Regarding the type of machine learning model, consider one or more examples, such as a Support Vector Machine (SVM) model, a k-nearest neighbor (KNN) model, an integrated classifier model, a Neural Network (NN) model, and so forth. As an example, the machine learning model may be a deep learning model (e.g., a deep boltzmann machine, a deep belief network, a convolutional neural network, a stacked auto encoder, etc.), an integration model (e.g., random forest, gradient lifting machine, bootstrap aggregation, adaptive enhancement (AdaBoost), stacked summarization, gradient lifting regression tree, etc.), a neural network model (e.g., radial basis function network, perceptron, back propagation, hopfield network, etc.), a regularization model (e.g., ridge regression, minimum absolute contraction and selection operator, elastic network, minimum angle regression), a rule system model (e.g., stereology, a rule, zero rule, repeated increment pruning to generate error reduction), a regression model (e.g., linear regression, general least squares regression, stepwise regression, multiple adaptive regression splines, local estimation scatter plot smoothing, logistic regression, etc.), bayesian models (e.g., naive bayes, mean dependency estimators, bayesian belief networks, gaussian naive bayes, polynomial naive bayes, bayesian networks), decision tree models (e.g., classification and regression trees, iterative dichotomy 3, C4.5, C5.0, chi-square auto-interaction detection, decision stumps, conditional decision trees, M5), dimensionality reduction models (e.g., principal component analysis, partial least squares regression, sammon mapping, multidimensional scaling, projection pursuit, principal component regression, partial least squares discriminant analysis, hybrid discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, flexible discriminant analysis, linear discriminant analysis, etc.), example models (e.g., k-nearest neighbors, learning vector quantization, self-organizing map, local weighted learning, etc.), cluster models (e.g., k-means, k-median, expectation maximization, hierarchical clustering, etc.), etc.

As an example, a machine model, which may be a machine learning model, may be built using a computing framework with libraries, toolboxes, etc., such as, for example, those in MATLAB frameworks (MathWorks, inc., natick, massachusetts). The MATLAB framework includes a toolbox that provides supervised and unsupervised machine learning algorithms, including Support Vector Machines (SVMs), lifting and packing decision trees, k-nearest neighbors (KNNs), k-means, k-centreline methods, hierarchical clustering, gaussian mixture models, and hidden markov models. Another MATLAB framework toolbox is a Deep Learning Toolbox (DLT) that provides a framework for designing and implementing deep neural networks with algorithms, pre-training models, and applications. DLT provides convolutional neural networks (ConvNets, CNN) and long-term memory (LSTM) networks to perform classification and regression on image, time series, and text data. DLT includes functions to build a network architecture, such as a Generative Antagonism Network (GAN) and a siamese network, using custom training loops, shared weights, and automatic differentiation. DLT provides model exchanges to various other frameworks.

As mentioned, an example of a machine learning model is a Neural Network (NN) (e.g., a neural network model) that may include neurons and connections, where each connection provides the output of one neuron as the input of another neuron. Each connection may be assigned a weight representing its relative importance. A given neuron may have multiple input and output connections. The NN may include a propagation function that computes the inputs to neurons from their precursor neuron outputs and their connections as weighted sums. As an example, a bias term may be added to the propagated results.

As an example, neurons may be organized in multiple layers, particularly in deep learning NNs. As an example, the layer receiving the external data may be an input layer and the layer generating one or more results may be an output layer. As an example, NNs may be fully connected, with each neuron in one layer connected to each neuron in the next layer. As an example, NNs may utilize pooling, where a group of neurons in one layer are connected to a single neuron in the next layer, thereby reducing the number of neurons in that layer. As an example, the NN may include connections that form a Directed Acyclic Graph (DAG), which may define a feed forward network. Alternatively, NNs may allow connections (e.g., a circular network) between neurons in the same or previous layers.

As an example, a trained ML model (e.g., a trained ML tool including hardware, etc.) may be used for one or more tasks. As an example, various types of data may be acquired and optionally stored, which may provide training of one or more ML models, retraining of one or more ML models, further training of one or more ML models, and/or offline analysis, among others.

