CN115526400A - Drilling operation parameter self-adaptive decision-making method based on feeding damping and mechanical specific energy - Google Patents
Drilling operation parameter self-adaptive decision-making method based on feeding damping and mechanical specific energy Download PDFInfo
- Publication number
- CN115526400A CN115526400A CN202211198991.XA CN202211198991A CN115526400A CN 115526400 A CN115526400 A CN 115526400A CN 202211198991 A CN202211198991 A CN 202211198991A CN 115526400 A CN115526400 A CN 115526400A
- Authority
- CN
- China
- Prior art keywords
- drilling
- specific energy
- mechanical specific
- feeding
- damping
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005553 drilling Methods 0.000 title claims abstract description 130
- 238000013016 damping Methods 0.000 title claims abstract description 98
- 238000000034 method Methods 0.000 title claims abstract description 40
- 230000008859 change Effects 0.000 claims abstract description 37
- 238000001914 filtration Methods 0.000 claims abstract description 28
- 238000005070 sampling Methods 0.000 claims description 36
- 230000003044 adaptive effect Effects 0.000 claims description 20
- 230000015572 biosynthetic process Effects 0.000 claims description 7
- 239000003550 marker Substances 0.000 claims description 4
- 230000035515 penetration Effects 0.000 claims description 4
- 230000001131 transforming effect Effects 0.000 claims 1
- 238000005265 energy consumption Methods 0.000 abstract 1
- 230000008569 process Effects 0.000 description 12
- 238000005457 optimization Methods 0.000 description 9
- 238000005755 formation reaction Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 3
- 239000003245 coal Substances 0.000 description 3
- 239000011435 rock Substances 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000003247 decreasing effect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000011112 process operation Methods 0.000 description 2
- 230000001143 conditioned effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000002893 slag Substances 0.000 description 1
- 239000002002 slurry Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/02—Agriculture; Fishing; Forestry; Mining
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Mining & Mineral Resources (AREA)
- Life Sciences & Earth Sciences (AREA)
- Marine Sciences & Fisheries (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Agronomy & Crop Science (AREA)
- Animal Husbandry (AREA)
- Primary Health Care (AREA)
- Earth Drilling (AREA)
Abstract
The invention discloses a drilling operation parameter self-adaptive decision method based on feeding damping and mechanical specific energy, which comprises the following steps: s1: obtaining feeding pressure, rotating speed, torque and drilling speed as drilling parameters, and performing median filtering by using a sliding window to obtain filtering drilling parameters; s2: calculating a feeding damping coefficient, determining the variation range of the feeding damping coefficient, and converting the feeding damping coefficient into a self-adaptive adjustment index; s3: calculating mechanical specific energy, judging whether the direction of the updated operation parameters needs to be changed or not by combining the feeding damping change rate, and adjusting the parameter updating zone bit; s4: and updating the zone bit according to the self-adaptive adjustment indexes and the parameters, and deciding the change value of the final feeding pressure. The method is characterized in that an operation parameter self-adaptive adjustment strategy is designed based on the magnitude of the feeding damping coefficient, and the mechanical specific energy is used as a key index for evaluating the quality of the operation parameter, so that the optimal operation parameter drilling under different stratum environments is realized; experimental data show that the mechanical specific energy can be effectively reduced by using the method, and low-energy-consumption and high-efficiency drilling is realized.
Description
Technical Field
The invention relates to the technical field of underground coal mine tunnel drilling, in particular to a drilling operation parameter self-adaptive decision method based on feed damping and mechanical specific energy.
Background
The drilling operation parameters refer to important parameters of equipment, tools, mud and operation conditions contained in controllable factors in the process of drilling, and mainly comprise: feed pressure, rotational speed, pump volume, etc. The optimization of the drilling process operation parameters refers to that under a certain condition, according to the law of influence on indexes such as drilling speed, service life of a drill bit, specific energy of the drill bit, drilling accident rate and the like when different parameters are matched, an optimization method is adopted, reasonable time sequence combination of the drilling process operation parameters is selected, and an important research foundation is laid for realizing safe and efficient intelligent control of the drilling process.
The drilling efficiency and safety are determined by various indexes such as drilling speed, the service life of the drill bit, the specific energy of the drill bit, the drilling accident rate and the like. The drilling rate is a key index for determining the drilling efficiency, the mechanical specific energy is a key index for evaluating the drilling state, a high-temperature, high-pressure and high-steep structure and mining disturbed mechanical environment exist in the process of underground construction, the formation drillability is poor, the drilling rate is slow, and the requirement for improving the drilling rate is very urgent. The drilling efficiency and the safety can be improved to a greater extent by prolonging the service life of the drill bit, improving the specific energy of the drill bit, reducing the drilling accident rate and other indexes while improving the drilling speed.
Mechanical Specific Energy (MSE) is a physical model which is established to overcome regional differences and meets the technical performance standard, namely Mechanical Energy required for crushing rock in unit volume per unit time under the action of feed pressure and torque crushing. As the drilling speed is in proportion to the feeding pressure and the rotating speed within a certain range, the mechanical specific energy can be used as the optimization criterion of the operating parameters to better grasp the advantages and disadvantages of the current operating parameters. The mechanical specific energy shows a non-linear change with the change of the operating parameters, for example, when drilling in a single stratum, the mechanical specific energy shows a trend of decreasing first and then increasing with the increase of the feeding pressure, and therefore, the optimal point of the lowest mechanical specific energy is found to realize the goal of optimizing the operating parameters. In the traditional method, the softness and hardness degree of the stratum is not considered in the parameter adjustment process, the operation parameters with the same size are changed in different stratums, and the change of the mechanical specific energy is different. Therefore, the method utilizes the minimum mechanical specific energy as an operation parameter evaluation index on one hand, and introduces the feeding damping as an adjustment factor for optimizing and updating the parameters on the other hand, thereby realizing the optimal self-adaptive drilling under different stratum environments. The method provides an idea for the self-adaptive decision of the drilling operation parameters of the underground coal mine tunnel, avoids the current situation of excessively depending on manual experience, and has practical significance for improving the safety and efficiency of the underground tunnel drilling.
Disclosure of Invention
The invention discloses a drilling operation parameter self-adaptive decision-making method based on feed damping and mechanical specific energy, and aims to solve the problem that in the prior art, the parameter optimization mostly does not consider stratum lithology and excessively depends on artificial experience, and the optimal drilling is realized by utilizing a mechanical specific energy theory.
In order to solve the above problems, the present invention adopts a technical solution comprising:
a drilling operation parameter adaptive decision-making method based on feed damping and mechanical specific energy comprises the following steps:
s1, obtaining feeding pressure, rotating speed, torque and drilling speed as drilling parameters: performing median filtering by using a sliding window to obtain filtering drilling parameters;
s2, calculating a feeding damping coefficient: substituting the filtering drilling parameters into a feeding damping coefficient calculation formula, calculating a key index reflecting the current stratum drilling difficulty degree, and carrying out difference on the current feeding damping coefficient and the feeding damping coefficient at the last moment to obtain a feeding damping change rate; determining the variation range of the feeding damping coefficient, and converting the feeding damping coefficient into a self-adaptive adjustment index;
s3, calculating mechanical specific energy: the filtering drilling parameters are substituted into a mechanical specific energy calculation formula, a key index reflecting the quality of the current drilling state is calculated, and the minimum value of the mechanical specific energy in the sliding window is recorded; comparing the current mechanical specific energy with the minimum mechanical specific energy in the sliding window, judging whether the direction of the updated operation parameter needs to be changed or not by combining the feeding damping change rate, and adjusting the parameter updating zone bit;
and S4, updating the zone bit according to the self-adaptive adjustment indexes and the parameters, and deciding the change value of the final feeding pressure.
Optionally, the obtaining of the filtering drilling parameter by performing median filtering with a sliding window includes:
Data f (k)＝Φ[Data(k),Data(k-1),…,Data(k-n+1)]；
wherein Data represents feed pressure, rotational speed, rate of penetration, or torque; data f (k) Filtered data representing a kth sample time; data (k) represents the raw Data at the kth sampling time; phi []Representing that the median filtering is carried out on the sampled data represented by the square, and the median of the sequence is taken; n represents the size of the sliding window.
Optionally, the calculating may reflect a key indicator of the current drilling difficulty of the formation, including:
in the formula: k is a radical of formula r (k) The feeding damping coefficient size representing the kth sampling time; WOB f (i) A filtered feed pressure magnitude representing an ith sample time; ROP f (i) A filtered drilling rate magnitude representing an ith sample time; n represents the size of the sliding window.
Optionally, the feed damping change rate is expressed as:
in the formula: δ represents the feed damping rate of change for the kth time; k is a radical of r (k) The feeding damping coefficient value representing the kth sampling time; k is a radical of r (k-1) represents the magnitude of the feed damping coefficient at the k-1 th sampling time; | represents taking the absolute value.
Optionally, the determining a variation range of the feeding damping coefficient and converting the feeding damping coefficient into a self-adaptive adjustment index include:
in the formula: theta represents a self-adaptive adjustment index; k is a radical of rmax And k rmin The upper and lower limits of the set feeding damping coefficient are set.
Alternatively, the mechanical specific energy can be calculated as follows:
in the formula: MSE (k) represents the mechanical specific energy, WOB, at the kth sampling time f (k) A filtered feed pressure magnitude representing a kth sampling time; RPM f (k) Representing the filtering rotating speed of the k sampling time; TOR f (k) A magnitude of the filtering torque representing a kth sampling time; ROP f (k) Representing the filtered drilling rate magnitude at the kth sample time.
Alternatively, the minimum mechanical specific energy within the sliding window can be expressed as follows:
MSE min (k)＝MIN[MSE(k),MSE(k-1),…,MSE(k-n+1)]；
in the formula: MSE min (k) Represents the minimum mechanical specific energy at the kth sampling time; MIN (-) denotes taking the minimum value for the sample data.
Optionally, the S3 specifically includes:
s31, judging whether the drilling can be started or not in the working procedure and the working condition;
s32, judging whether the drilling speed exceeds the maximum drilling speed upper limit, and reducing the feeding pressure if the drilling speed exceeds the maximum drilling speed upper limit;
s33, judging whether the torque exceeds the maximum torque upper limit, and reducing feeding pressure if the torque exceeds the maximum torque upper limit;
s34, if the upper limit is not exceeded in S32 and S33, adjusting the feeding pressure according to the self-adaptive adjustment indexes and the parameter updating zone bits;
s35, judging whether the parameter updating zone bit needs to be updated or not according to the feeding damping change rate and the mechanical specific energy;
and S36, repeating the steps to realize self-adaptive drilling.
Optionally, the feeding damping flag bit adjustment is based on the comparison between the current mechanical specific energy and the minimum mechanical specific energy;
if the current mechanical specific energy is larger than 1.1 times of the minimum mechanical specific energy in one adjustment and the change rate of the feeding damping is smaller than 0.2, changing the parameter updating flag bit, namely changing the positive direction into the negative direction or changing the negative direction into the positive direction;
and if the change rate of the feeding damping is more than 0.2, the zone bit is not changed.
Optionally, adjusting the feeding pressure comprises:
WOB d (k)＝WOB d (k-1)+ΔWOB d (k)；
ΔWOB d (k)＝SIGN·θ·ΔWOB；
in the formula: WOB d (k) A desired feed pressure representative of a kth sampling time; WOB d (k-1) a desired feed pressure representing a k-1 sample time; delta WOB d (k) A desired feed pressure increase representative of a kth sampling time; SIGN represents a marker bit, and the bit is-1 or 1; theta represents a self-adaptive adjustment index; Δ WOB represents the base increment size, being a positive constant.
The invention has the following beneficial effects:
the method can effectively realize that whether the updating direction of the current parameter is correct or not by utilizing the specific energy theory of the drilling parameter and the machinery, thereby guiding the setting of the operation parameter, automatically searching the optimal parameter, ensuring the safety and the efficiency of the drilling process and having practicability and applicability.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of the intelligent adaptive decision making process for drilling operation parameters based on feed damping and mechanical specific energy of the present invention;
FIG. 2 is a block diagram of the present invention for an intelligent adaptive decision making for drilling operation parameters based on feed damping and mechanical specific energy;
FIG. 3 is a flow diagram of an intelligent adaptive decision-making routine for drilling operating parameters based on feed damping and mechanical specific energy in accordance with the present invention;
FIG. 4 is experimental feed pressure data of the present invention;
FIG. 5 is experimental torque data for the present invention;
FIG. 6 is experimental rate of penetration data for the present invention;
FIG. 7 is experimental rotational speed data of the present invention;
FIG. 8 is mechanical specific energy data for the present invention;
figure 9 is feed damping data for the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be further described with reference to the accompanying drawings.
The overall scheme of the invention is as follows: firstly, acquiring key data in a drilling process in real time, and carrying out median filtering on the data; calculating feed damping and change rate based on the obtained data; calculating a self-adaptive adjustment index according to the set upper and lower limits of the feeding damping; calculating the mechanical specific energy and the minimum mechanical specific energy in the sliding window based on the obtained data; judging the updating direction of the parameters according to the feeding damping change rate and the mechanical specific energy; and adjusting the operation parameters by combining the self-adaptive adjustment indexes and the parameter updating direction.
The invention discloses a drilling operation parameter self-adaptive decision-making method based on feed damping and mechanical specific energy, which comprises the following steps of:
firstly, collecting key parameters of a drilling process: feeding pressure (N), rotating speed (rpm), torque (Nm) and drilling speed (m/min), selecting a proper sliding window size according to the time scale and the variation trend of actual data, performing median filtering on the data, and taking a median in the window so as to remove abnormally changed data.
Establishing a drilling process feeding damping model, substituting the filtering data into the model to obtain a current feeding damping, and carrying out difference on the current feeding damping and the feeding damping at the last moment to obtain a feeding damping change rate;
converting the current feeding damping into a self-adaptive adjustment index according to the upper and lower limits of the set feeding damping;
constructing a mechanical specific energy model, substituting the filtering data into the model to obtain the current mechanical specific energy, and simultaneously solving the minimum value of the mechanical specific energy in a sliding window;
judging whether the parameter updating direction needs to be changed or not according to the relative error between the current mechanical specific energy and the minimum mechanical specific energy and by combining the size of the variation rate of the feeding damping, and updating the parameter updating zone bit;
and updating the zone bit according to the self-adaptive adjustment index and the parameter, and adjusting the operation parameter. If the rate of penetration and torque exceed the upper limits, the operating parameters are preferentially reduced.
Referring to fig. 1, fig. 1 is a flow chart of the intelligent adaptive decision making of drilling operation parameters based on feeding damping and mechanical specific energy of the present invention, and the present invention specifically comprises the following steps:
s1, obtaining feeding pressure, rotating speed, torque and drilling speed as drilling parameters: preprocessing data, and performing median filtering on drilling data;
the drilling parameters mainly include operation parameters and state parameters, wherein the core parameters include feed pressure (N), rotation speed (rpm), torque (Nm), and drilling rate (m/min). Due to the fact that the actual underground working condition is complex, factors such as strong disturbance and measurement noise exist, data abnormity inevitably occurs, and therefore judgment of the drilling state is affected. Therefore, the data are subjected to median filtering to remove the drilling parameters with abnormal changes.
The filtered drilling parameters may be expressed as:
Data f (k)＝Φ[Data(k),Data(k-1),…,Data(k-n+1)]；
wherein Data represents four key parameters of feed pressure (WOB), rotating speed (RPM), torque (TOR) and drilling Rate (ROP); data f (k) Filtered data representing a kth sample time; data (k) represents the raw Data at the kth sampling time; phi [. To]Representing that the median filtering is carried out on the sampled data represented by the square, and the median of the sequence is taken; n represents the size of the sliding window. The selection of the size of the sliding window mainly depends on the time scale of core parameter change, and as underground roadway drilling rigs are mostly all-hydraulic drilling rigs and can quickly track operation instructions, n is generally 10. In the case of a sampling time of 1 second, the size of the sliding window is 10 seconds.
S2, calculating a feeding damping coefficient:
referring to fig. 2, the parameters required for calculating the feed damping coefficient are the filter feed pressure and the filter drilling rate. The feeding damping is an index reflecting the current difficulty degree of feeding, namely the feeding pressure required by increasing the unit drilling speed. It is expressed as:
in the formula:
k r (k) The feeding damping coefficient value representing the kth sampling time; WOB f (i) A filtered feed pressure magnitude representing an ith sample time; ROP f (i) The magnitude of the filter drilling rate representing the ith sampling time; n represents the size of the sliding window.
And similarly, a sliding window is adopted to avoid abnormal values, and n is 10. In the case of a sampling time of 1 second, the size of the sliding window is 10 seconds.
The rate of change of the feed damping can be expressed as:
in the formula: δ represents the kth samplingRate of change of feed damping with time; k is a radical of r (k) The feeding damping coefficient size representing the kth sampling time; k is a radical of r (k-1) represents the magnitude of the feed damping coefficient at the k-1 th sampling time; | · | represents taking the absolute value.
And calculating a self-adaptive adjustment index based on the feeding damping by combining the upper and lower bounds of the feeding damping. The adaptive adjustment index determines the size of the single parameter update value. Due to different lithologies, the feeding damping is different, namely the feeding pressure required for increasing the unit drilling speed is different. Therefore, the shortest adjusting times can be used to optimize the mechanical specific energy by combining the self-adaptive adjusting indexes. The adaptive adjustment index may be expressed as:
in the formula: theta represents a self-adaptive adjustment index; k is a radical of r (k) Damping said feed; k is a radical of rmax And k is rmin The upper and lower bounds of the feed damping are set. The adaptive adjustment index is designed to be a continuous value between 1 and 5, i.e., continuous gradation. The size of the operation parameter single updating value can be continuously adjusted between 1 to 5 levels according to the self-adaptive index. Furthermore, k rmax And k is rmin The determination may be made from a priori knowledge of the drilling in the different formations.
S3, calculating a mechanical specific energy index:
mechanical Specific Energy (MSE) is a physical model which is established to overcome regional differences and meets the technical performance standard, namely Mechanical Energy required for crushing rock in unit volume per unit time under the action of feed pressure and torque crushing. Since the drilling speed is in proportion to the rotating speed and the feeding pressure within a certain range, the mechanical specific energy can be used as the optimization criterion of the operation parameters to better grasp the quality of the current operation parameters. The mechanical specific energy may exhibit a non-linear change in operating parameters, such as a decrease in mechanical specific energy followed by an increase in feed pressure while drilling in a single formation. The optimum point of lowest specific energy, i.e. the goal of operating parameter optimization, is found. Referring to fig. 2, the mechanical specific energy can be calculated for all 4 drilling core parameters, which are expressed as follows:
in the formula: MSE (k) represents the mechanical specific energy at the kth sampling time; WOB f (k) A filtered feed pressure magnitude representing a kth sampling time; RPM f (k) Representing the filtering rotating speed of the k sampling time; TOR f (k) The filtering torque magnitude representing the k sampling time; ROP f (k) Representing the filtered drilling rate magnitude at the kth sample time.
The minimum mechanical specific energy within the sliding window can be expressed as:
MSE min (k)＝MIN[MSE(k),MSE(k-1),…,MSE(k-n+1)]；
in the formula: MSE min (k) Represents the minimum mechanical specific energy at the kth sampling time; MIN [. C]Indicating that the pair represents taking the minimum value to the sampled data. And n is 10. In the case of a sampling time of 1 second, the size of the sliding window is 10 seconds.
S4, updating the zone bit according to the self-adaptive adjustment indexes and the parameters, and deciding the change value of the final feeding pressure:
s4.1, calculating a parameter updating zone bit:
the parameter update flag bit represents the direction of the parameter change at the next operation parameter update time, i.e. increase or decrease. The trend of the change of the mechanical specific energy reflects whether the adjustment of the current operating parameters is carried out towards the optimal parameter combination, if the mechanical specific energy shows a continuously decreasing trend, the current parameter optimization direction is correct, otherwise, the current parameter optimization direction is wrong. Referring to fig. 3, if the current mechanical specific energy is greater than 1.1 times the minimum mechanical specific energy and the feed damping change rate is less than 0.2 in one adjustment, the parameter update flag is changed, i.e., positive to negative or negative to positive. The rate of change of feed damping is conditioned to overcome the effect of formation changes on mechanical specific energy. If the change rate of the feeding damping is larger than 0.2, the zone bit is not changed, and the change of the mechanical specific energy is regarded as the formation influence rather than the direction factor of parameter adjustment.
The rule for adjusting the flag bit is specifically expressed as follows:
IF:MSE(k)>1.1MSE min (k) And delta<0.2; THEN: making a change in the flag (SIGN: -1 → 1 or-1 → 1). Wherein SIGN represents a marker bit.
S4.2, operating parameter decision:
referring to fig. 3, fig. 3 is a flow diagram of an intelligent adaptive decision-making routine for drilling operating parameters based on feed damping and mechanical specific energy of the present invention for determining a final operating parameter decision.
In the actual drilling process, the mechanical specific energy is taken as an evaluation index set by the operation parameters, and constraints of working procedures, working conditions and state parameters exist. If the drilling machine is in the process of additionally connecting a drill rod and the like, the feeding pressure and the rotating speed are not sent down by the self-adaptive system; similarly, when the drilling tool is in abnormal underground working conditions such as drilling jamming and drilling burying, the operation parameters cannot be issued at will.
The constraints on the state parameters mainly include the constraints on drilling rate and torque:
determining the upper limit of the maximum drilling speed according to parameters such as the selected drill bit, the slurry discharge capacity and the like, and avoiding blockage of a drill hole due to unsmooth slag discharge;
according to the factors such as rated power of the drilling machine, the size of a drill rod and the like, the maximum upper limit of the torque is determined, and the long-time full-load work is avoided, so that the service life of the drilling machine is not influenced.
When the drilling speed and the torque exceed the maximum amplitude limit, the operation parameter is directly reduced.
And when the drilling speed and the torque are within the maximum amplitude limit, updating the zone bit according to the self-adaptive adjustment index and the parameter, and adjusting the operation parameter.
The update of the operating parameter feed pressure is as follows:
WOB d (k)＝WOB d (k-1)+ΔWOB d (k)；
ΔWOB d (k)＝SIGN·θ·ΔWOB；
in the formula: WOB d (k) A desired feed pressure representative of a kth sampling time; WOB d (k-1) a desired feed pressure representing a k-1 sample time; delta WOB d (k) Represents the firstA desired feed pressure increment of k sample times; SIGN represents a marker bit, and the bit is-1 or 1; theta represents a self-adaptive adjustment index; Δ WOB represents a base increment size, a positive constant;
by the steps S1 to S4, the operation parameters can be optimized in real time aiming at the underground tunnel drilling machine of the coal mine, and the optimal drilling is realized. Based on the program flow diagram in fig. 3, an operation parameter optimization system is established, which is applied to a laboratory micro-drilling machine, and relevant experimental data are shown in fig. 4 to 7, which are respectively feed pressure (N), rotation speed (rpm), torque (Nm), and drilling rate (mm/s). Under system action, the feed pressure continues to rise, while the mechanical specific energy continues to fall, as shown in fig. 8, achieving a continuous approximation to the optimum point. However, since the actual drilling rock sample is not changed, the feeding damping is rapidly reduced in the initial stage (0-100 seconds), and the subsequent feeding damping does not show large change, as shown in fig. 9.
The invention has the following beneficial effects after the concrete implementation: the method can effectively realize that whether the updating direction of the current parameters is correct or not by utilizing the drilling parameter and mechanical specific energy theory, thereby guiding the setting of the operation parameters, automatically searching for the optimal parameters, ensuring the safety and efficiency of the drilling process and having practicability and applicability.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A drilling operation parameter adaptive decision method based on feed damping and mechanical specific energy is characterized by comprising the following steps:
s1, obtaining feeding pressure, rotating speed, torque and drilling speed as drilling parameters: performing median filtering by using a sliding window to obtain filtering drilling parameters;
s2, calculating a feeding damping coefficient: substituting the filtering drilling parameters into a feeding damping coefficient calculation formula, calculating a key index reflecting the current stratum drilling difficulty degree, and carrying out difference on the current feeding damping coefficient and the feeding damping coefficient at the last moment to obtain a feeding damping change rate; determining the variation range of the feeding damping coefficient, and converting the feeding damping coefficient into a self-adaptive adjustment index;
s3, calculating mechanical specific energy: the filtering drilling parameters are substituted into a mechanical specific energy calculation formula, a key index reflecting the quality of the current drilling state is calculated, and the minimum value of the mechanical specific energy in the sliding window is recorded; comparing the current mechanical specific energy with the minimum mechanical specific energy in the sliding window, judging whether the direction of the updated operation parameter needs to be changed or not by combining the feeding damping change rate, and adjusting the parameter updating zone bit;
and S4, updating the zone bit according to the self-adaptive adjustment indexes and the parameters, and deciding the change value of the final feeding pressure.
2. The feed-damping and mechanical specific energy based drilling operation parameter adaptive decision method as claimed in claim 1, wherein said median filtering with a sliding window to obtain filtered drilling parameters comprises:
Data f (k)＝Φ[Data(k),Data(k-1),…,Data(k-n+1)]；
wherein Data represents feed pressure, rotational speed, torque, and rate of penetration; data f (k) Filtered data representing a kth sample time; data (k) represents the raw Data at the kth sampling time; phi [. To]Representing that the median filtering is carried out on the sampled data represented by the square, and the median of the sequence is taken; n represents the size of the sliding window.
3. The adaptive decision-making method for drilling operation parameters based on feeding damping and mechanical specific energy as claimed in claim 1 or 2, characterized in that the calculation reflects key indexes of current formation drilling difficulty degree, and comprises:
in the formula: k is a radical of formula r (k) The feeding damping coefficient size representing the kth sampling time; WOB f (i) A filtered feed pressure magnitude representing an ith sample time; ROP f (i) A filtered drilling rate magnitude representing an ith sample time; n represents the size of the sliding window.
4. A method for adaptive decision making of drilling operation parameters based on feed damping and mechanical specific energy according to claim 1 or 2, characterized in that the feed damping change rate is expressed as:
in the formula: δ represents the feed damping rate of change for the kth time; k is a radical of formula r (k) The feeding damping coefficient size representing the kth sampling time; k is a radical of r (k-1) represents the magnitude of the feed damping coefficient at the k-1 th sampling time; | represents taking the absolute value.
5. The feed damping and mechanical specific energy based drilling operation parameter adaptive decision method as claimed in claim 1 or 2, characterized in that said determining the variation range of the feed damping coefficient, transforming the feed damping coefficient into an adaptive adjustment index, comprises:
in the formula: theta represents a self-adaptive adjustment index; k is a radical of rmax And k is rmin The upper and lower limits of the set feeding damping coefficient are set.
6. The adaptive decision method for drilling operation parameters based on feed damping and mechanical specific energy according to claim 1 or 2, characterized in that the mechanical specific energy is calculated as follows:
in the formula: MSE (k) represents the mechanical specific energy, WOB, at the kth sampling time f (k) A filtered feed pressure magnitude representing a kth sampling time; RPM f (k) Representing the filtering rotating speed of the k sampling time; TOR f (k) A magnitude of the filtering torque representing a kth sampling time; ROP f (k) Representing the filtered drilling rate magnitude at the kth sample time.
7. The adaptive decision making method for drilling operating parameters based on feed damping and mechanical specific energy as claimed in claim 6, characterized in that the minimum mechanical specific energy within the sliding window is expressed as follows:
MSE min (k)＝MIN[MSE(k),MSE(k-1),…,MSE(k-n+1)]；
in the formula: MSE min (k) Represents the minimum mechanical specific energy at the kth sampling time; MIN [. C]Indicating that the pair represents taking the minimum value to the sampled data.
8. The adaptive decision method for drilling operation parameters based on feed damping and mechanical specific energy according to claim 1 or 2, characterized in that said S3 comprises in particular:
s31, judging whether the drilling can be started or not in the working procedure and the working condition;
s32, judging whether the drilling speed exceeds the maximum drilling speed upper limit, and reducing the feeding pressure if the drilling speed exceeds the maximum drilling speed upper limit;
s33, judging whether the torque exceeds the maximum torque upper limit, and reducing feeding pressure if the torque exceeds the maximum torque upper limit;
s34, if the upper limit is not exceeded in S32 and S33, adjusting the feeding pressure according to the self-adaptive adjustment index and the parameter updating zone bit;
s35, judging whether the parameter updating zone bit needs to be updated or not according to the feeding damping change rate and the mechanical specific energy;
and S36, repeating the steps to realize self-adaptive drilling.
9. The adaptive decision-making method for drilling operation parameters based on feed damping and mechanical specific energy according to claim 8, characterized in that the feed damping flag bit adjustment is based on a comparison of the current mechanical specific energy and the minimum mechanical specific energy;
if the current mechanical specific energy is larger than 1.1 times of the minimum mechanical specific energy in one adjustment and the change rate of the feeding damping is smaller than 0.2, changing the parameter updating flag bit, namely changing the positive direction into the negative direction or changing the negative direction into the positive direction;
and if the change rate of the feeding damping is more than 0.2, the zone bit is not changed.
10. The feed damping and mechanical specific energy based drilling operation parameter adaptive decision method of claim 8, wherein adjusting the feed pressure comprises:
WOB d (k)＝WOB d (k-1)+ΔWOB d (k)；
ΔWOB d (k)＝SIGN·θ·ΔWOB；
in the formula: WOB d (k) A desired feed pressure representative of a kth sampling time; WOB d (k-1) a desired feed pressure representing a k-1 sample time; delta WOB d (k) A desired feed pressure increase representative of a kth sampling time; SIGN represents a marker bit, and the bit is-1 or 1; theta represents a self-adaptive adjustment index; Δ WOB represents the base increment size, being a positive constant.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211198991.XA CN115526400A (en) | 2022-09-29 | 2022-09-29 | Drilling operation parameter self-adaptive decision-making method based on feeding damping and mechanical specific energy |
| PCT/CN2023/120556 WO2024067376A1 (en) | 2022-09-29 | 2023-09-22 | Drilling-operation-parameter adaptive decision making method based on feeding damping and mechanical specific energy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211198991.XA CN115526400A (en) | 2022-09-29 | 2022-09-29 | Drilling operation parameter self-adaptive decision-making method based on feeding damping and mechanical specific energy |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115526400A true CN115526400A (en) | 2022-12-27 |
Family
ID=84699777
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211198991.XA Pending CN115526400A (en) | 2022-09-29 | 2022-09-29 | Drilling operation parameter self-adaptive decision-making method based on feeding damping and mechanical specific energy |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN115526400A (en) |
| WO (1) | WO2024067376A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024067376A1 (en) * | 2022-09-29 | 2024-04-04 | 中煤科工西安研究院(集团)有限公司 | Drilling-operation-parameter adaptive decision making method based on feeding damping and mechanical specific energy |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2886243C (en) * | 2012-10-03 | 2020-09-08 | Shell Internationale Research Maatschappij B.V. | Optimizing performance of a drilling assembly |
| CN106640035A (en) * | 2016-12-19 | 2017-05-10 | 四川宏华电气有限责任公司 | VFD control system and method for automatic optimization of drilling parameters |
| US10900343B1 (en) * | 2018-01-25 | 2021-01-26 | National Technology & Engineering Solutions Of Sandia, Llc | Control systems and methods to enable autonomous drilling |
| CN114215499B (en) * | 2021-11-15 | 2024-01-26 | 西安石油大学 | Well drilling parameter optimization method based on intelligent algorithm |
| CN115526400A (en) * | 2022-09-29 | 2022-12-27 | 中煤科工西安研究院(集团)有限公司 | Drilling operation parameter self-adaptive decision-making method based on feeding damping and mechanical specific energy |
-
2022
- 2022-09-29 CN CN202211198991.XA patent/CN115526400A/en active Pending
-
2023
- 2023-09-22 WO PCT/CN2023/120556 patent/WO2024067376A1/en unknown
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024067376A1 (en) * | 2022-09-29 | 2024-04-04 | 中煤科工西安研究院(集团)有限公司 | Drilling-operation-parameter adaptive decision making method based on feeding damping and mechanical specific energy |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2024067376A1 (en) | 2024-04-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106761480A (en) | A kind of underground moment of torsion self-balancing has cable drilling system | |
| CN104453841B (en) | Drilling energy-saving acceleration navigation optimizing method | |
| CN115526400A (en) | Drilling operation parameter self-adaptive decision-making method based on feeding damping and mechanical specific energy | |
| CN115387777B (en) | Feeding and rotation control method of hydraulic tunnel drilling machine based on coal rock sensing | |
| CN104695937A (en) | Well drilling comprehensive speed accelerating optimization expert system | |
| JP2000507659A (en) | How to adjust drilling conditions applied to well bits | |
| CN106640035A (en) | VFD control system and method for automatic optimization of drilling parameters | |
| CN111364966B (en) | Constant-pressure-difference automatic bit feeding control method for petroleum drilling | |
| CN114427430B (en) | Multi-well real-time collaborative drilling parameter optimization method and system | |
| CN113378344A (en) | Method and system for optimizing drilling tripping speed threshold | |
| CN112031749A (en) | Comprehensive performance evaluation method of drill bit for oil and gas drilling | |
| CN206448779U (en) | A kind of underground moment of torsion self-balancing has cable drilling system | |
| CN108825204B (en) | Drilling time correction method based on engineering parameters while drilling | |
| CN109779601B (en) | Adaptive decoding method for downlink instruction of rotary steering drilling system | |
| CN113935546A (en) | Slurry water balance type shield tunneling parameter optimization method based on RF-PSO | |
| CN116151101A (en) | Horizontal well drilling parameter optimization chart establishing method | |
| CN116084910A (en) | Method for predicting guiding instruction of pushing-type rotary guiding tool in real time | |
| CN114021358A (en) | Horizontal well drilling parameter optimization method | |
| CN112554861B (en) | Design method of anti-fracturing interference obstacle-detouring track | |
| CN112632758A (en) | Fine management method for friction coefficient of single well of oil and gas well | |
| CN113255080A (en) | Composite drill bit optimization method based on fine pressure control drilling technology | |
| CN111827964A (en) | Drill bit working condition judgment method while drilling based on comprehensive logging parameters | |
| CN114017010B (en) | Multi-parameter self-adaptive transmission method for underground drilling | |
| CN116974312B (en) | Method for controlling pressure of drilling and production shaft of natural gas hydrate | |
| CN113591315B (en) | Drilling rock breaking mode and matching parameter optimization method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |