CN115726760A - Autonomous decision-making method and system for realizing intelligent control of drilling - Google Patents

Abstract

The invention provides an autonomous decision-making method and system for realizing intelligent control of drilling, wherein the method determines the comprehensive intelligent capability of each intelligent point according to set influence factors for intelligent degree evaluation by judging the intelligent scene and the intelligent point to which the current drilling work belongs; then, the intelligent level of the current drilling intelligent point is determined by combining the calculated comprehensive intelligent capacity and the capacity threshold value corresponding to the intelligent point; and finally, integrating the intelligent grade and the parameter content in the intelligent evaluation step, and deciding the optimal intelligent control measure of the current drilling intelligent point. By adopting the scheme, the maturity quantitative evaluation matrix is established for each intelligent point in different drilling scenes, the optimal intelligent control strategy suitable for each intelligent point can be effectively decided according to the evaluation result, the intelligent control advantage is exerted to the maximum extent, meanwhile, the low control quality caused by blind control is avoided, and the optimal control considering the drilling timeliness and stability is realized.

Description

Autonomous decision-making method and system for realizing intelligent control of drilling

Technical Field

The invention relates to the technical field of oil and gas well drilling and optimization, in particular to an autonomous decision-making method and system for realizing intelligent control of drilling.

Background

The realization of complete well drilling is a complex system engineering, multiple tasks such as track control, efficiency optimization, risk control and the like must be solved simultaneously, the tasks have correlation and mutual influence relationship, each task must be realized by combining a plurality of branch decision control points, and ideally, each decision point automatically sends an operation instruction to well drilling equipment through decision, so that full closed-loop or local closed-loop automatic control well drilling is realized. However, only part of the analysis results can be reliably used for automatic control, and the points can be used for automatic closed-loop control, which is unknown in the existing research.

The key change of intelligent drilling is to reduce human intervention as much as possible in the drilling process until the human intervention is completely separated, so as to achieve the effect of full-automatic closed-loop control drilling. The final objective of intelligent drilling is to dynamically optimize the drilling scheme by processing and analyzing various data in the drilling process, output various instructions according to the optimal scheme, control the operation of drilling equipment, and form a large technical system, wherein the intelligent drilling closed-loop technical system is shown in figure 1, so as to achieve the optimal drilling efficiency, speed and quality. In the prior art, the structure or communication logic of a closed-loop control system is explained, and reliable guidance of intelligent control decision is not provided; however, drilling is a very complicated system engineering, and the ultimate ideal target cannot be achieved within a certain period of time, especially for a complicated and difficult well, full-automatic closed-loop drilling cannot be completely achieved, that is, even if the used equipment has the capacity of intelligent automatic operation according to the control instruction, the quality of the control instruction cannot be guaranteed, and effective and complete intelligent control cannot be achieved. Therefore, in the large closed-loop technology system, the autonomous decision-making plays a decisive role in the success or failure of the drilling, and once the decision-making error puts the non-optimal scheme (even the worst scheme) into practice, the decision-making error may cause complete deviation from the drilling purpose and even cause a serious accident.

The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

Disclosure of Invention

In order to solve the problems, the invention provides an autonomous decision-making method and system for realizing intelligent control drilling, which can effectively identify which intelligent points are suitable for adopting complete intelligent control by establishing a maturity quantitative evaluation matrix for each intelligent point in different drilling scenes, and can be used for deciding the optimal control strategy for the intelligent drilling points which are not suitable for the complete intelligent control. In one embodiment, the method comprises:

a construction identification step, namely judging an intelligent scene and an intelligent point to which the current drilling work belongs;

the intelligent evaluation step includes calculating comprehensive intelligent capacity of the current drilling intelligent point by considering different construction factors influencing intelligent degree of the intelligent point so as to represent real intelligent degree of each intelligent point;

a grade evaluation and analysis step, namely determining the intelligent grade of the current drilling intelligent point by combining the comprehensive intelligent capability obtained by calculation and a capability threshold value corresponding to the intelligent point;

and controlling a decision step, integrating the determined intelligent grade and evaluation parameters and data contents in the intelligent evaluation step, and deciding the optimal intelligent control measure of the current drilling intelligent point.

In a preferred embodiment, the method further comprises:

and a scene division step, namely dividing the drilling process into a plurality of intelligent scenes and intelligent points in advance according to the well zone stratum data and the drilling requirement, wherein the control logic of each intelligent point can be independently described and implemented.

Further, in one embodiment, the technology maturity level of each intelligent point is set according to different intelligent control forms in the drilling engineering, the intelligent control forms comprise intelligent description, intelligent diagnosis, intelligent prediction, intelligent consultation optimization and intelligent control optimization, and the requirement on the intelligent degree is gradually increased.

Specifically, in one embodiment, in the intelligent evaluation step, different construction factors affecting the intelligent degree of the intelligent points include data maturity, model maturity and equipment maturity, and in the intelligent evaluation step, a plurality of corresponding evaluation indexes are set for each intelligent point based on the construction factors.

In an optional embodiment, the comprehensive intelligent capacity a of each intelligent point is determined according to the evaluation indexes according to the following operations:

A＝k*MIN(D,M,E)+(1-k)*AVERAGE(D,M,E)

in the formula, k represents a dominant factor constant of the current intelligent point, D represents the data maturity of the current intelligent point, M represents the model maturity of the current intelligent point, and E represents the equipment maturity of the current intelligent point.

Further, in one embodiment, in the level evaluation step, the comprehensive intelligent capability value of the intelligent point is compared with the capability threshold values corresponding to the intelligent description, the intelligent diagnosis, the intelligent prediction level, the intelligent consultation optimization and the intelligent control optimization levels, and if the comprehensive intelligent capability value of the Lx level corresponding to the intelligent point exceeds L, the comprehensive intelligent capability value of the Lx level corresponding to the intelligent point exceeds L_X And setting the intelligent mark of the intelligent point at the level as a set value to indicate that the construction of the intelligent point at the corresponding level can be automatically executed by a software system.

Optionally, in an embodiment, the capability threshold is set for each intelligent level of each intelligent point according to the requirement of the construction task of each intelligent point for intellectualization, and the higher the requirement for drilling safety, the higher the capability threshold is.

Specifically, in one embodiment, in the intelligent evaluation step, an evaluation index is set for each data maturity, model maturity and equipment maturity;

wherein, the evaluation of the maturity of the single data comprises the following steps: accurate satisfaction index a_d And the aging satisfies the index t_d ；

The maturity d of the individual data was determined according to the following formula_n ：

d_n ＝k*MIN(a_d ,t_d )+(1-k)*AVERAGE(a_d ,t_d )

Calculating the integral data maturity D of the corresponding intelligent point according to the following formula:

D＝MIN(d₁ ,d₂ ,…,d_n )；

in the formula, n is the number of parameters related to the current intelligent point, and k is a constant of a dominant factor.

The present invention also provides a storage medium having stored thereon program code that implements the method described in any one or more of the above embodiments, based on the method described in any one or more of the above embodiments.

Based on further aspects of the method described in any one or more of the above embodiments, the invention also provides an autonomous decision-making system for implementing intelligently controlled drilling, the system being configured to perform the method described in any one or more of the above embodiments, comprising:

the construction identification module is configured to judge an intelligent scene and an intelligent point to which the current drilling work belongs according to a pre-constructed construction division data table;

the intelligent evaluation module is configured to calculate the comprehensive intelligent capacity of the current drilling intelligent point by considering different construction factors influencing the intelligent degree of the intelligent point so as to represent the real intelligent degree of each intelligent point;

the grade evaluation module is configured to determine the intelligent grade of the current drilling intelligent point by combining the comprehensive intelligent capacity obtained by calculation and the capacity threshold value corresponding to the intelligent point;

and the control decision module is configured to integrate the determined intelligent grade and evaluation parameters and data content in the intelligent evaluation process and decide the optimal intelligent control measure of the current drilling intelligent point.

Compared with the closest prior art, the invention also has the following beneficial effects:

the invention provides an autonomous decision-making method and system for realizing intelligent control of drilling, which can be used for carrying out intelligent degree evaluation on each intelligent point by judging the intelligent scene and the intelligent point to which the current drilling work belongs so as to determine the comprehensive intelligent capability of each intelligent point; the intelligent degree evaluation system is suitable for drilling construction of oil and gas wells in different areas and different types, can comprehensively cover each construction task point in the drilling process, carries out targeted intelligent degree evaluation operation aiming at the intelligent point of each construction task, and can provide data support for reliably carrying out intelligent capacity grading;

in addition, the intelligent grade of the current drilling intelligent point is determined by combining the capacity threshold value, the intelligent grade and the evaluation parameters and data content in the intelligent evaluation step are finally integrated, the optimal intelligent control measure of the current drilling intelligent point is decided, the maturity of the intelligent control technology in each intelligent point can be identified, the optimal intelligent control strategy suitable for each intelligent point is further effectively decided, various instructions are output according to the optimal scheme, the intelligent control advantages are exerted to the maximum extent, manual intervention is reduced, meanwhile, the low control quality caused by blind control is avoided, and the optimal control of both drilling timeliness and stability is realized;

in addition, by adopting the technical scheme of the invention, the construction scene capable of effectively accepting the control of the full closed-loop intelligent system can be identified at the first time, and reliable data support is provided for the implementation and optimization of the intelligent technology of the complex system.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention 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 block diagram of a closed loop technology architecture for intelligent drilling commonly used in drilling engineering, provided by an embodiment of the present invention;

FIG. 2 is a flow chart illustrating an implementation of an autonomous decision-making method for intelligently controlling drilling according to an embodiment of the present invention

FIG. 3 is a data, model and equipment maturity assessment navigation map of an autonomic decision making method provided in another embodiment of the present invention;

fig. 4 is a schematic structural diagram of an autonomous decision making system for implementing intelligent control drilling provided in an embodiment of the present invention.

Detailed Description

The following detailed description will be provided for the embodiments of the present invention with reference to the accompanying drawings and examples, so that the practitioner of the present invention can fully understand how to apply the technical means to solve the technical problems, achieve the technical effects, and implement the present invention according to the implementation procedures. It should be noted that, unless otherwise conflicting, the embodiments and features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are all within the scope of the present invention.

Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel, concurrently, or simultaneously. The order of the operations may be rearranged. A process may be terminated when its operations are completed, but may have additional steps not included in the figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc.

The computer equipment comprises user equipment and network equipment. The user equipment or the client includes but is not limited to a computer, a smart phone, a PDA, and the like; network devices include, but are not limited to, a single network server, a server group of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers. The computer devices may operate individually to implement the present invention or may be networked and interoperate with other computer devices in the network to implement the present invention. The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, and the like.

The terms "first," "second," and the like may be used herein to describe various elements, but these elements should not be limited by these terms, which are used merely to distinguish one element from another. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items. When an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

With the continuous improvement of the automatic intelligent drilling technology and the continuous fusion development of the digital technologies such as big data, artificial intelligence and the like, the drilling technology is turning from automation to intelligence. The intelligent drilling technology is provided with an artificial intelligent robot with learning, memorizing and judging functions, an intelligent, refined and miniaturized ground drilling system, an underground control system, a high-precision sensor and a high-speed transmission system, so that intelligent decision and control of partial drilling operation can be realized, field operation personnel are reduced, and the operation efficiency and safety are greatly improved. The national science and technology department has started a project research of 'national key research and development project special item of' revolutionary technical key scientific problem '-complex intelligent oil and gas drilling theory and method' in 2020 to promote the development of intelligent drilling technology.

(1) CN104806226A "Intelligent drilling expert System", the invention provides an intelligent drilling expert system, comprising: the system comprises a field sensor detection system, an intelligent expert system and an actuating mechanism; the output end of the on-site sensor detection system is connected with the input end of the intelligent expert system; the output end of the intelligent expert system is connected with the input end of the actuating mechanism, so that an automatic closed-loop drilling regulation and control system is formed; wherein the in situ sensor detection system is to: in the whole drilling process, drilling detection data are collected in real time, and the collected drilling detection data are uploaded to the intelligent expert system in real time; the intelligent expert system is configured to: receiving the drilling detection data uploaded by the field sensor detection system, carrying out intelligent analysis on the drilling detection data in real time, generating a regulation and control instruction for drilling field equipment by aiming at optimizing a drilling field process, and issuing the regulation and control instruction to a corresponding execution mechanism; the actuator is configured to: and receiving the regulation and control instruction issued by the intelligent expert system, and sending the regulation and control instruction to the corresponding field equipment so as to control the working state of the field equipment, thereby optimizing the technical process of the drilling field.

The intelligent drilling expert system described herein illustrates three major components (well known in the art) of sensors, intelligent expert systems, and actuators, and proposes a detailed control flow, but does not propose a method of autonomous decision making.

(2) CN106121621A "an Intelligent drilling expert System", the invention discloses an intelligent drilling expert system, which comprises a field sensor detection system, a communication system, an intelligent expert system and an operating system (which belongs to the industry general knowledge); the field sensor detection system is connected with the intelligent expert system through a communication system; the intelligent expert system is connected with the operating system, so that an automatic closed-loop drilling regulation and control system is formed, and the field sensor detection system acquires data of the whole drilling process; and sending the acquired data to the intelligent expert system through a communication system for processing monitoring, forecasting, analyzing, controlling and processing, and finally executing an operation instruction sent out after the intelligent expert system is analyzed by the operation system.

The intelligent drilling expert system described herein includes an in-situ sensor detection system, a communication system, an intelligent expert system, and an operating system (well known in the art), detailing the detailed construction of each component, and no method for autonomous decision making is presented.

(3) The invention provides an intelligent drilling system and method in CN109138836A, and belongs to the field of oil and gas drilling. The system comprises: the system comprises a ground system, a downhole system and an intelligent drilling control platform; the ground system comprises: the device comprises a coiled tubing truck group, a coiled tubing control platform, a coiled tubing drum, a lower casing derrick, an injection head, a rotary control head and matched blowout prevention equipment; the coiled tubing control platform and the coiled tubing roller are arranged on the coiled tubing truck group, and the lower casing derrick is arranged above the wellhead; the injection head, the rotary control head and the matched blowout preventer are sequentially connected from top to bottom and then arranged at the wellhead; the downhole system comprises: the system comprises a coiled tubing with a cable, a wet joint and an underground intelligent tubing string; one end of the coiled tubing with the cable is wound on the coiled tubing roller, and the other end of the coiled tubing with the cable sequentially penetrates through the injection head, the rotary control head and the matched blowout preventer and then enters the well mouth and is connected with the underground intelligent pipe string through the wet joint. The above scheme introduces the technical components of casing drilling and does not address the autonomous decision-making method.

(4) The document "intelligent drilling technology current situation and development direction (Petroleum institute, 2020.04)", proposes an intelligent drilling technology attack direction, which comprises a frame planning and standard system, a data real-time measurement technology, an information high-speed transmission technology, an automatic control system, a drilling intelligent decision analysis system and an intelligent drilling integrated technology. The required autonomous decision-making method is not elucidated.

(5) The document "the research and development of the intelligent drilling technology in China (the university of northeast China, the institute of Petroleum, 2020.04)", proposes the basic theory and the development process of the intelligent drilling technology, and analyzes the drilling key technologies such as the measurement while drilling technology, the intelligent guiding drilling technology, the intelligent drilling machine, the intelligent drill bit, the intelligent drill rod, the intelligent pressure control drilling technology, the remote intelligent drilling decision control system and the like. The drilling technology of China is upgraded from automation to intellectualization, and the drilling technology is deeply integrated with advanced technologies such as cognitive intelligence, deep learning, cloud computing, computer vision, man-machine interaction, virtual and reality, and the like, so that the intelligent development requirement of the multi-dimensional and combined drilling technology of China is met. No autonomous decision making method is set forth.

The drilling is a complex system engineering, multiple tasks such as trajectory control, efficiency optimization, risk control and the like must be solved simultaneously, the tasks have correlation and mutual influence relationship, each task must be realized jointly through a plurality of branch decision control points, and each decision point automatically sends an operation instruction to drilling equipment through decision so as to realize full closed-loop or local closed-loop automatic control drilling. The technology describes key technical constitution of intelligent drilling and technical points such as measurement, analysis, control and the like (all have an implicit assumption that all the technologies are mature and reliable and can be automatically executed without decision), but does not relate to an autonomous decision technology.

The ultimate goal of intelligent drilling is to dynamically optimize the drilling scheme by processing and analyzing various data in the drilling process, output various instructions in the optimal scheme, control the operation of drilling equipment, form a large technical system (as shown in the following figure), and achieve the optimal drilling efficiency, speed and quality. The key change of intelligent drilling is to reduce human intervention as much as possible in the drilling process until the human intervention is completely separated, so as to achieve the effect of full-automatic closed-loop control drilling. However, drilling is a very complex system project, and the ultimate ideal target cannot be achieved within a certain period of time, especially for complex and difficult wells, the full-automatic closed-loop drilling cannot be completely realized. Therefore, in the large closed-loop technology system, the autonomous decision-making plays a decisive role in the success or failure of the drilling, and once the decision-making error puts the non-optimal scheme (even the worst scheme) into practice, the decision-making error may cause complete deviation from the drilling purpose, considerably affect the timeliness and stability of the drilling and even cause serious accidents.

In order to solve the problems, the invention provides an automatic decision-making method for intelligent drilling, which completes the autonomous decision-making of intelligent points according to the maturity of each intelligent point by establishing a drilling intelligent point system and a drilling intelligent advanced matrix so as to realize intelligent closed-loop control drilling.

The detailed flow of the method of the embodiments of the present invention is described in detail below based on the accompanying drawings, and the steps shown in the flow chart of the drawings can be executed in a computer system containing a computer-executable instruction such as a set of computer-executable instructions. Although a logical order of steps is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.

Example one

Fig. 2 is a schematic flow chart illustrating a method for implementing an autonomous decision-making method for intelligently controlling drilling according to an embodiment of the present invention, and as can be seen from fig. 2, the method includes the following steps.

A construction identification step, namely judging an intelligent scene and an intelligent point to which the current drilling work belongs according to a pre-constructed construction division data table;

the intelligent evaluation step includes calculating comprehensive intelligent capacity of the current drilling intelligent point by considering different construction factors influencing intelligent degree of the intelligent point so as to represent real intelligent degree of each intelligent point;

a grade evaluation and analysis step, namely determining the intelligent grade of the current drilling intelligent point by combining the comprehensive intelligent capability obtained by calculation and a capability threshold value corresponding to the intelligent point;

and controlling a decision step, integrating the determined intelligent grade and evaluation parameters and data contents in the intelligent evaluation step, and deciding the optimal intelligent control measure of the current drilling intelligent point.

By adopting the decision logic of the embodiment of the invention, which schemes can be directly implemented and which schemes need to be manually decided can be intelligently and automatically selected according to actual conditions, thereby ensuring high efficiency and safety of drilling.

In practical applications, in an embodiment, the method further includes:

and a scene division step, dividing the drilling process into a plurality of intelligent scenes and intelligent points in advance according to well zone stratum data and drilling requirements, recording to form a construction division data table, wherein the control logic of each intelligent point can be independently described and implemented.

Specifically, in the scene division step, the drilling process is divided into a plurality of intelligent scenes (layers B) and intelligent points (layers C), and in one embodiment, the dividing of the intelligent scenes of the layers B for the drilling engineering includes: intelligent guiding, intelligent optimization and intelligent risk prevention and control; the intelligent points for dividing the layer C comprise: geosteering, trajectory control, optimization of mechanical drilling speed, optimization of tripping speed, optimization of drilling speed, optimization of pump-on speed, intelligent pressure control and optimization of drilling fluid performance are shown in the following table:

in the drilling scene, only the layer C is needed to make a decision manually or autonomously by the system, and complete intelligent autonomous control can be realized only after the intelligent degree of the intelligent points reaches a certain level (L5 level as described later), so that the autonomy of the corresponding intelligent scene is realized, and the ultimate goal of intelligent drilling can be reached after all scenes and the intelligent points reach autonomy: the large closed loop fully autonomous decision-making controls drilling. With the development of well drilling technology, the C layer can be expanded.

In a specific embodiment, the technology maturity level of each intelligent point is set according to different intelligent control forms in the drilling engineering, wherein the intelligent control forms comprise intelligent description, intelligent diagnosis, an intelligent prediction stage, intelligent consultation optimization and intelligent control optimization, and the requirement on the intelligent degree is gradually increased.

In practical application, the technical maturity of each intelligent point is divided into 5 grades: l1-description stage, L2-diagnosis stage, L3-prediction stage, L4-scheme optimization (consultation) stage, L5-scheme optimization (control) stage, step-by-step dependence and step-by-step promotion, and the step matrix of each stage is as follows:

further, in an embodiment, the different construction factors affecting the intelligent degree of the intelligent points include data maturity, model maturity and equipment maturity, and in the intelligent evaluation step, a plurality of corresponding evaluation indexes are set for each intelligent point based on the construction factors. For example, in the process of realizing intelligent evaluation, 3 pairs of evaluation indexes are set for each intelligent point, namely data maturity D belongs to [0,10], model maturity M belongs to [0,10] and equipment maturity E belongs to [0,10];

specifically, in one embodiment, the comprehensive intelligent capacity a of each intelligent point is determined according to the evaluation indexes according to the following operations:

A＝k*MIN(D,M,E)+(1-k)*AVERAGE(D,M,E)

in the formula, k represents a dominant factor constant of the current intelligent point, D represents the data maturity of the current intelligent point, M represents the model maturity of the current intelligent point, and E represents the equipment maturity of the current intelligent point.

In practical applications, in order to ensure that the lowest of the three indexes plays a leading role (i.e. the worst index can also reach the maturity approved by experts), the value of the constant k of the leading factor is usually set to a larger value close to 1, and k is greater than 0.5.

As shown in the maturity navigation information of fig. 3, each intelligent point may need to input a series of parameters, output the optimization result and convert the optimization result into a series of device execution parameters, which are respectively implemented by a series of device execution mechanisms. Because the parameters required by intelligent analysis may be multi-source, in one embodiment, evaluation indexes are set for input parameters of each data maturity, model maturity and equipment maturity;

wherein, the evaluation of the maturity of the single parameter data comprises the following steps: accurate satisfaction index a_d And the aging satisfies the index t_d ；

Data maturity d for individual parameters was determined according to the following formula_n ：

d_n ＝k*MIN(a_d ,t_d )+(1-k)*AVERAGE(a_d ,t_d )

Calculating the integral data maturity D of the corresponding intelligent point according to the following formula:

D＝MIN(d₁ ,d₂ ,…,d_n )；

in the formula, n is the number of parameters related to the current intelligent point, and k is a constant of a dominant factor.

In practical application, for model maturity evaluation, the model maturity evaluation is composed of 2 indexes: model accuracy a_m Calculating the rate satisfaction degree t_m ；

Specifically, the data maturity m of a single parameter is determined according to the following formula_n ：

m_n ＝k*MIN(a_m ,t_m )+(1-k)*AVERAGE(a_m ,t_m )

Calculating the integral data maturity D of the corresponding intelligent point according to the following formula:

M＝MIN(m₁ ,m₂ ,…,m_n )；

for the equipment maturity evaluation, the equipment maturity evaluation is set to be composed of 2 indexes: automatic execution capability a_e Response rate satisfaction degree t_e ；

Specifically, the data maturity e of a single parameter is determined according to the following formula_n ：

e_n ＝k*MIN(a_e ,t_e )+(1-k)*AVERAGE(a_e ,t_e )

Calculating the integral data maturity D of the corresponding intelligent point according to the following formula:

E＝MIN(e₁ ,e₂ ,…,e_n )。

after the comprehensive intelligent capacity data of the intelligent points are obtained, in the grade evaluation step, the comprehensive intelligent capacity value of the intelligent points is compared with the capacity threshold values corresponding to the intelligent description, the intelligent diagnosis, the intelligent prediction grade, the intelligent consultation optimization and the intelligent control optimization grades, and if the comprehensive intelligent capacity value of the Lx level corresponding to the intelligent points exceeds L_X And setting the intelligent mark of the intelligent point at the level as a set value to indicate that the construction of the intelligent point at the corresponding level can be automatically executed by a software system.

When A is>A₀ When the intelligent mark I of the intelligent point is set to be 1 (namely, the maturity degree reaches the level standard), otherwise, the intelligent mark I is set to be 0;

some intelligent point C_X Of the corresponding Lx levelIntelligent sign I (C)_X ，L_X ) And when the intelligent point reaches 1, the intelligent point reaches the maturity recognized by experts at the corresponding level, and the intelligent point can be automatically executed by a software system without manual intervention. When I (C)_X ，L₅ ) When =1, indicates C_X The intelligent point reaches the highest level, and the closed-loop automatic control of the machine can be realized.

Setting a capability threshold A for each intelligent level of each intelligent point₀ ∈[5,10]The higher the drilling safety requirements A₀ The higher the value; in a specific embodiment, the capacity threshold is set for each intelligent level of each intelligent point according to the requirement of the construction task of each intelligent point on intelligence, and the higher the requirement on drilling safety, the higher the capacity threshold is.

Because the geological environment of each well is different and the technical capability and equipment capability of construction teams are different, the A pairs can be combined with the factors in advance by an experienced expert for each well₀ The index is evaluated and set in a targeted manner. In the drilling process, a certain index can be updated at any time according to the actual situation.

The evaluation matrix of the single intelligent point is as follows:

in the control decision step, the determined intelligent grade and the evaluation parameters and data content in the intelligent evaluation step are integrated, and the intelligent control measure which is the best in the drilling work and corresponds to the current intelligent point is decided; for example, when the evaluation result of a certain intelligent point is determined that the intelligent point reaches the L3 level (simulation prediction), the data and the data calculated by the model can be used as the basis of expert decision, but can not be used as the automatic control instruction of the drilling machine (although the drilling machine per se meets the automatic control execution capability).

By adopting the decision logic in the embodiment of the invention, the drilling process control is divided into a plurality of intelligent application scenes, intelligent points are refined aiming at each scene, the autonomous decision is realized by evaluating the maturity of each intelligent point, the grade of each intelligent point is determined, and when the L5 grade reaches the set capacity, the ultimate goal of intelligent drilling, namely closed-loop automatic control drilling, can be reached.

Implementation scenario examples

Taking the drilling rate optimization intelligent point (intelligent point C3) of the W well as an example, suppose that: the W well is in a block with mature geological research and has no complex structures such as fault and the like, and the formation pressure is occasionally abnormal in small amplitude; the drilling footage of 200 wells exceeding 100 kilometers in the area is completed, and the wells use the latest automatic drilling machine (a winch, a top drive and a slurry pump can be continuously and automatically regulated and controlled, and an equipment control system is provided with an open interface and is in seamless connection with an intelligent decision-making system); the drilling fluid performance parameters can be automatically detected on line, the time delay is within 5s (the acceptability is 90%), the data error is about 10%, but the drilling fluid cannot be automatically regulated, a PDC drill bit is used, the used drilling speed prediction model utilizes the historical well data verification error of the block to be less than 14%, the average error is 9.6%, the real-time calculation time delay is within 6s (the acceptability is 83%), and the automatic optimization control of the drilling speed of a three-throw well section (sandstone is the main component and a small amount of mudstone components are provided) is realized through the real-time optimization of the drilling pressure, the rotating speed and the pumping pressure. The well drilling autonomous decision-making model construction step comprises:

(1) Setting a threshold value

Assume threshold values A of L1 to L5₀ Respectively as follows: 7.0, 8.0, 8.5, 9.0

(2) DME maturity assessment

First, a DME evaluation summary table was constructed

(1) Data maturity D rating (leading constant k = 0.85)

From the above table, D =8.8875 can be obtained.

(2) Model maturity M evaluation (k = 0.85)

The used artificial intelligent mechanical drilling rate prediction model is trained and verified by a model with 200 wells in the local area exceeding 100 kilometers in footage, and the corresponding level of the model is L₃ Cannot reach L₄ Or L₅ The results are relatively credible, the errors are all within 14%, the average error is 9.7%, the model accuracy is 8.6 by the highest error (worst case), the time delay acceptability is calculated in real time and can be set to be 8.3, so that the M =8.5775.

(3) Equipment maturity E evaluation (k = 0.85)

From the above table, D =9.575

(3) Calculation of Integrated Intelligence capability value A (ignoring E since M corresponds only to L3)

A＝k*MIN(D,M,E)+(1-k)*AVERAGE(D,M,E)＝0.85*MIN(8.8875,8.5775)+0.15*AVERAGE(8.8875,8.5775)＝8.6008

(4) Determining intelligence levels

As can be seen from the above table, the intelligent point reaches the L3 level (simulation prediction), and the data calculated by the above data and model can be used as the basis for expert decision making, but cannot be used as the automatic control instruction of the drilling machine (although the drilling machine itself meets the automatic control execution capability).

The invention relates to an intelligent drilling autonomous decision-making method, which establishes a maturity quantitative evaluation matrix for each intelligent point in different drilling scenes, when the maturity reaches a preset threshold value, the intelligent point reaches a corresponding intelligent level, when the maturity reaches an intelligent maturity of an L5 level, the intelligent point can automatically control drilling equipment without manual intervention to realize closed-loop decision-making control, when all the intelligent points reach the level (meaning that all the intelligent scenes reach the highest level), the final target of intelligent drilling, namely full-automatic closed-loop control drilling can be realized, which intelligent points are suitable for adopting full intelligent control can be effectively identified, and the optimal control strategy for the intelligent drilling point decision unsuitable for full intelligent control is ensured, so that the intelligent control advantage is exerted to the maximum degree in the drilling engineering, the low omnibearing control quality caused by blind intelligent control is avoided, and the optimal control of the time effect and the quality of the drilling engineering is realized in a true sense.

While, for purposes of simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present invention is not limited by the illustrated ordering of acts, as some steps may occur in other orders or concurrently with other steps in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.

It should be noted that in other embodiments of the present invention, the method may further obtain a new autonomous decision for implementing intelligent control drilling by combining one or some of the above embodiments, so as to implement analysis and optimization of intelligent control of oil and gas wells.

It should be noted that, based on the method in any one or more of the above embodiments of the present invention, the present invention further provides a storage medium storing program code capable of implementing the method in any one or more of the above embodiments, wherein the program code, when executed by an operating system, is capable of implementing the autonomous decision-making method for intelligently controlling drilling as described above.

Example two

While the method has been described in detail in the embodiments of the disclosure, it will be appreciated that the method may be implemented in any of numerous forms of apparatus or systems, and thus, based on other aspects of the method described in any one or more of the embodiments above, the present invention also provides an autonomous decision making system for implementing intelligently controlled drilling for performing the method of intelligently controlling drilling described in any one or more of the embodiments above. Specific examples are given below for a detailed description.

Specifically, fig. 4 is a schematic structural diagram of an autonomous decision-making system for implementing intelligent control drilling provided in an embodiment of the present invention, and as shown in fig. 4, the system includes:

a construction identification module 41 configured to determine an intelligent scene and an intelligent point to which the current drilling work belongs according to a pre-constructed construction division data table;

an intelligent evaluation module 43 configured to calculate the comprehensive intelligent capability of the current drilling intelligent point in consideration of different construction factors affecting the intelligent degree of the intelligent point so as to represent the real intelligent degree of each intelligent point;

a grade evaluation module 45 configured to determine an intelligent grade of the current drilling intelligent point by combining the calculated comprehensive intelligent capability and the capability threshold value corresponding to the intelligent point;

and a control decision module 47 configured to integrate the determined intelligent level and the evaluation parameters and data content in the intelligent evaluation process, and decide the optimal intelligent control measure of the current drilling intelligent point.

In a preferred embodiment, the system further comprises:

and the scene dividing module 40 is configured to divide the drilling process into a plurality of intelligent scenes and intelligent points in advance according to the well zone stratum data and the drilling requirement, record and form a construction dividing data table, wherein the control logic of each intelligent point can be independently described and implemented.

In a specific embodiment, the system sets the technology maturity level of each intelligent point according to different intelligent control forms in the drilling engineering, wherein the intelligent control forms comprise intelligent description, intelligent diagnosis, an intelligent prediction stage, intelligent consultation optimization and intelligent control optimization, and the requirement on the intelligent degree is gradually increased.

Further, in an embodiment, the intelligent evaluation module is configured to consider different construction factors affecting the intelligent degree of the intelligent points, and set a plurality of corresponding evaluation indexes for each intelligent point, where the construction factors include data maturity, model maturity, and equipment maturity.

In one embodiment, the intelligent evaluation module determines the comprehensive intelligent capacity a of each intelligent point according to the evaluation indexes according to the following operations:

A＝k*MIN(D,M,E)+(1-k)*AVERAGE(D,M,E)

in the formula, k represents a dominant factor constant of the current intelligent point, D represents the data maturity of the current intelligent point, M represents the model maturity of the current intelligent point, and E represents the equipment maturity of the current intelligent point.

In an optional embodiment, the level evaluation module is configured to: comparing the comprehensive intelligent capacity value of the intelligent point with the capacity threshold values corresponding to the intelligent description, intelligent diagnosis, intelligent prediction level, intelligent consultation optimization and intelligent control optimization levels, and if the comprehensive intelligent capacity value of the Lx level corresponding to the intelligent point exceeds L_X And setting the intelligent mark of the intelligent point at the level as a set value to indicate that the construction of the intelligent point at the corresponding level can be automatically executed by a software system.

Further, in one embodiment, the capacity threshold is set for each intelligent level of each intelligent point according to the requirement of the construction task of each intelligent point on intelligence, and the higher the requirement on drilling safety, the higher the capacity threshold is.

In practical application, in one embodiment, the intelligent evaluation module sets evaluation indexes for each data maturity, model maturity and equipment maturity;

wherein, the evaluation of the maturity of the single data comprises the following steps: accurate satisfaction index a_d And the aging satisfies the index t_d ；

The maturity d of the individual data was determined according to the following formula_n ：

d_n ＝k*MIN(a_d ,t_d )+(1-k)*AVERAGE(a_d ,t_d )

Calculating the integral data maturity D of the corresponding intelligent point according to the following formula:

D＝MIN(d₁ ,d₂ ,…,d_n )；

in the formula, n is the number of parameters related to the current intelligent point, and k is a constant of a dominant factor.

In the autonomous decision-making system for realizing intelligent control of drilling provided by the embodiment of the invention, each module or unit structure can independently operate or operate in a combined mode according to actual construction operation and decision requirements so as to realize corresponding technical effects.

It is to be understood that the disclosed embodiments of the invention are not limited to the particular structures, process steps, or materials disclosed herein but are extended to equivalents thereof as would be understood by those ordinarily skilled in the relevant arts. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting.

Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, appearances of the phrase "an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment.

Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. An autonomous decision-making method for intelligently controlling drilling, the method comprising:

a construction identification step, namely judging an intelligent scene and an intelligent point to which the current drilling work belongs according to a pre-constructed construction division data table;

the intelligent evaluation step is to evaluate the comprehensive intelligent capacity of the current intelligent drilling point by considering different construction factors influencing the intelligent degree of the intelligent drilling point so as to represent the real intelligent degree of each intelligent drilling point;

a grade evaluation and analysis step, namely determining the intelligent grade of the current drilling intelligent point by combining the comprehensive intelligent capability obtained by calculation and a capability threshold value corresponding to the intelligent point;

and a decision control step, integrating the determined intelligent grade and the evaluation parameters and data content in the intelligent evaluation step, and deciding the optimal intelligent control measure of the current drilling intelligent point.

2. The method of claim 1, wherein the method further comprises:

the method comprises a scene division step, wherein the drilling process is divided into a plurality of intelligent scenes and intelligent points according to well zone stratum data and drilling requirements in advance, and a construction division data table is formed by recording, wherein the control logic of each intelligent point can be independently described and implemented.

3. The method of claim 1, wherein the technology maturity level of each intelligent point is set according to different intelligent control forms in the drilling engineering, the intelligent control forms comprise intelligent description, intelligent diagnosis, intelligent prediction stage, intelligent consultation optimization and intelligent control optimization, and the requirement on the intelligent degree is gradually increased.

4. The method as claimed in claim 1, wherein the different construction factors affecting the intelligent degree of the intelligent points include data maturity, model maturity and equipment maturity, and in the intelligent evaluation step, a plurality of corresponding evaluation indexes are set for each intelligent point based on the construction factors.

5. The method of claim 1, wherein the comprehensive intelligence capability a of each intelligent point is determined according to the evaluation indexes according to the following operations:

A＝k*MIN(D,M,E)+(1-k)*AVERAGE(D,M,E)

in the formula, k represents a dominant factor constant of the current intelligent point, D represents the data maturity of the current intelligent point, M represents the model maturity of the current intelligent point, and E represents the equipment maturity of the current intelligent point.

6. The method of claim 1, wherein in the level evaluation step, the comprehensive intelligent capability value of the intelligent point is compared with the capability threshold values corresponding to the intelligent description, the intelligent diagnosis, the intelligent prediction level, the intelligent consultation optimization and the intelligent control optimization levels, and if the comprehensive intelligent capability value of the Lx level corresponding to the intelligent point exceeds L_X And setting the intelligent mark of the intelligent point at the level as a set value to indicate that the construction of the intelligent point at the corresponding level can be automatically executed by a software system.

7. The method of claim 6, wherein the capability threshold is set for each intelligent level of each intelligent point based on the requirement for intelligence by the construction task at each intelligent point, the higher the capability threshold for drilling safety requirements.

8. The method according to claim 1, wherein in the intelligent evaluation step, an evaluation index is set for each of data maturity, model maturity, and equipment maturity;

wherein, the evaluation of the maturity of the single data comprises the following steps: accurate satisfaction index a_d And the aging satisfies the degree index t_d ；

The maturity d of the individual data is determined according to the formula_n ：

d_n ＝k*MIN(a_d ,t_d )+(1-k)*AVERAGE(a_d ,t_d )

Calculating the integral data maturity D of the corresponding intelligent points according to the following formula:

D＝MIN(d₁ ,d₂ ,…,d_n )；

in the formula, n is the number of parameters related to the current intelligent point, and k is a constant of a dominant factor.

9. A storage medium having stored thereon program code for implementing the method according to any one of claims 1 to 8.

10. An autonomous decision making system for enabling intelligent controlled drilling, the system comprising:

the construction identification module is configured to judge an intelligent scene and an intelligent point to which the current drilling work belongs;

the intelligent evaluation module is configured to evaluate the comprehensive intelligent capacity of the current drilling intelligent point by considering different construction factors influencing the intelligent degree of the intelligent point so as to represent the real intelligent degree of each intelligent point;

the grade evaluation module is configured to determine the intelligent grade of the current drilling intelligent point by combining the comprehensive intelligent capacity obtained by calculation and the capacity threshold value corresponding to the intelligent point;

and the control decision module is configured to integrate the determined intelligent grade and evaluation parameters and data content in the intelligent evaluation process and decide the optimal intelligent control measure of the current drilling intelligent point.

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