As an example, the TENSORFLOW framework (*** responsibility inc, mountain view, california) can be implemented, which is an open source software library for data stream programming, including a symbolic math library, which can be implemented for machine learning applications that can include neural networks. As an example, a CAFFE framework may be implemented, which is a DL framework developed by the berkeley AI study (BAIR) (university of california, berkeley division, california). As another example, consider the SCITIT platform (e.g., SCIKIT-learn) using the PYTHON programming language. As an example, a framework such as the apollo AI framework (apollo AI limited, germany) may be utilized. As an example, a framework such as the PYTORCH framework (facebook artificial intelligence research laboratory (FAIR), facebook corporation, glopak, california) may be utilized.

As explained, a system such as system 1600 of fig. 16 may provide for detection of lifting and/or lowering weight, weight on bit, torque, and/or pressure based on machine learning of surface sensors. Such methods may be implemented as standalone surface data and/or in the case of acquiring subsurface data, noting that time delays and/or transmission problems may occur in data communication from one or more downhole sensors to the surface, depending on depth (e.g., total depth or measured depth). As an example, the drilling system may automatically switch to a surface sensor-based approach in the event that one or more downhole sensors and/or downhole-to-uphole transmission channels fail or become problematic. In each case, a few seconds or minutes can vary. For example, in an automated or semi-automated system, reducing decision making (e.g., control signal issuance, etc.) may help reduce time. The various operations at the surface are automated, so that the use of surface data can help to expedite these operations as compared to the use of downhole data without directly involving manual labor; note that in various cases, the downhole data may be used to examine surface data calculations, and one or more operations may be adjusted (e.g., slowing down, etc.) as appropriate, e.g., in the event of one or more discrepancies between the output based on the surface data and the downhole data, so that the downhole data may be utilized until such one or more discrepancies decrease. In such an example, once reduced, operations can be performed more quickly once switching back to using surface data.

As explained, wells such as ERW using ERD drilling may be quite long (e.g., consider wells 10 km or more). As drilling progresses, in the case of using one or more downhole sensors, it may be expected that latency with respect to downhole data will increase, which may in various cases make more efficient use of surface data calculations.

FIG. 27 illustrates an example of a method 2700 that includes an identification block 2710 for identifying a threshold value for determination of a drill string off-bottom condition; a filtering block 2720 for filtering the time-series surface data of the post-connection drilling state of the drill string using a threshold to generate filtered time-series surface data; and a determination block 2730 for statistically determining a drill string off-bottom condition value using the filtered time-series surface data. As shown in the example of fig. 27, the method 2700 may include a determination block 2740 for determining a downhole operational drilling value using drill string off-bottom condition values (e.g., downhole torque, downhole friction coefficient, etc.). As an example, the drill string off-bottom condition value may be a torque loss value, a hook load value, or a pressure value (see, e.g., fig. 12). As an example, blocks 2720 and 2730 may be performed in series and/or in parallel. As explained, the statistical determination may utilize a "P" type of determination (e.g., P10, P50, P90, etc.). As an example, the statistical determination may utilize a median determination. The median determination may provide a more reliable value than the average determination, as the average determination may be affected by one or more data outliers, etc.

FIG. 27 also shows various computer-readable media (CRM) blocks 2711, 2721, 2731, and 2741. These blocks may include instructions executable by one or more processors, which may be one or more processors of a computing framework, system, computer, or the like. The computer readable medium may be a non-signal, a non-carrier wave, and a non-transitory computer readable storage medium. For example, a computer-readable medium may be a physical memory component capable of storing information in a digital format.

As explained, the method may operate on a column-by-column basis, where one or more states may be used to define data. Consider, for example, a state before, during, or after a connection. As an example, consider successive columns numbered 32 and 33. In such examples, a method may include identifying a threshold value for determination of a drill string off-bottom condition using data acquired prior to connecting the stand column 33; filtering time-series surface data acquired after connection of the string 33 (e.g., a post connection state of the drill string) using a threshold to generate filtered time-series surface data; and statistically determining a drill string off-bottom condition value using the filtered time-series surface data (e.g., data obtained after connecting the legs 33).

As explained, one approach may utilize one or more types of machine models. For example, consider an example analysis of 100 wells, which may provide an output of one or more parameter values, which may be used to identify a threshold. In such examples, one or more parameter values may be static or dynamic, if dynamic, they may change at a rate less than the column-by-column rate. As explained, the method may be dynamic in that the threshold varies at a column-by-column rate. As an example, a method may include utilizing one or more machine models that may provide for a determination of one or more states, for output of one or more parameter values, and/or for identifying one or more thresholds.

Fig. 28 shows an example of a system 2800, which system 2800 may be a well construction ecosystem. As shown, system 2800 can include one or more instances of ML model system 1600, and can include rig infrastructure 2810 and drilling planning component 2820, drilling planning component 2820 can generate or otherwise communicate information associated with a plan to be performed with rig infrastructure 2810, e.g., via drilling operations layer 2840 including wellsite component 2842 and offsite component 2844. As shown, data acquired and/or generated by the drilling operations layer 2840 may be transmitted to a data archiving component 2850, which may be used, for example, for purposes of planning one or more operations (e.g., in accordance with the drilling planning component 2820).

As an example, the computing framework can be implemented within or in operable coupling to a DELFI cognitive exploration and production (E & P) environment (Schlumberger of houston, texas) that is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies such as artificial intelligence and machine learning. By way of example, such an environment may provide operations involving one or more frameworks. The DELFI environment can be referred to as a DELFI framework, which can be a framework of multiple frameworks. As an example, the DELFI framework can include various other frameworks that can include, for example, one or more types of models (e.g., simulation models, etc.).

By way of example, a system such as system 1600 of FIG. 16 can be used for one or more planning, execution, etc. phases, which can be implemented using a framework such as a DELFI framework. For example, consider a simulated drilling in which surface measurements are generated that can be used as inputs to system 1600 to determine one or more performance aspects of system 1600 prior to drilling using system 1600. In such examples, the simulation may help decide how to utilize system 1600, e.g., which segment or segments may be suitable for using system 1600 for one or more purposes.

As an example, the workflow may proceed to a geological and land physical ("G & G") service provider, which may generate a well trajectory, which may involve execution of one or more G & G software packages (see also, e.g., block 2820 of system 2800 of fig. 28). Examples of such software packages include PETREL frameworks. As an example, one or more systems can utilize a framework such as a DELFI framework. Such a frame may be operably coupled to various other frames to provide a multi-frame workspace.

As an example, a method may include receiving a threshold for torque loss determination; using the threshold filtered surface torque time series data of the connected drilling state to generate filtered surface torque time series data; and statistically determining a torque loss value using the filtered ground torque time series data.

As an example, a method may include receiving a filtered value for weight determination; filtering the surface hook load time series data of the pre-connection drilling state using the filtered values to generate filtered surface hook load time series data; and statistically determining a weight value using the filtered ground torque time series data.

As an example, a system may include a processor; a memory accessible to the processor; processor-executable instructions stored in memory, the instructions being executable to instruct the system to receive a threshold value for torque loss determination and a filtered value for weight determination; using the threshold filtered surface torque time series data of the connected drilling state to generate filtered surface torque time series data; statistically determining a torque loss value using the filtered ground torque time series data; filtering the surface hook load time series data of the pre-connection drilling state using the filtered values to generate filtered surface hook load time series data; and statistically determining a weight value using the filtered ground torque time series data.

As an example, one or more computer-readable storage media may include processor-executable instructions to instruct a computing system to receive a threshold value for torque loss determination and a filtered value for weight determination; using the threshold filtered surface torque time series data of the connected drilling state to generate filtered surface torque time series data; statistically determining a torque loss value using the filtered ground torque time series data; filtering the surface hook load time series data of the pre-connection drilling state using the filtered values to generate filtered surface hook load time series data; and statistically determining a weight value using the filtered ground torque time series data.

As an example, a method may include identifying a threshold value for determination of a drill string off-bottom condition; filtering the time series surface data of the post-connection drilling state of the drill string using a threshold to generate filtered time series surface data; and statistically determining a drill string off-bottom condition value using the filtered time-series surface data. In such examples, the drill string off-bottom condition value may be a torque loss value, wherein, for example, the method may include determining a downhole torque value based at least in part on the torque loss value. In such examples, determining the downhole torque value may include utilizing a difference between the surface torque and the torque loss. As an example, the downhole torque loss value may be indicative of an amount of cuttings adjacent to at least a portion of the drill string, particularly if the drill string is disposed in a deviated well.

As an example, the drill string off-bottom condition value may be a hook load value. For example, consider one or more hook load free rotation weight values, hook load ramp up values, or hook load ramp down values. As an example, a method may include determining a weight-on-bit downhole based at least in part on a hook load value. In such an example, consider determining weight-on-bit using a difference between a hook load value that is a hook load free rotation value and at least one measured hook load value.

As an example, a method may include determining a coefficient of friction based at least in part on a value of a drill string bottoming condition. In such examples, the method may include utilizing a broom model.

As an example, the drill string off-bottom condition value may be a pressure value. Consider, for example, a unseating pressure and/or a pressure differential.

As an example, the drill string off-bottom condition value may be an intermediate value. For example, in statistics and probability theory, the median value is the value separating the upper half of the data sample, population, or probability distribution from the lower half. For a dataset, the median (or median) value may be considered as the "median" value. As an example, a method using a median may be compared or contrasted with a method using an average (e.g., an average), where the median is not skewed by a fraction of a maximum or minimum value by the comparison. Thus, the median may better represent the possible expectations, etc., than the average.

As an example, a method may include identifying a threshold using surface data of a drilling state of a drill string. As an example, a method may include filtering surface data of a post connection state of a drill string, where such filtering may utilize one or more filter models. In such examples, the method may include utilizing parameter values of the filter model determined from the offset well data, which may include comparing offset well data results of different parameter values.

As an example, a method may include identifying a threshold value by utilizing time-series surface data acquired during drilling of a drill string.

As an example, a method may include filtering with a filter model that includes parameter values determined from offset well data. In such examples, the offset well data may include downhole sensor-based data.

As an example, a method may include identifying a plurality of different thresholds for a plurality of different drill string off-bottom condition determinations; filtering the time-series surface data of the post-connection drilling state of the drill string using a plurality of different thresholds to generate filtered time-series surface data; and statistically determining a plurality of different drill string off-bottom condition values using the filtered time-series surface data, wherein the plurality of different drill string off-bottom condition values may include at least one member selected from the group consisting of a torque value, a weight value, and a pressure value.

As an example, a system may include a processor; a memory accessible to the processor; processor-executable instructions stored in the memory, the instructions being executable to instruct the system to identify a threshold value for determination of a drill string bottoming condition; filtering the time series surface data of the post-connection drilling state of the drill string using a threshold to generate filtered time series surface data; and statistically determining a drill string off-bottom condition value using the filtered time-series surface data.

As an example, the one or more computer-readable storage media may include processor-executable instructions to instruct a computing system to identify a threshold value for a drill string bottoming condition determination; filtering the time series surface data of the post-connection drilling state of the drill string using a threshold to generate filtered time series surface data; and statistically determining a drill string off-bottom condition value using the filtered time-series surface data.

As an example, the method may be implemented in part using a Computer Readable Medium (CRM), e.g., as a module, block, etc. Which includes information such as instructions adapted to be executed by one or more processors (or processor cores) to instruct a computing device or system to perform one or more actions. As an example, a single medium may be configured with instructions to allow, at least in part, various actions of a method to be performed. By way of example, a Computer Readable Medium (CRM) may be a computer readable storage medium (e.g., a non-transitory medium) that is not a carrier wave.

According to an embodiment, one or more computer-readable media may include computer-executable instructions to instruct a computing system to output information for controlling a process. For example, such instructions may provide outputs to sensing processes, injection processes, drilling processes, extraction processes, extrusion processes, pumping processes, heating processes, and the like.

In some embodiments, one or more methods may be performed by a computing system. FIG. 29 illustrates an example of a system 2900 that may include one or more computing systems 2901-1, 2901-2, 2901-3, and 2901-4, which may be operatively coupled via one or more networks 2909, which networks 2909 may include wired and/or wireless networks.

As an example, the system may comprise a separate computer system or an arrangement of distributed computer systems. In the example of fig. 29, computer system 2901-1 may include one or more modules 2902 that may be or include processor-executable instructions, for example, that are executable to perform various tasks (e.g., receive information, request information, process information, simulate, output information, etc.).

As an example, the modules may execute independently or in conjunction with the one or more processors 2904, the processor 2904 being operatively coupled to the one or more storage media 2906 (e.g., via wire, wireless, etc.). As an example, one or more of the one or more processors 2904 may be operatively coupled to at least one of the one or more network interfaces 2907. In such examples, computer system 2901-1 may send and/or receive information, for example, via one or more networks 2909 (e.g., consider one or more of the internet, a private network, a cellular network, a satellite network, etc.).

By way of example, computer system 2901-1 may receive information from and/or send information to one or more other devices, which may be or include, for example, one or more computer systems 2901-2, and the like. The device may be located in a physical location different from the physical location of the computer system 2901-1. As examples, the location may be, for example, a processing facility location, a data center location (e.g., server farm, etc.), a rig location, a wellsite location, a downhole location, etc.

By way of example, a processor may be or include a microprocessor, microcontroller, processor module or subsystem, programmable integrated circuit, programmable gate array, or other control or computing device.

By way of example, storage medium 2906 may be implemented as one or more computer-readable or machine-readable storage media. As an example, the storage may be distributed within and/or among multiple internal and/or external chassis of the computing system and/or additional computing systems.

By way of example, the one or more storage media may include one or more different forms of memory, including semiconductor memory devices such as dynamic or static random access memory (DRAM or SRAM), erasable and programmable read-only memory (EPROM), electrically erasable and programmable read-only memory (EEPROM) and flash memory, magnetic disks such as fixed, floppy and removable disks, other magnetic media including magnetic tape, optical disks such as Compact Disks (CD) or Digital Video Disks (DVD), BLUERAY optical disks, or other types of optical storage or other types of storage devices.

By way of example, one or more storage media may reside in a machine executing machine-readable instructions or at a remote site from which machine-readable instructions may be downloaded over a network for execution.

By way of example, various components of a system, such as, for example, a computer system, may be implemented in hardware, software, or a combination of hardware and software (e.g., including firmware), including one or more signal processing and/or application specific integrated circuits.

As an example, the system may include a processing device, which may be or include a general purpose processor or a dedicated chip (e.g., or chipset), such as ASIC, FPGA, PLD or other suitable device.

Fig. 30 illustrates components of a computing system 3000 and a networking system 3010 having a network 3020. The system 3000 includes one or more processors 3002, memory and/or storage components 3004, one or more input and/or output devices 3006, and a bus 3008. According to an embodiment, the instructions may be stored in one or more computer-readable media (e.g., memory/storage component 3004). Such instructions may be read by one or more processors (e.g., processor 3002) via a communication bus (e.g., bus 3008), which may be wired or wireless. One or more processors may execute such instructions to implement (in whole or in part) one or more attributes (e.g., as part of a method). A user may view and interact with output from a process through an I/O device (e.g., device 3006). According to an embodiment, the computer readable medium may be a storage component, such as a physical memory storage device, e.g., a chip on a package, a memory card, etc.

According to an embodiment, the components may be distributed, for example in the network system 3010. The network system 3010 includes components 3022-1, 3022-2, 3022-3, 3022-N. For example, the component 3022-1 may include the processor 3002 and the component 3022-3 may include memory accessible by the processor 3002. Further, the assembly 3022-2 may include an I/O device for displaying and optionally interacting with the method. The network may be or include the internet, an intranet, a cellular network, a satellite network, and the like.

By way of example, the device may be a mobile device that includes one or more network interfaces for communication of information. For example, the mobile device may include a wireless network interface (e.g., operable via IEEE 802.11, ETSI GSM, bluetooth, satellite, etc.). As an example, a mobile device may include components such as a main processor, memory, display graphics circuitry (e.g., optionally including touch and gesture circuitry), SIM slots, audio/video circuitry, motion processing circuitry (e.g., accelerometers, gyroscopes), wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS circuitry, and batteries. As an example, the mobile device may be configured as a cell phone, tablet, or the like. As an example, a method may be implemented (e.g., in whole or in part) using a mobile device. As an example, a system may include one or more mobile devices.

By way of example, the system may be a distributed environment, such as a so-called "cloud" environment, in which various devices, components, etc. are located. Interaction is for data storage, communication, computation, etc. As an example, a device or system may include one or more components for communicating information via one or more of the internet (e.g., where communication is via one or more internet protocols), a cellular network, a satellite network, and so forth. As an example, the method may be implemented in a distributed environment (e.g., as a cloud-based service in whole or in part).

As an example, information may be input from a display (e.g., consider a touch screen), output to a display, or both. As an example, the information may be output to a projector, a laser device, a printer, etc., so that the information may be viewed. As an example, the information may be output stereoscopically or holographically. As for the printer, consider a 2D or 3D printer. As an example, a 3D printer may include one or more substances that may be output to construct a 3D object. As an example, the data may be provided to a 3D printer to construct a 3D representation of the subsurface formation. As an example, a layer may be constructed in a 3D manner (e.g., horizon, etc.), a three-dimensional constructed geologic volume, etc. As an example, holes, cracks, etc. may be constructed in a 3D manner (e.g., as positive structures, as negative structures, etc.).

Although only a few examples have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in these examples. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical ground to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures.

Claims

1. A method, comprising:

identifying a threshold value for determination of a drill string off-bottom condition;

filtering the time series surface data of the post-connection drilling condition of the drill string using the threshold to generate filtered time series surface data; and

the filtered time series surface data is used to statistically determine a drill string off-bottom condition value.

2. The method of claim 1, wherein the drill string off-bottom condition value comprises a torque loss value.

3. The method of claim 2, comprising determining a downhole torque value based at least in part on the torque loss value.

4. A method according to claim 3, wherein the determining comprises using the difference between the ground torque and the torque loss.

5. The method of claim 3, wherein the downhole torque loss value is indicative of an amount of cuttings adjacent at least a portion of a drill string.

6. The method of claim 5, wherein the drill string is disposed in an inclined well.

7. The method of claim 1, wherein the drill string off-bottom condition value comprises a hook load value.

8. The method of claim 7, wherein the hook load value comprises a hook load free rotation weight value, a hook load ramp-up value, or a hook load ramp-down value.

9. The method of claim 7, comprising determining a weight-on-bit downhole based at least in part on a hook load value.

10. The method of claim 9, wherein determining weight-on-bit includes utilizing a difference between a hook load value as a hook load free-wheeling value and at least one measured hook load value.

11. The method of claim 1, comprising determining a coefficient of friction based at least in part on a drill string off-bottom condition value.

12. The method of claim 11, comprising utilizing a broom model.

13. The method of claim 1, wherein the drill string off-bottom condition value comprises a pressure value.

14. The method of claim 1, wherein the drill string off-bottom condition value comprises a median value.

15. The method of claim 1, wherein identifying the threshold comprises utilizing time-series surface data acquired during drilling by the drill string.

16. The method of claim 1, wherein the filtering comprises utilizing a filter model comprising parameter values determined via offset well data.

17. The method of claim 16, wherein the offset well data comprises downhole sensor-based data.

18. The method of claim 1, comprising identifying a plurality of different thresholds for a plurality of different drill string off-bottom condition determinations; filtering the time series surface data of the post connection drilling condition of the drill string using a plurality of different thresholds to generate filtered time series surface data; and statistically determining a plurality of different drill string off-bottom condition values using the filtered time-series surface data, wherein the plurality of different drill string off-bottom condition values comprises at least one member selected from the group consisting of a torque value, a weight value, and a pressure value.

19. A system, comprising:

a processor;

a memory accessible by the processor;

processor-executable instructions stored in memory and executable to instruct a system to:

20. One or more computer-readable storage media comprising processor-executable instructions to instruct a computing system to: