CN112330038A

CN112330038A - Method, device and equipment for determining stress condition of tubular column

Info

Publication number: CN112330038A
Application number: CN202011258628.3A
Authority: CN
Inventors: 宋先知; 朱硕; 李根生; 黄中伟
Original assignee: China University of Petroleum Beijing
Current assignee: China University of Petroleum Beijing
Priority date: 2020-11-12
Filing date: 2020-11-12
Publication date: 2021-02-05
Anticipated expiration: 2040-11-12

Abstract

The embodiment of the specification provides a method, a device and equipment for determining stress condition of a pipe column, wherein the method comprises the following steps: acquiring friction coefficients of a target well at a plurality of moments before a target moment; obtaining a first drilling data set of a target well; determining hook load and rotary table torque of the target well at a target moment according to friction coefficient, the first drilling data set and a first prediction model at a plurality of moments before the target moment; and determining the stress condition of the pipe column in the target well at the target moment based on the hook load and the turntable torque at the target moment and the friction coefficient at the moment before the target moment. In the embodiment of the description, the stress condition of the tubular column in the target well at the target moment can be accurately determined at the moment before the target moment, and then the early warning analysis can be performed on the drilling abnormal conditions, such as drill sticking and the like, at the target moment before the target moment so as to ensure the safe and efficient drilling.

Description

Method, device and equipment for determining stress condition of tubular column

Technical Field

The embodiment of the specification relates to the technical field of geological exploration, in particular to a method, a device and equipment for determining stress conditions of a pipe column.

Background

With the advance of oil and gas exploration and development towards deep and complex strata, the working conditions of a drilling tool are severe, the stress of a drilling pipe column is complex, the friction torque is increased rapidly, drilling and stuck accidents occur frequently, and further the drilling efficiency is reduced. Therefore, it is very important to determine the stress condition of the downhole tubular column at the next moment in time, and the drilling sticking risk of the drilling well can be avoided, and the mechanical drilling speed can be improved, so that the safe and efficient drilling can be ensured.

In the prior art, theoretical analysis is usually performed by using tubular column mechanics, and a tubular column integral stress model is established, so that parameters such as friction coefficient and axial force of the current tubular column are calculated according to parameters such as hook load, turntable torque and the like which can be measured in real time at the current moment, and the stress condition of the downhole tubular column at the current moment is analyzed and determined. Therefore, the method for determining the stress condition of the tubular column in the prior art can only determine the stress condition of the underground tubular column at the current moment, and cannot accurately determine the stress condition of the underground tubular column at the next moment at the current moment.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The embodiment of the specification provides a method, a device and equipment for determining the stress condition of a tubular column, and aims to solve the problem that the stress condition of the underground tubular column at the next moment at the current moment cannot be accurately determined in the prior art.

The embodiment of the specification provides a method for determining the stress condition of a pipe column, which comprises the following steps: acquiring friction coefficients of a target well at a plurality of moments before a target moment; obtaining a first drilling data set of a target well; wherein the first drilling data set comprises drilling design parameter values for the target well and drilling condition data at a plurality of times prior to the target time; determining hook load and rotary table torque of the target well at a target moment according to friction coefficient, the first drilling data set and a first prediction model at a plurality of moments before the target moment; wherein the first predictive model is configured to predict hook load and rotary table torque at a target time based on friction coefficients at a plurality of times prior to the target time and data in the first drilling data set; and determining the stress condition of the pipe column in the target well at the target moment based on the hook load and the turntable torque at the target moment and the friction coefficient at the moment before the target moment.

The embodiment of the present specification further provides a device for determining a stress condition of a pipe column, including: the first acquisition module is used for acquiring friction coefficients of the target well at a plurality of moments before the target moment; a second acquisition module for acquiring a first drilling data set of a target well; wherein the first drilling data set comprises drilling design parameter values for the target well and drilling condition data at a plurality of times prior to the target time; the first determination module is used for determining the hook load and the rotary table torque of the target well at a target moment according to the friction coefficient, the first drilling data set and the first prediction model at a plurality of moments before the target moment; wherein the first predictive model is configured to predict hook load and rotary table torque at a target time based on friction coefficients at a plurality of times prior to the target time and data in the first drilling data set; and the second determination module is used for determining the stress condition of the pipe column in the target well at the target moment based on the hook load and the rotary table torque at the target moment and the friction coefficient at the moment before the target moment.

The embodiment of the specification further provides a device for determining the stress condition of the tubular column, which comprises a processor and a memory for storing executable instructions of the processor, wherein the processor executes the instructions to realize the steps of the method for determining the stress condition of the tubular column.

The embodiment of the specification also provides a computer readable storage medium, which stores computer instructions, and the instructions realize the steps of the determination method of the stress condition of the tubular column when executed.

The embodiment of the specification provides a method for determining stress conditions of a pipe string, friction coefficients of a target well at a plurality of moments before a target moment and a first drilling data set of the target well can be obtained, and therefore hook load and rotating disc torque of the target well at the target moment can be predicted by using a first prediction model according to the friction coefficients and the first drilling data set of the target well at the plurality of moments before the target moment. The first drilling data set may include drilling design parameter values of the target well and drilling condition data at a plurality of moments before the target moment. Further, the stress condition of the pipe column in the target well at the target moment can be determined based on the hook load and the rotary table torque at the target moment and the friction coefficient at the moment before the target moment. Therefore, the stress condition of the tubular column in the target well at the target moment can be accurately determined at the moment before the target moment, and then early warning analysis can be carried out on the drilling abnormal conditions such as drilling sticking and the like at the target moment before the target moment, so that the risks such as drilling sticking and the like are avoided, the mechanical drilling speed can be increased, and the safe and efficient drilling can be ensured.

Drawings

The accompanying drawings, which are included to provide a further understanding of the embodiments of the disclosure, are incorporated in and constitute a part of this specification, and are not intended to limit the embodiments of the disclosure. In the drawings:

FIG. 1 is a schematic diagram of steps of a method for determining a stress condition of a pipe string provided in an embodiment of the present disclosure;

FIG. 2 is a schematic illustration of a string segmentation result for a target well provided in accordance with an embodiment of the present description;

fig. 3 is a schematic diagram of a structure of a first target network provided according to an embodiment of the present description;

FIG. 4 is a schematic diagram of a prediction result for predicting a downhole weight-on-bit using a second prediction model provided in accordance with an embodiment of the present description;

FIG. 5 is a schematic illustration of a prediction result for predicting a downhole torque using a second predictive model provided in accordance with an embodiment of the disclosure;

FIG. 6 is a schematic diagram of a prediction result for predicting a hook load using a first prediction model according to an embodiment of the present disclosure;

FIG. 7 is a graphical illustration of a prediction of rotor disc torque using a first predictive model in accordance with an embodiment of the present disclosure;

FIG. 8 is a schematic diagram of stress conditions and early warning results of a pipe string provided in an embodiment of the present disclosure;

FIG. 9 is a schematic structural diagram of a device for determining a stress condition of a tubular column according to an embodiment of the present disclosure;

fig. 10 is a schematic structural diagram of a pipe column stress condition determination device provided in an embodiment of the present specification.

Detailed Description

The principles and spirit of the embodiments of the present specification will be described with reference to a number of exemplary embodiments. It should be understood that these embodiments are presented merely to enable those skilled in the art to better understand and to implement the embodiments of the present description, and are not intended to limit the scope of the embodiments of the present description in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

As will be appreciated by one skilled in the art, implementations of the embodiments of the present description may be embodied as a system, an apparatus, a method, or a computer program product. Therefore, the disclosure of the embodiments of the present specification can be embodied in the following forms: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.

Although the flow described below includes operations that occur in a particular order, it should be appreciated that the processes may include more or less operations that are performed sequentially or in parallel (e.g., using parallel processors or a multi-threaded environment).

Referring to fig. 1, the present embodiment may provide a method for determining a stress condition of a pipe column. The method for determining the stress condition of the tubular column can be used for determining the hook load and the turntable torque of the target well at the target moment by utilizing the first prediction model according to the drilling design parameter value of the target well, the drilling working condition data at a plurality of moments before the target moment and the friction coefficient at a plurality of moments before the target moment, and can determine the stress condition of the tubular column in the target well at the target moment based on the hook load and the turntable torque at the target moment and the friction coefficient at the moment before the target moment, so that the drilling condition of the target well can be pre-judged at the moment before the target moment according to the predicted stress condition of the tubular column in the target well at the target moment, the drilling sticking risk is avoided, the mechanical drilling speed is improved, and the safe and efficient drilling is ensured. The method for determining the stress condition of the pipe column can comprise the following steps.

S101: and acquiring friction coefficients of the target well at a plurality of moments before the target moment.

In the embodiment, the friction coefficients of the target well at a plurality of moments before the target moment can be obtained, wherein the friction coefficients can be used for representing not only the surface conditions of the drill string and the well wall, but also the influences of slurry viscosity and borehole abnormal conditions (such as a rock debris bed, a reduced diameter, a key groove and the like).

In this embodiment, the target time may be a time next to the current drilling time, but the target time may also be any other time, which may be determined according to actual needs, and the present application does not limit this. Further, the plurality of time points before the target time point may be, in an example: t-1 and t-2 … … t-n, where t is a target time, n may be a preset time step, and n is a positive integer greater than 0, for example, if the preset time step is 6, the friction coefficient of the target well at 6 times before the target time needs to be obtained. Of course, the above description is only an example, and other modifications are possible for those skilled in the art in light of the technical spirit of the embodiments of the present disclosure, and all that can be achieved is intended to be included within the scope of the embodiments of the present disclosure as long as the functions and effects achieved by the embodiments of the present disclosure are the same or similar to the embodiments of the present disclosure.

In some embodiments, the friction coefficient may include: the axial friction coefficient and the circumferential friction coefficient, wherein the axial direction refers to the direction along the axis of the cylinder, and the circumferential direction refers to the direction around the axis of the cylinder.

In this embodiment, the manner of obtaining the friction coefficients of the target well at a plurality of moments before the target moment may include: and pulling the friction coefficient from a preset database, or receiving the friction coefficient of the target well input by a user at a plurality of moments before the target moment. It is understood that, the friction coefficients of the target well at a plurality of moments before the target moment may also be obtained in other possible manners, for example, searching in a webpage according to a certain search condition may be determined according to an actual situation, which is not limited in this description embodiment.

S102: obtaining a first drilling data set of a target well; the first drilling data set comprises drilling design parameter values of a target well and drilling condition data at a plurality of moments before the target moment.

In this embodiment, a first drilling data set of the target well may be obtained, where the first drilling data set may include drilling design parameter values of the target well and drilling condition data at a plurality of times prior to the target time. The drilling design parameter of the target well may be a static parameter, i.e., the drilling design parameter may be constant for the target well regardless of the stage of drilling the target well. The drilling condition data can be time sequence data, namely the drilling condition data is data generated in the drilling process of the target well and can change in real time according to the drilling of the target well.

In one embodiment, the drilling design parameters of the target well may include: the drilling tool comprises a drilling tool assembly, a drill bit type, a drilling fluid type and the like, wherein the corresponding parameter values of the drilling tool assembly can comprise: full eyes, pendulum, tower, etc.; the values of the parameters corresponding to the above-mentioned drill bit types may include: PDC (Polycrystalline Diamond Compact), roller cone drill bits, and the like; the parameter values corresponding to the drilling fluid types may include: water-based, oil-based, gas-based, and the like. It will be appreciated that the values of the well design parameters for the same well may be the same.

In this embodiment, the drilling design parameters may be designed before drilling, and therefore, the manner of acquiring the first drilling data set may include: and receiving the drilling design parameters input by the user, or obtaining the drilling design parameters by querying according to a preset path. It is understood that the drilling design parameters may be obtained in other possible manners, for example, searching for the target drilling design parameter in a webpage according to a certain search condition, which may be determined according to actual situations, and the present application is not limited thereto.

In one embodiment, because the drilling condition data is time-series data, the drilling condition data may be acquired at a plurality of times prior to the target time. The drilling condition data at a plurality of times prior to the target time may include at least one of: the well depth, the well angle, the azimuth, the predicted value of the bottom hole weight, the predicted value of the bottom hole torque, the hook load, the surface torque, the pump pressure, the rotating speed of the rotary table, the pump stroke, the riser pressure, the casing pressure, the inlet density, the outlet density, the inlet temperature, the outlet temperature, the inlet conductance, the outlet conductance, the inlet flow, the outlet flow, the displacement, the equivalent density, the total pool volume and the like at each moment before the target moment.

In the embodiment, in the drilling process, a proper drill bit type can be selected according to different stratum conditions and drilling depths, and the rotating speed, the bit pressure, the displacement and the mud performance are in an optimal combined state, so that the highest drilling speed is obtained. As normal drilling continues, the wellbore continues to deepen. Thus, drilling condition data at multiple successive moments before the target moment may be obtained in real time. The bottom hole bit pressure and the bottom hole torque can be measured by a bottom hole sensor during drilling, but cannot be transmitted in real time, so that the bottom hole bit pressure and the bottom hole torque cannot be acquired in real time, and therefore the bottom hole bit pressure and the bottom hole torque in the first drilling data set are predicted values.

In this embodiment, the drilling condition data may be obtained by real-time measurement by sensors disposed on the ground and in the downhole, but it is understood that the drilling condition data may also be obtained by other possible manners, which may be determined according to actual conditions, and this is not limited by the examples in this specification.

S103: determining the hook load and the turntable torque of the target well at the target moment according to the friction coefficient, the first drilling data set and the first prediction model at a plurality of moments before the target moment; the first prediction model is used for predicting the hook load and the turntable torque at the target moment according to the friction resistance coefficients at a plurality of moments before the target moment and the data in the first drilling data set.

In this embodiment, the hook load and the rotary table torque of the target well at the target time may be determined based on the friction coefficient, the first drilling data set, and the first prediction model at a plurality of times before the target time. The hook load and the turntable torque at the target time may be predicted values, the first prediction model may be obtained by pre-training using a neural network, and the first prediction model may be used to predict the hook load and the turntable torque at the target time based on friction coefficients at a plurality of times before the target time and data in the first drilling data set.

In the embodiment, since the hook load and the turntable torque at the target time cannot be accurately measured at the time before the target time, the hook load and the turntable torque at the target time can be predicted by using the friction coefficient, the drilling condition data and the drilling design parameters at a plurality of times before the target time.

S104: and determining the stress condition of the tubular column in the target well at the target moment based on the hook load and the rotary table torque at the target moment and the friction coefficient at the moment before the target moment.

In the present embodiment, since the change between the friction coefficient at the target time and the friction coefficient at the time immediately before the target time is very small, the accuracy is higher than that in the case of using the empirical value. Therefore, the stress condition of the pipe column in the target well at the target moment can be determined based on the predicted hook load and the rotary table torque at the target moment and the friction coefficient at the moment before the target moment. The integral stress model of the pipe column takes the pipe column with a certain length in the well as a research object, and discusses the transmission of axial force and torque and the distribution of bending moment, contact force and buckling state. The string of pipe may be in a borehole of a target well and may include: drill string, casing string, test string, sucker rod string, coiled tubing, etc.

In this embodiment, the above-mentioned stress condition of the pipe column may include, but is not limited to, at least one of the following: the pipe column axial force, the torque, the bending moment, the contact force, the buckling state and the like. Whether the target well is abnormal in the drilling process at the target moment can be determined according to the analyzed and determined pipe column stress condition at the target moment, so that early warning analysis can be performed on drilling abnormal conditions such as drill sticking and the like at the target moment in the previous moment of the target moment, and the drilling safety is improved. Of course, the stress condition of the pipe column is not limited to the above examples, and other modifications may be made by those skilled in the art in light of the technical spirit of the embodiments of the present disclosure, but the functions and effects achieved by the present disclosure are all within the scope of the embodiments of the present disclosure.

In one embodiment, the tubular columns of the target well may be divided according to preset intervals to obtain multiple tubular columns, and characteristic parameters of each tubular column in the multiple tubular columns are obtained. Furthermore, the bottom hole drilling pressure, the bottom hole torque and the axial force of the pipe column can be determined by utilizing the whole stress model of the pipe column according to the characteristic parameters of each section of the pipe column, the hook load and the turntable torque at the target moment and the friction coefficient at the previous moment of the target moment.

In this embodiment, the preset interval may be a value greater than 0, for example: 10 meters, 13.5 meters and the like, which can be determined according to actual conditions and is not limited in the application. The characteristic parameters of the various sections of the tubular string may be used to characterize the state of the tubular string, and in some embodiments may include at least one of: the curvature, the flexibility, the bending rigidity, the inner diameter, the wall thickness, etc. of each section of the pipe string.

In the present embodiment, the string of the target well is preset as described aboveAfter dividing intervals into N sections, the intervals can be sorted from the ground down, that is, the serial number of each section of pipe column from the ground to the drill bit can be [0,1,2,3, …, i, i +1, …, N-1 [ ]]The length of each corresponding segment is [ d ]_s0,d_s1,d_s2,…,d_si,…d_sN-1]. Wherein, if the tubular column of the target well can be just divided into N equal parts, then d_s0＝d_s1＝d_s2＝…＝d_si＝…d_sN-1Intervals. Otherwise, d_s0＝L-(N-1)×intervals，d_s1＝d_s2＝…＝d_si＝…d_sN-1Intervals. Further, the well depths corresponding to two ends (one end close to the ground and one end close to the drill bit) of the N unit segments may be: (0, d)_s0)、(d_s0,1×intervals+d_s0)、(1×intervals+d_s0,2×intervals+d_s0)、……、((i-1)×intervals+d_s0,i×intervals+d_s0)、……、((N-1-1)×intervals+d_s0,(N-1)×intervals+d_s0) The segmentation result can be shown in fig. 2, in which the characteristic parameter of each string corresponds to the initialization parameter.

In one embodiment, in the case where the interval is 10m and the string is 316m long, the string may be divided into 32 sections, the corresponding string section number from the surface to the drill bit is [0,1,2,3,4,5 …,31], the corresponding ith unit section length is [6,10,10,10 … 10], and the corresponding well depth from the surface to both ends of the string section (near the surface, drill bit) is [ (0,6), (6,16), (16,26), (26,36), … (306,316) ].

In one embodiment, the stress condition of the tubular string in the target well at the target moment can be determined by using a tubular string overall stress model, and the transfer equation of the tubular string overall stress model can be shown as the following formula, taking the pressure as positive:

in the formula: f_iThe axial force of the ith section of pipe column close to the wellhead is N (ox); f_i+1The axial force of the ith section of the pipe column close to the drill bit is expressed by N; m_TiThe torque of the ith section of pipe column close to the wellhead is N.m; m_Ti+1The torque of the ith section of the pipe column close to the drill bit is N.m; EI (El)_iThe bending rigidity of the ith section of pipe column;

for wellbore curvature of the i-th string near the wellhead, m^-1；

For the curvature of the borehole at the i-th string near the drill bit, m^-1；q_iThe linear weight of the ith section of tubular column is N/m; alpha is alpha_iIs the well inclination angle of the ith section of tubular column; n is_tiThe contact force between the ith section of pipe column and the well wall is N/m; Δ s_iThe length m of the unit section corresponding to the ith section of the pipe column; d_biThe outer diameter m of the ith section of tubular column; mu.s₁And mu₂Respectively the axial friction coefficient and the circumferential friction coefficient of the current pipe column.

In this embodiment, the torque at the lowermost end of the string near the bit is the bottom hole torque and the torque at the uppermost end of the string near the wellhead is the rotary table torque. The hook load and the rotary table torque at the target moment and the friction coefficient at the previous moment of the target moment can be substituted into the formula, and the stress analysis result of the tubular column in the target well at the target moment can be obtained by iteratively calculating the friction coefficient to the drill bit section by section from the well mouth. Specifically, the axial force of each section of pipe column in the target well at the target moment can be obtained by calculating from the well head to the drill bit section by section through iterative calculation, and different values of the axial force correspond to different buckling states.

In one embodiment, the bending moment may be calculated according to the following formula:

M_b＝EI×k_b

wherein M is_bThe bending moment on the pipe column is expressed in the unit of N.m; EI being a tubular columnFlexural rigidity in units of N.m²；k_bIs the borehole axis curvature, in units, m^-1。

In one embodiment, the contact force may be calculated according to the following formula:

wherein n is_tThe contact force of the pipe column and the well wall (casing pipe) is N/m; the parameter A, B may be calculated; mu.s₂Is a circumferential friction coefficient and is dimensionless.

In one embodiment, the buckling state of the pipe string may be judged in the following manner, F_sinIs a sinusoidal buckling critical load; f_{hel_inter}For low-order helical buckling, F_{hel_intra}High order helical buckling. F is the axial force of the pipe column, when F is less than F_sinWhen the pipe column is not bent; when F is greater than F_sinLess than F_{hel_inter}When the pipe column is in sinusoidal buckling; when F is greater than F_{hel_inter}And is less than F_{hel_intra}When the pipe column is bent, the low-order spiral of the pipe column is bent; when F is greater than F_{hel_intra}When the pipe column is bent spirally at a high order.

From the above description, it can be seen that the embodiments of the present specification achieve the following technical effects: the friction coefficients of the target well at a plurality of moments before the target moment and the first drilling data set of the target well can be obtained, so that the hook load and the rotating disc torque of the target well at the target moment can be predicted by using the first prediction model according to the friction coefficients and the first drilling data set of the target well at the plurality of moments before the target moment. The first drilling data set may include drilling design parameter values of the target well and drilling condition data at a plurality of moments before the target moment. Further, the stress condition of the pipe column in the target well at the target moment can be determined based on the hook load and the rotary table torque at the target moment and the friction coefficient at the moment before the target moment. Therefore, the stress condition of the tubular column in the target well at the target moment can be accurately determined at the moment before the target moment, and then early warning analysis can be carried out on the drilling abnormal conditions such as drilling sticking and the like at the target moment before the target moment, so that the risks such as drilling sticking and the like are avoided, the mechanical drilling speed can be increased, and the safe and efficient drilling can be ensured.

In one embodiment, before obtaining the friction coefficients of the target well at a plurality of moments before the target moment, the method may further include: a second drilling data set of the target well is obtained, wherein the second drilling data set comprises drilling design parameter values of the target well and drilling condition data at a plurality of moments before the target moment. The predicted values of the bottom-hole weight and the bottom-hole torque of the target well at a time before the target time can be determined according to the second drilling data set and a second prediction model, wherein the second prediction model is used for predicting the bottom-hole weight and the bottom-hole torque at the time before according to the data in the second drilling data set. Furthermore, the hook load and the measured value of the turntable torque of the target well at the moment before the target moment can be obtained, and the friction coefficient of the target well at the moment before the target moment is determined by utilizing the integral stress model of the pipe column according to the predicted values of the bottom hole drilling pressure and the bottom hole torque, the measured values of the hook load and the turntable torque at the moment before the target moment.

In this embodiment, since the drilling at the time immediately before the target time is completed, the hook load and the turntable torque of the target well at the time immediately before the target time can be obtained in real time by the ground sensor, and the accuracy of determining the friction coefficient can be improved by using the actually measured hook load and turntable torque.

In this embodiment, the transfer equation of the integral stress model of the tubular column may be represented by the following formula, taking the pressure as positive:

in the formula: f_iIs the ith section of pipe columnAxial force near the wellhead, N (newton); f_i+1The axial force of the ith section of the pipe column close to the drill bit is expressed by N; m_TiThe torque of the ith section of pipe column close to the wellhead is N.m; m_Ti+1The torque of the ith section of the pipe column close to the drill bit is N.m; EI (El)_iThe bending rigidity of the ith section of pipe column;

for wellbore curvature of the i-th string near the wellhead, m^-1；

For the curvature of the borehole at the i-th string near the drill bit, m^-1；q_iThe linear weight of the ith section of tubular column is N/m; n is_tiThe contact force between the ith section of pipe column and the well wall is N/m; Δ s_iThe length m of the unit section corresponding to the ith section of the pipe column; d_biThe outer diameter m of the ith section of tubular column; mu.s₁And mu₂Respectively the axial friction coefficient and the circumferential friction coefficient of the current pipe column.

In this embodiment, the friction coefficient may be inverted by using a bisection method, and the calculation process may be: firstly, initializing between 0 and 1 to generate a friction coefficient, bringing predicted values of bottom hole drilling pressure and bottom hole torque at the previous moment of a target moment into a tubular column integral stress model, iteratively calculating the axial force and the torque of the upper end (close to the ground) of each section of tubular column section by section from a drill bit by utilizing a transfer equation of the axial force and the torque until the tubular column section closest to the ground is calculated, and obtaining the axial force and the torque value of the uppermost end (close to the ground) of the whole tubular column, wherein the axial force plus the minus sign is a calculated value of hook load, and the torque value is a calculated value of turntable torque.

In this embodiment, the calculated value of the hook load and the calculated value of the turntable torque can be compared with the actually measured hook load and turntable torque, and how the error between the calculated value and the actually measured value is within the preset range indicates that the friction resistance factor is more accurate and the cycle is over. Otherwise, a new friction resistance factor is initialized again, the calculated value and the measured value are compared again section by section and are subjected to iterative calculation to the ground, and the calculation is continuously and circularly carried out until the errors between the calculated value and the measured value of the hook load and the rotating disc torque are within the preset range, so that the friction resistance coefficient at the previous moment of the target moment can be obtained.

In the embodiment, the real-time inversion of the friction coefficient can be realized by combining the actually measured hook load and the rotary table torque, the predicted values of the bottom hole bit pressure and the bottom hole torque and the whole stress model of the pipe column in the drilling process, and the problem of blindness caused by directly adopting the empirical value of the friction coefficient in the prior art is solved.

In one embodiment, before determining the predicted value of the bottom hole weight and the bottom hole torque of the target well at the time before the target time according to the second drilling data set and the second prediction model, the method may further include: and acquiring a drilling data set of at least one adjacent well of the target well, and preprocessing the drilling data set of the at least one adjacent well to obtain a preprocessed drilling data set. Further, the drilling condition data in the pre-processed drilling data set can be converted into a supervised learning format according to a preset time step to obtain a first training set, wherein the drilling condition data is time-series data. The well design parameters in the pre-processed well data set may be used as a second training set, where the well design parameters are non-time-ordered data. Therefore, the first training set can be used as input training data of a long-term and short-term memory network in a first target network, and the second training set can be used as input training data of a multi-layer feedforward neural network in the target network; the target network is obtained by constructing a long-short term memory network and a multilayer feedforward neural network, and the first target network is trained by utilizing a first training set and a second training set to obtain a second prediction model.

In this embodiment, in order to enable the data for training to better characterize the target well, the drilling data set of at least one adjacent well of the target well may be obtained in advance, and further, the drilling data set of the at least one adjacent well may be preprocessed. Wherein the preprocessing of the drilling data set for the at least one neighboring well may comprise: and performing operations of well splitting storage, data splicing, data cleaning, time sequencing and the like on the data in the drilling data set of the at least one adjacent well, so as to obtain a preprocessed drilling data set. The pre-processed drilling data set may include drilling condition data and drilling design parameters. The drilling engineering parameters may include well depth, angle of inclination, azimuth, predicted value of bottom-hole weight, predicted value of bottom-hole torque, hook load, surface torque, pump pressure, rotary table rotation speed, pump stroke, riser pressure, casing pressure, inlet density, outlet density, inlet temperature, outlet temperature, inlet conductance, outlet conductance, inlet flow, outlet flow, displacement, equivalent density, total pool volume, etc.; the drilling design parameters include drilling fluid system, drilling tool assembly type, drill bit type, etc.

In the embodiment, the drilling condition data is generated in real time along with the time change in the drilling process and has the property of time series, and the drilling design parameters are well-designed parameters before drilling and do not change along with the time, but are different from each well. The method aims at the characteristics of time sequence and non-time sequence of the data in the well drilling data set. Therefore, the first target network can be constructed by preferably an LSTM (long short term memory neural network) and a BP neural network (multi-layer feedforward neural network) which have a better processing effect on the time-series data. Among them, LSTM is designed to solve the long-term dependence problem in traditional recurrent neural networks, can remember information for long periods of time, and is well suited to handle dynamic changes in the drilling process.

In the embodiment, the long-short term memory neural network and the multi-layer feedforward neural network can obtain a double-input network structure, namely a first target network, by means of parallel connection design. In one embodiment, the first target network may be configured as shown in FIG. 3, with the first target network having two inputs, with the chronological data as the input to the LSTM and the non-chronological data as the input to the BP neural network. Correspondingly, the first target network may have two output data, the first target network is trained by using the first training set and the second training set, and the obtained output data of the second prediction model may include: bottom hole weight and bottom hole torque.

In this embodiment, before training the first target network, the drilling condition data in the preprocessed drilling data set may be converted into a supervised learning format according to a preset time step according to the structural characteristics of the LSTM, so as to obtain a first training set. The preset time step may be any value greater than 0, for example: the time sequence data of the preset time step length time 5, namely t-5, t-4, t-3, t-2 and t-1 can be selected as input data for predicting the bottom hole drilling pressure and the bottom hole torque at the t moment. It is understood that the value of the preset time step may be adjusted according to the computer power and the quality of the model prediction result, and may be determined according to the actual situation, which is not limited in this application.

In this embodiment, the well design parameters in the pre-processed well data set may be digitized using One-Hot encoding prior to training the first target network. The One-Hot coding is also called One-bit effective coding, and mainly adopts an N-bit state register to code N states, each state is provided with an independent register bit, and only One bit is effective at any time. One-Hot encoding is the representation of classification variables as binary vectors. This first requires mapping the classification values to integer values. Each integer value is then represented as a binary vector, which is a zero value, except for the index of the integer, which is marked as 1.

In the embodiment, the second prediction model can be used for accurately predicting the bottom hole bit pressure and the bottom hole torque at the next moment, so that the problems that the bit pressure read by a weight indicator is often distorted due to the influences of well deviation, well wall friction, mud performance and the like, and the bottom hole bit pressure and the bottom hole torque cannot be obtained in real time in the prior art are effectively solved.

In one embodiment, before determining the hook load and the rotary table torque of the target well at the target time according to the friction coefficient, the first drilling data set and the first prediction model at a plurality of times before the target time, the method may further comprise: a drilling data set and a friction data set of at least one adjacent well of the target well are obtained, wherein the friction data set may include a friction coefficient of the at least one adjacent well at each moment of drilling. The drilling data set for at least one adjacent well may be preprocessed to obtain a preprocessed drilling data set.

In this embodiment, since the preprocessed drilling data set includes time-series data and non-time-series data, the drilling condition data and the friction coefficient in the friction data set, which are the time-series data in the preprocessed drilling data set, may be converted into a supervised learning format according to a preset time step to obtain a third training set. And the drilling design parameters in the pre-processed drilling data set that are non-time-sequential data may be used as a fourth training set. Further, the third training set may be used as input training data of a long-short term memory network in the target network, and the fourth training set may be used as input training data of a multi-layer feedforward neural network in the target network, where the target network is constructed according to the long-short term memory network and the multi-layer feedforward neural network. And training the target network by utilizing the third training set and the fourth training set to obtain a first prediction model.

In the embodiment, the drilling condition data and the friction coefficient are generated in real time along with the time change in the drilling process, so that the method has the characteristic of time series, and the drilling design parameters are well-designed parameters before drilling and do not change along with the time, but are different from each well. The method aims at the characteristics of time sequence and non-time sequence of the data in the well drilling data set. Therefore, the second target network can be constructed by preferably an LSTM (long short term memory neural network) and a BP neural network (multi-layer feedforward neural network) which have a better processing effect on the time-series data. Among them, LSTM is designed to solve the long-term dependence problem in traditional recurrent neural networks, can remember information for long periods of time, and is well suited to handle dynamic changes in the drilling process.

In the embodiment, the long-short term memory neural network and the multi-layer feedforward neural network can obtain a double-input network structure, namely a second target network, by means of parallel connection design. In one embodiment, the second target network has two inputs, wherein the time-ordered data is used as the input of the LSTM and the non-time-ordered data is used as the input of the BP neural network. Correspondingly, the second target network may have two output data, and the third training set and the fourth training set are used to train the target network, and the obtained output data of the first prediction model may include: hook load and turntable torque.

In this embodiment, before training the first target network, the drilling condition data in the preprocessed drilling data set and the friction coefficient in the friction data set may be converted into a supervised learning format according to the structural characteristics of the LSTM, so as to obtain a third training set. The preset time step may be any value greater than 0, for example: the time sequence data of the preset time step, namely t-5, t-4, t-3, t-2 and t-1 can be selected as input data for predicting the hook load and the rotating disc torque at the t moment. It is understood that the value of the preset time step may be adjusted according to the computer power and the quality of the model prediction result, and may be determined according to the actual situation, which is not limited in this application.

In the embodiment, the friction coefficient at a plurality of moments before the target moment, the drilling design parameter value of the target well and the drilling working condition data at a plurality of moments before the target moment, which are obtained by real-time inversion, are used as the input of the first prediction model to predict the hook load and the turntable torque at the target moment, so that the prediction accuracy of the hook load and the turntable torque at the next moment can be effectively improved.

In one embodiment, after determining the stress condition of the pipe column in the target well at the target moment by using the pipe column overall stress model based on the hook load and the rotary table torque at the target moment and the friction coefficient at the moment before the target moment, the method further comprises the following steps: and determining whether the stress condition of the tubular column in the target well at the target moment is abnormal, and generating prompt information according to the stress condition of the tubular column in the target well at the target moment under the condition of determining that the abnormal condition exists. Further, the prompt information may be sent to the target processing object.

In the embodiment, whether the stress condition of the tubular column in the target well is abnormal at the target moment can be determined according to the buckling state. The normal condition is that buckling does not occur, when sinusoidal buckling and spiral buckling occur, the stress of the pipe column is serious, and prompt information is sent at the moment. The corresponding processing mode can be that under the condition of ensuring certain rock breaking capacity, the pipe column is lifted properly, the bit pressure is reduced, and the stress state of the pipe column is improved.

In this embodiment, the prompt information may include, but is not limited to: the current time, the type of anomaly, etc., although other information may be included, such as: the related data of the target well at the target time, the suggested processing mode, and the like can be determined according to the actual situation, and the embodiment of the present specification does not limit this. The target processing object may be a worker of the target well, and certainly may be other preset management personnel, which may be determined according to actual conditions, and this is not limited in this specification.

In one example scenario, taking buy 322 wells as an example, the drilling data set for an adjacent one of buy 322 wells may include: bottom hole weight, bottom hole torque, hook load, rotary table torque, well depth, well inclination angle, azimuth angle, rotation speed, riser pressure, inlet density, outlet density, inlet temperature, outlet temperature, inlet conductance, outlet conductance, inlet flow rate, outlet flow rate, displacement, equivalent density, pump 1# pump stroke, pump 2# pump stroke, total pool volume, drilling fluid type, drilling tool assembly type, and drill bit type, wherein each type of data has 3355 data samples, 70% of 2348 data samples are used for training, and 30% of 1007 data samples are used for verification.

In one example scenario, the well bore configuration design parameters for the target well are shown in Table 1 and the string configuration and parameters for the target well are shown in Table 2.

TABLE 1

Number of opening	Well depth/m	Hole diameter (casing inside diameter))/m
			One opening	301	0.3397
Two-way valve	1500	0.2445
			Three opening	3361	0.1778
Quarto	3649	0.1978

TABLE 2

Kind of pipe column	Well depth/m	Inner diameter/m	Outer diameter/m	Joint diameter/m	Line weight (kg/m)
						First kind of drill rod	2720	0.08829	01016	0.1116	19.45
Second type of drill rod	3170	0.06985	0.1143	0.1243	61.01
						Third type drill rod	3540	0.09718	0.1016	0.1116	27.00
Fourth drill rod	3649	0.06510	0.1143	0.1243	44.20

In one example scenario, the first target network may be trained based on training samples in the drilling data sets of neighboring wells to the Kyoto 322 well, as described above, resulting in a second prediction model. Further, the accuracy of the second prediction model prediction may be verified using validated samples in the drilling data set of the neighboring wells of the buoyi 322 well. Wherein, the result of predicting the bottom hole weight on bit by using the second prediction model is shown in figure 4; the result of predicting the bottom hole torque by using the second prediction model is shown in fig. 5, and as can be seen from fig. 4 and 5, the accuracy of predicting the bottom hole weight and the bottom hole torque by using the second prediction model is high.

In one scenario example, the prediction result of predicting the hook load using the first prediction model is shown in fig. 6; the predicted rotor disc torque using the first prediction model is shown in fig. 7, and it can be found from fig. 6 and 7 that the error between the predicted values and the actual values of the hook load and the rotor disc torque is small.

In a scenario example, the stress condition of the tubular column in the target well at the target time may be determined according to the technical solutions in the embodiments of the present specification according to the data in tables 1 and 2, where the stress condition of the tubular column and the early warning result are shown in fig. 8. The line graph in fig. 8 is a schematic diagram of the axial force as a function of the well Depth at the target time, where Resistance is the frictional Resistance and Depth is Depth. As can be seen from fig. 8, when the well depth is 3542 m, the axial force is abnormal, and it can be determined that the buckling state of the buyback 322 well is low-order spiral buckling, and an early warning is required at this time.

Based on the same inventive concept, the embodiment of the present specification further provides a device for determining the stress condition of a pipe column, such as the following embodiments. Because the principle of solving the problems of the device for determining the stress condition of the tubular column is similar to the method for determining the stress condition of the tubular column, the implementation of the device for determining the stress condition of the tubular column can refer to the implementation of the method for determining the stress condition of the tubular column, and repeated parts are not repeated. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated. Fig. 9 is a block diagram of a structure of a device for determining a stress condition of a tubular column according to an embodiment of the present disclosure, and as shown in fig. 9, the device may include: a first obtaining module 901, a second obtaining module 902, a first determining module 903, and a second determining module 904, and the structure will be described below.

The first obtaining module 901 may be configured to obtain friction coefficients of the target well at multiple moments before a target moment;

a second acquisition module 902 that may be used to acquire a first drilling dataset for a target well; the first drilling data set comprises drilling design parameter values of a target well and drilling working condition data at a plurality of moments before the target moment;

a first determining module 903, configured to determine a hook load and a turntable torque of the target well at the target time according to the friction coefficient, the first drilling data set, and the first prediction model at multiple times before the target time; the first prediction model is used for predicting the hook load and the turntable torque at the target moment according to the friction resistance coefficients at a plurality of moments before the target moment and the data in the first drilling data set;

the second determination module 904 may be configured to determine a stress condition of the string in the target well at the target time based on the hook load and the dial torque at the target time and the friction coefficient at a time before the target time.

The embodiment of the present specification further provides an electronic device, which may specifically refer to a schematic structural diagram of the electronic device based on the determination method of the stress condition of the tube column provided in the embodiment of the present specification, and the electronic device may specifically include an input device 41, a processor 42, and a memory 43. The input device 41 may be specifically configured to input the friction coefficient of the target well at a plurality of time points before the target time point, and a first drilling data set of the target well. The processor 42 may be specifically configured to obtain friction coefficients of the target well at a plurality of moments before the target moment; obtaining a first drilling data set of a target well; the first drilling data set comprises drilling design parameter values of a target well and drilling working condition data at a plurality of moments before the target moment; determining the hook load and the turntable torque of the target well at the target moment according to the friction coefficient, the first drilling data set and the first prediction model at a plurality of moments before the target moment; the first prediction model is used for predicting the hook load and the turntable torque at the target moment according to the friction resistance coefficients at a plurality of moments before the target moment and the data in the first drilling data set; and determining the stress condition of the tubular column in the target well at the target moment based on the hook load and the rotary table torque at the target moment and the friction coefficient at the moment before the target moment. The memory 43 may be specifically configured to store parameters such as stress conditions of the string in the target well at the target time.

In this embodiment, the input device may be one of the main apparatuses for information exchange between a user and a computer system. The input devices may include a keyboard, mouse, camera, scanner, light pen, handwriting input panel, voice input device, etc.; the input device is used to input raw data and a program for processing the data into the computer. The input device can also acquire and receive data transmitted by other modules, units and devices. The processor may be implemented in any suitable way. For example, the processor may take the form of, for example, a microprocessor or processor and a computer-readable medium that stores computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, an embedded microcontroller, and so forth. The memory may in particular be a memory device used in modern information technology for storing information. The memory may include multiple levels, and in a digital system, memory may be used as long as binary data can be stored; in an integrated circuit, a circuit without a physical form and with a storage function is also called a memory, such as a RAM, a FIFO and the like; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card and the like.

In this embodiment, the functions and effects specifically realized by the electronic device can be explained by comparing with other embodiments, and are not described herein again.

The embodiment of the present specification further provides a computer storage medium of a determination method based on a stress condition of a tubular column, where the computer storage medium stores computer program instructions, and when the computer program instructions are executed, the computer storage medium may implement: acquiring friction coefficients of a target well at a plurality of moments before a target moment; obtaining a first drilling data set of a target well; the first drilling data set comprises drilling design parameter values of a target well and drilling working condition data at a plurality of moments before the target moment; determining the hook load and the turntable torque of the target well at the target moment according to the friction coefficient, the first drilling data set and the first prediction model at a plurality of moments before the target moment; the first prediction model is used for predicting the hook load and the turntable torque at the target moment according to the friction resistance coefficients at a plurality of moments before the target moment and the data in the first drilling data set; and determining the stress condition of the tubular column in the target well at the target moment based on the hook load and the rotary table torque at the target moment and the friction coefficient at the moment before the target moment.

In this embodiment, the storage medium includes, but is not limited to, a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk Drive (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.

In this embodiment, the functions and effects specifically realized by the program instructions stored in the computer storage medium can be explained by comparing with other embodiments, and are not described herein again.

It will be apparent to those skilled in the art that the modules or steps of the embodiments of the present specification described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed over a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different from that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, embodiments of the present description are not limited to any specific combination of hardware and software.

Although the embodiments herein provide method steps as in the embodiments or flowcharts described above, more or fewer steps may be included in a method based on conventional or non-inventive efforts. In the case of steps where no causal relationship is logically necessary, the order of execution of the steps is not limited to that provided by the embodiments of the present description. When the method is executed in an actual device or end product, the method can be executed sequentially or in parallel according to the embodiment or the method shown in the figure (for example, in the environment of a parallel processor or a multi-thread processing).

It is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and many applications other than the examples provided will be apparent to those of skill in the art upon reading the above description. The scope of embodiments of the present specification should, therefore, be determined not with reference to the above description, but should instead be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

The above description is only a preferred embodiment of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure, and it will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the embodiments of the present disclosure should be included in the protection scope of the embodiments of the present disclosure.

Claims

1. A method for determining stress condition of a pipe column is characterized by comprising the following steps:

acquiring friction coefficients of a target well at a plurality of moments before a target moment;

obtaining a first drilling data set of a target well; wherein the first drilling data set comprises drilling design parameter values for the target well and drilling condition data at a plurality of times prior to the target time;

determining hook load and rotary table torque of the target well at a target moment according to friction coefficient, the first drilling data set and a first prediction model at a plurality of moments before the target moment; wherein the first predictive model is configured to predict hook load and rotary table torque at a target time based on friction coefficients at a plurality of times prior to the target time and data in the first drilling data set;

and determining the stress condition of the pipe column in the target well at the target moment based on the hook load and the turntable torque at the target moment and the friction coefficient at the moment before the target moment.

2. The method of claim 1, wherein the well design parameter comprises at least one of: drilling tool combinations, bit types, drilling fluid types;

drilling conditions data at a plurality of times prior to the target time comprises at least one of: the well depth, the well inclination angle, the azimuth angle, the predicted value of the well bottom drilling pressure, the predicted value of the well bottom torque, the hook load, the ground torque, the pump pressure, the rotating disc rotating speed, the pump stroke, the riser pressure, the casing pressure, the inlet density, the outlet density, the inlet temperature, the outlet temperature, the inlet conductance, the outlet conductance, the inlet flow, the outlet flow, the discharge capacity, the equivalent density and the total pool volume at each moment before the target moment.

3. The method of claim 1, further comprising, prior to obtaining the friction coefficient for the target well at a plurality of times before the target time:

obtaining a second drilling data set of a target well, wherein the second drilling data set comprises drilling design parameter values of the target well and drilling condition data at a plurality of moments before the target moment;

determining predicted values of bottom hole drilling pressure and bottom hole torque of the target well at the moment before the target moment according to the second drilling data set and a second prediction model; wherein the second predictive model is configured to predict a bottom hole weight and a bottom hole torque at a previous time from data in a second drilling data set;

acquiring measured values of hook load and rotary table torque of the target well at a moment before the target moment;

and determining the friction coefficient of the target well at the moment before the target moment by utilizing the integral stress model of the pipe column according to the predicted values of the bottom hole drilling pressure and the bottom hole torque, the hook load and the measured value of the turntable torque at the moment before the target moment.

4. The method of claim 3, further comprising, prior to determining the predicted values of bottom hole weight and bottom hole torque for the target well at a time prior to the target time based on the second drilling data set and a second predictive model:

obtaining a drilling data set for at least one neighboring well of the target well;

preprocessing the drilling data set of the at least one adjacent well to obtain a preprocessed drilling data set;

converting the drilling working condition data in the pretreated drilling data set into a supervised learning format according to a preset time step to obtain a first training set; wherein the drilling condition data is time sequence data;

using the well design parameters in the preprocessed well data set as a second training set; wherein the well design parameter is non-time-sequential data;

taking the first training set as input training data of a long-term and short-term memory network in a first target network, and taking the second training set as input training data of a multi-layer feedforward neural network in the target network; wherein the target network is constructed according to the long-short term memory network and the multilayer feedforward neural network;

and training the first target network by utilizing the first training set and the second training set to obtain the second prediction model.

5. The method of claim 1, wherein determining the force condition of the tubular string in the target well at the target time based on the hook load and the rotary table torque at the target time, and the friction coefficient at a time before the target time comprises:

substituting the hook load and the turntable torque at the target moment and the friction coefficient at the previous moment of the target moment into the integral stress model of the tubular column, and iteratively calculating section by section from the wellhead to the drill bit to obtain the stress condition of each section of the tubular column in the target well at the target moment; wherein, the stress condition of each section of pipe column comprises at least one of the following conditions: the pipe column axial force, the torque, the bending moment, the contact force and the buckling state.

6. The method of claim 1, after determining a force condition of the tubular string in the target well at the target time based on the hook load and the rotary table torque at the target time, the friction coefficient at a time prior to the target time, further comprising:

determining whether the stress condition of the pipe column in the target well is abnormal at the target moment;

under the condition that the abnormity is determined, generating prompt information according to the stress condition of the tubular column in the target well at the target moment;

and sending the prompt information to a target processing object.

7. The method of claim 1, further comprising, prior to determining a hook load and a rotary disc torque for the target well at the target time based on a friction coefficient, the first drilling data set, and a first predictive model at a plurality of times prior to the target time:

acquiring a drilling data set and a friction data set of at least one adjacent well of the target well; wherein the friction data set comprises a friction coefficient of the at least one adjacent well at each moment of drilling;

converting the drilling condition data in the preprocessed drilling data set and the friction coefficient in the friction data set into a supervised learning format according to a preset time step length to obtain a third training set; wherein the drilling condition data is time sequence data;

using the well design parameters in the preprocessed well data set as a fourth training set; wherein the well design parameter is non-time-sequential data;

taking the third training set as input training data of a long-term and short-term memory network in a target network, and taking the fourth training set as input training data of a multilayer feedforward neural network in the target network; wherein the target network is constructed according to the long-short term memory network and the multilayer feedforward neural network;

and training the target network by utilizing the third training set and the fourth training set to obtain the first prediction model.

8. A device for determining a force condition of a tubular string, comprising:

the first acquisition module is used for acquiring friction coefficients of the target well at a plurality of moments before the target moment;

a second acquisition module for acquiring a first drilling data set of a target well; wherein the first drilling data set comprises drilling design parameter values for the target well and drilling condition data at a plurality of times prior to the target time;

the first determination module is used for determining the hook load and the rotary table torque of the target well at a target moment according to the friction coefficient, the first drilling data set and the first prediction model at a plurality of moments before the target moment; wherein the first predictive model is configured to predict hook load and rotary table torque at a target time based on friction coefficients at a plurality of times prior to the target time and data in the first drilling data set;

and the second determination module is used for determining the stress condition of the pipe column in the target well at the target moment based on the hook load and the rotary table torque at the target moment and the friction coefficient at the moment before the target moment.

9. A pipe string force condition determining apparatus comprising a processor and a memory for storing processor executable instructions which when executed by the processor implement the steps of the method of any one of claims 1 to 7.

10. A computer-readable storage medium having stored thereon computer instructions which, when executed, implement the steps of the method of any one of claims 1 to 7.