Method, device, medium and equipment for intelligently deploying drilling machine

Abstract

The invention discloses a method, a device, a medium and equipment for intelligently deploying a drilling machine. The method comprises the steps that S10, a well drilling operation tracking theme library is constructed to obtain data of all available drilling machines and well sites, the well drilling operation tracking theme library comprises a well drilling dynamic database, and well drilling daily reports are stored in the well drilling dynamic database; s20, main work content of the drilling daily report is intercepted, data preprocessing is conducted on the intercepted content, and the drilling machine without the drilling footage state is screened out; and S30, outputting a drilling machine operation arrangement plan based on bilateral matching and a GSA algorithm so as to intelligently deploy the drilling machine in the drilling footage-free state. The method comprises the following steps: constructing a drilling operation tracking theme library to obtain data of all available drilling machines and well sites; by effectively fusing bilateral matching and the GSA algorithm, the allocation condition of the drilling machine can be efficiently and accurately tracked, the operation efficiency of drilling construction and the management level of drilling operation are improved, and the utilization rate of drilling machine resources is improved.

Description

Technical Field

[0001] This invention relates to the field of drilling rig allocation technology, and in particular to a method, apparatus, medium and equipment for intelligent drilling rig allocation. Background Technology

[0002] In recent years, with the in-depth implementation of the national oil and gas security strategy, many oilfields in my country have faced enormous pressure to increase reserves and production. Drilling is a key link in reservoir evaluation and production capacity construction, and its operational efficiency directly affects the development progress and economic benefits of the entire oil and gas field. As the core equipment in drilling operations, the rationality and efficiency of the drilling rig's operation are crucial to ensuring the smooth completion of various drilling indicators.

[0003] Currently, oilfield drilling managers primarily rely on daily drilling reports, drilling plans, and manual experience when compiling drilling rig operation schedules. While this method can meet basic production needs to some extent, the complexity and large volume of information in these reports require managers to invest significant time and effort in data processing and analysis, resulting in low drilling efficiency and poor management. Furthermore, with the expansion of oil and gas field development and advancements in drilling technology, the demand for drilling rig resources is increasing, and existing manual allocation methods often fall short, leading to low utilization rates of drilling rig resources. Summary of the Invention

[0004] The main objective of this invention is to provide a method and apparatus for intelligent allocation of drilling rigs, so as to solve the technical problems of low operating efficiency and low utilization rate of drilling rig resources in the prior art.

[0005] To achieve the above objectives, the present invention provides a method for intelligent allocation of drilling rigs, the method comprising the following steps: S10, constructing a drilling operation tracking subject library to obtain data on all available drilling rigs and well sites, the drilling operation tracking subject library including a drilling dynamic database, the drilling dynamic database storing daily drilling reports; S20, extracting the main work content of the daily drilling reports, and performing data preprocessing on the extracted content to filter out drilling rigs in a state of no progress; S30, outputting a drilling rig operation arrangement plan based on bilateral matching and GSA algorithm, thereby intelligently allocating the drilling rigs in a state of no progress.

[0006] In some embodiments, the drilling operation tracking subject library further includes a drilling information database and a drilling plan database, wherein the drilling information database stores basic drilling rig data and the drilling plan database stores drilling rig operation schedules.

[0007] In some embodiments, step S20 includes the following steps: S210, extracting the main work content of the drilling daily report; S220, performing data preprocessing on the extracted content, the data preprocessing including data cleaning, data integration, data conversion and data reduction; S230, filtering out drilling rigs with no progress based on the drilling information database.

[0008] In some embodiments, step S30 includes the following steps: S310, obtaining the drilling rig operation schedule plan for the non-shooting state based on the drilling plan database and performing initialization operations; S320, calculating the attraction between the non-shooting rig and the well site based on the GSA algorithm, according to their respective distances and masses; S330, evaluating the satisfaction of the matching combination obtained by the current attraction calculation based on the bilateral matching algorithm, according to the preference list and matching rules of the non-shooting rig and the well site; S340, updating the acceleration, velocity, and position of the non-shooting rig and the well site; S350, calculating the fitness and performing a selection operation based on the fitness value; S360, repeating the process of attraction calculation, updating, evaluation, and selection until a preset maximum number of iterations T is reached; S370, outputting the non-shooting rig and well site matching combination with the highest fitness obtained from the final iteration.

[0009] In some embodiments, step S310 includes the following steps: S3110, obtaining the drilling rig operation schedule plan for the non-shooting state based on the drilling plan database; S3120, defining the non-shooting rigs and well sites as individuals in the GSA algorithm; S3130, randomly generating an initial population and setting the population size to N, wherein the initial population includes a certain number of the non-shooting rigs and well sites; S3140, setting the initial gravity constant G to 10, the maximum number of iterations T to the total number of non-shooting rigs, and the initial mass M to 0; S3150, constructing a preference list.

[0010] In some embodiments, step S340 includes the following steps: S3410, updating the acceleration of the drilling rig and well site in the no-shoot state based on the resultant force calculated by gravity; S3420, updating the velocity and position of the drilling rig and well site in the no-shoot state based on Newton's second law and acceleration, simulating their movement in the search space.

[0011] In some embodiments, step S350 includes the following steps: S3510, calculating the fitness value of each individual in the current population according to the evaluation index in the bilateral matching algorithm; S3520, performing a selection operation based on the fitness value, retaining individuals with high fitness to enter the next generation population.

[0012] In addition, to achieve the above objectives, this application embodiment also provides a device for intelligent allocation of drilling rigs. The device includes: a construction module for constructing a drilling operation tracking theme library to obtain data on all available drilling rigs and well sites. The drilling operation tracking theme library includes a drilling dynamic database, which stores daily drilling reports. The preprocessing module is used to extract the main work content of the drilling daily report, perform data preprocessing on the extracted content, and filter out drilling rigs in the no-foot-feed state; the intelligent allocation module is used to output the drilling rig operation arrangement plan based on bilateral matching and GSA algorithm, thereby intelligently allocating the drilling rigs in the no-foot-feed state.

[0013] In addition, to achieve the above objectives, embodiments of this application also provide a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the drilling rig intelligent allocation method described in any embodiment of this application.

[0014] Furthermore, to achieve the above objectives, embodiments of this application also provide a computing device, which includes at least one processor, a memory, and an input / output unit; wherein the memory is used to store a computer program, and the processor is used to call the computer program stored in the memory to execute the drilling rig intelligent dispatching method described in any embodiment of this application.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: The intelligent drilling rig allocation method provided in this application constructs a drilling operation tracking database to obtain data on all available drilling rigs and well sites. By effectively integrating bilateral matching and the GSA algorithm, a new intelligent drilling rig allocation technology solution is constructed, which can efficiently and accurately track the allocation status of drilling rigs. This realizes the transformation from traditional "manual" allocation to modern "intelligent" allocation, greatly improving the operational efficiency and management level of drilling operations, and increasing the utilization rate of drilling rig resources. Attached Figure Description

[0016] Figure 1 A flowchart of the method for intelligent drilling rig allocation provided in the embodiments of this application; Figure 2 This is a structural block diagram of the drilling rig intelligent allocation device provided in the embodiments of this application; Figure 3 This is a schematic diagram illustrating the construction of a drilling operation tracking topic library provided in an embodiment of this application; Figure 4 This is a schematic diagram of data cleaning provided for an embodiment of this application; Figure 5 A schematic diagram illustrating keyword selection for a drilling rig in a state of no progress, as provided in an embodiment of this application. Figure 6 This is a schematic diagram of the structure of the medium provided in the embodiments of this application; Figure 7 A schematic diagram of the structure of a computing device provided in an embodiment of this application.

[0017] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the application. Rather, these embodiments are provided to make the disclosure more thorough and complete, and to fully convey the scope of the disclosure to those skilled in the art.

[0019] To address the aforementioned technical problems, embodiments of this application provide a method for intelligent drilling rig allocation, such as... Figure 1 As shown, the method may include the following steps: S10, Build a drilling operation tracking thematic library to obtain data on all available drilling rigs and well sites, for reference. Figure 3 The drilling operation tracking subject library includes a drilling dynamic database, which stores daily drilling reports.

[0020] Specifically, the drilling dynamics database stores nearly 10 years of daily drilling reports.

[0021] In an exemplary embodiment, the drilling operation tracking subject library further includes a drilling information database and a drilling plan database. The drilling information database stores basic drilling rig data, and the drilling plan database stores drilling rig operation schedules.

[0022] The basic data of the drilling rig includes the drilling rig name, drilling rig model, drilling rig status, drilling depth capacity, drilling team qualifications, well number, designed well depth, designed lifting load, and geographical location.

[0023] S20 extracts the main work content of the daily drilling report, performs data preprocessing on the extracted content, and filters out drilling rigs that have no progress.

[0024] Specifically, the state of no progress includes being in the relocation, intermediate completion, well completion, and waiting period.

[0025] In an exemplary embodiment, step S20 may include the following steps: S210 extracts the main work content of the daily drilling report; S220, perform data preprocessing on the extracted content, including data cleaning, data integration, data transformation, and data reduction; S230, reference Figure 5Based on the drilling information database, drilling rigs that have not made any progress are selected.

[0026] Specifically, refer to Figure 4 Data cleaning is used to remove outlier, erroneous, and duplicate data, and specifically includes the following: Common methods for handling missing values ​​include deletion, replacement, and imputation. Outliers are typically handled by identifying and processing them. Data integration is used to combine data from multiple data sources. Data transformation is used to convert raw data into a format suitable for analysis and modeling, such as converting date and time data into date format or categorical data into numerical data. Data specifications are used to simplify or standardize data, making it easier for data analysis and data mining.

[0027] In addition, before extracting the main work content of the drilling daily report, drilling daily reports for the past 3-5 years were obtained from the drilling dynamics database. The main work content of the drilling daily report is the complex accidents reported in the drilling daily report.

[0028] S30, based on bilateral matching and GSA algorithm, outputs drilling rig operation arrangement plan, thereby intelligently allocating drilling rigs in the no-foot-progress state.

[0029] Specifically, the bilateral matching algorithm is an algorithm used to solve matching problems between two sets, commonly found in fields such as economics, computer science, and social sciences. Its goal is to establish one-to-one pairings between elements of two sets to maximize some overall benefit.

[0030] Furthermore, the bilateral matching algorithm in this exemplary embodiment is a simulated annealing algorithm. This algorithm improves the efficiency and quality of matching through improvements such as initial solution selection strategy, double threshold setting for inner and outer loops, dynamic selection of cooling function, and optimal state recording.

[0031] Furthermore, the Gravitational Search Algorithm (GSA) is used to solve optimization problems, particularly nonlinear ones, and it follows Newton's law of universal gravitation. In the proposed algorithm, particles are treated as objects, and their properties are estimated in consideration of their mass. Gravity is the tendency for masses to accelerate each other. It is one of the four fundamental forces in nature (the others being electromagnetism, the weak nuclear force, and the strong nuclear force).

[0032] In an exemplary embodiment, step S30 may specifically include the following steps: S310, Obtain the drilling rig operation schedule plan for the no-foot-progress state based on the drilling plan database, and perform initialization operations; S320, based on the GSA algorithm, calculates the gravitational force between the drilling rig and the well site in the no-foot-progress state, according to their respective masses; S330, based on the bilateral matching algorithm, evaluates the satisfaction of the matching combination obtained by the current gravity calculation according to the preference list and matching rules of the drilling rig and well site in the no-foot-progress state; S340, update the acceleration, velocity, and position of the drilling rig and well site in the no-foot-progress state; S350, calculate fitness and perform selection operations based on fitness values; S360 repeats the process of gravity calculation, updating, evaluation, and selection until the preset maximum number of iterations T is reached; S370 outputs the rig and well site matching combination with the highest fitness obtained from the final iteration for the no-foot-progress state.

[0033] In an exemplary embodiment, step S310 may specifically include the following steps: S3110, Obtain the drilling rig operation schedule plan for the no-feeding state based on the drilling plan database; S3120, the drilling rig and well site in the state of no progress are defined as individuals in the GSA algorithm; S3130, Randomly generate an initial population and set the population size to N. The initial population includes a certain number of the drilling rigs and well site individuals in the no-foot-progress state. S3140, set the initial gravity constant G to 10, the maximum number of iterations T to the total number of drilling rigs in the no-foot-progress state, and the initial mass M to 0; S3150, Construct the preference list. The specific construction process is as follows: ①Refer to Tables 1 and 2 to obtain basic information about the well site and drilling rigs in the state of no footage by considering the geographical location and business attributes of the drilling rigs and well sites. The basic information table for a single well is shown in Table 1, and the basic information table for drilling rigs in the state of no footage is shown in Table 2.

[0034] ②Refer to Table 3. Based on the key business attributes of the well site and drilling rig, five priority factors are extracted, including drilling depth capability, relocation distance, well team qualifications, lifting load, and drive mode. Weight values ​​are assigned to each factor. Table 3 is the factor weight table.

[0035] ③ Calculate the preference scores of drilling rigs and well sites in the no-shoot state for all other objects. Consider the pairing combination between each drilling rig and well site in the no-shoot state to evaluate drilling depth capability, relocation distance, well team qualifications, lifting load, and drive mode factors. Weight these factors according to their relative importance and construct a descending preference list for each drilling rig and a descending preference list for each well site.

[0036] Table 1

[0037] Table 2

[0038] Table 3

[0039] Furthermore, the drilling rigs and well sites in the bilateral matching problem are treated as individuals within the GSA algorithm, leveraging the global search capability of GSA to optimize the matching results. When integrating GSA and bilateral matching algorithms, the search process of GSA can be combined with the pairing process of bilateral matching. For example, in each iteration of GSA, bilateral matching can be evaluated based on the current population (i.e., combinations of drilling rigs and well sites in a no-shoot state), and the population can be updated based on the matching results (i.e., recombining drilling rigs and well sites in a no-shoot state). In this way, by combining the search capability of GSA with the pairing rules of bilateral matching, matching combinations that meet the needs of both parties can be found more effectively.

[0040] Furthermore, the algorithm fusion approach of GSA and bilateral matching is as follows: (1) Two-sided matching algorithm: Two sets are defined to represent drilling rigs and well sites in a non-moving state, respectively. Based on the actual needs and performance of the drilling rigs and well sites in a non-moving state, a preference list is established for each drilling rig or well site, indicating the degree of preference for the other. A bilateral matching algorithm is used to achieve initial matching of drilling rigs and well sites in a non-moving state based on the preference lists of both parties.

[0041] (2) GSA algorithm (Gravity Search Algorithm): The matching problem between a drilling rig and a well site in a non-moving state is treated as an optimization problem, where each solution (i.e., a matching combination of a drilling rig and a well site in a non-moving state) is considered an individual, and its quality (fitness) is determined by indicators such as matching satisfaction. The optimal matching combination is searched by simulating the gravitational interactions between individuals.

[0042] (3) Algorithm fusion: First, a preliminary matching algorithm is used to obtain a set of candidate matching combinations. Then, these candidate matching combinations are used as the initial solutions for the GSA algorithm. The global search capability and convergence of the GSA algorithm are leveraged to optimize the bilateral matching algorithm, finding the optimal matching combination and improving matching efficiency and quality.

[0043] Furthermore, the iterative steps of the fusion algorithm are as follows: (1) In each iteration, the particle update mechanism of GSA and the pairing logic of the bilateral matching algorithm are combined.

[0044] (2) Update the position of the particle using the law of gravity and the acceleration equation in the GSA algorithm (i.e., the pairing of the drilling rig and the well site in the state of no progress).

[0045] (3) During the update process, the preference and stability requirements in the bilateral matching algorithm are considered to ensure that the newly generated pairing is stable and conforms to the preferences of both parties.

[0046] In an exemplary embodiment, step S340 may specifically include the following steps: S3410, based on the resultant force calculated by gravity, update the acceleration of the drilling rig and well site in the no-foot-progress state; S3420 updates the velocity and position of the drilling rig and well site in the no-foot-progress state based on Newton's second law and acceleration, simulating their movement in the search space.

[0047] Newton's second law states that the acceleration of an object is directly proportional to the force acting upon it and inversely proportional to its mass. The direction of the acceleration is the same as the direction of the force. Mathematically, this is expressed as F = ma, where F is the force, m is the mass of the object, and a is the acceleration of the object.

[0048] In an exemplary embodiment, step S350 may specifically include the following steps: S3510, Calculate the fitness value of each individual in the current population based on the evaluation index in the bilateral matching algorithm; S3520, a selection operation is performed based on the fitness value, retaining individuals with high fitness to enter the next generation of the population.

[0049] Specifically, fitness value refers to the ability of an individual with a particular genotype to survive and leave offspring compared to individuals with other genotypes. It is an important parameter for quantitative research on natural selection, and the highest fitness value is usually set at 1. Furthermore, an individual's fitness value measures its advantage in the population, used to distinguish between "good" and "bad" individuals.

[0050] It should be noted that the drilling rigs in the above embodiments all refer to drilling rigs in a state of no progress.

[0051] In one and more of these exemplary embodiments, a drilling operation tracking subject library is constructed to obtain data on all available drilling rigs (i.e., drilling rigs with no footage) and well sites. By effectively integrating bilateral matching and the GSA algorithm, a new intelligent drilling rig allocation technology solution is constructed, which can efficiently and accurately track the allocation status of drilling rigs, realize the transformation from traditional "manual" allocation to modern "intelligent" allocation, greatly improve the operational efficiency and management level of drilling operations, and improve the utilization rate of drilling rig resources.

[0052] The following is another method for intelligent drilling rig allocation; (1) Data preparation: Collect relevant information about the drilling rig and well site, including rig name, rig model, drilling depth capacity, well team qualifications, well number, designed well depth, designed lifting load, and geographical location.

[0053] Based on five key factors—drilling depth capability, relocation distance, well crew qualifications, lifting load, and drive mode—a preference list for drilling rigs and well sites is constructed.

[0054] (2) Two-sided matching: Using a two-sided matching algorithm, a preliminary matching is performed based on the preference lists of the drilling rig and the well site to obtain a set of candidate matching combinations.

[0055] (3) Optimization of GSA algorithm: Candidate matching combinations are used as the initial solution for the GSA algorithm. The positions and masses of the drilling rig and well site are initialized. The magnitude and acceleration of the gravitational force between each drilling rig and well site are calculated. Based on the magnitude and acceleration of the gravitational force, the positions and velocities of the drilling rig and well site are updated. Repeat the iterations until the stopping condition is met (the maximum number of iterations is the number of drilling rigs) to obtain the optimal matching combination.

[0056] (4) Output of results: Output the optimal matching combination, including information such as the matching drilling rig and well site, and matching satisfaction.

[0057] It should be noted that the drilling rigs in this embodiment all refer to drilling rigs that are not making any progress.

[0058] Based on the above embodiments, refer to Figure 2 Another embodiment of this application also provides a device for intelligent drilling rig allocation, the device 200 for intelligent drilling rig allocation may include the following modules: Module 210 is used to build a drilling operation tracking theme library to obtain data on all available drilling rigs and well sites. The drilling operation tracking theme library includes a drilling dynamic database, which stores daily drilling reports. The preprocessing module 220 is used to extract the main work content of the drilling daily report, perform data preprocessing on the extracted content, and filter out drilling rigs with no progress. The intelligent allocation module 230 is used to output a drilling rig operation schedule based on bilateral matching and GSA algorithm, thereby intelligently allocating drilling rigs in the no-foot-progress state.

[0059] Based on the above embodiments, this application also provides a computer-readable storage medium, see reference. Figure 6 The computer-readable storage medium shown is an optical disc 50, on which a computer program (i.e., a program product) is stored. When the computer program is executed by a processor, it implements the steps described in the above-described method implementation, such as: constructing a drilling operation tracking subject library to obtain data on all available drilling rigs and well sites; the drilling operation tracking subject library includes a drilling dynamic database, which stores daily drilling reports; extracting the main work content of the daily drilling reports and performing data preprocessing on the extracted content to filter out drilling rigs in a state of no progress; and outputting a drilling rig operation scheduling plan based on bilateral matching and the GSA algorithm, thereby intelligently allocating the drilling rigs in a state of no progress. The specific implementation methods of each step will not be repeated here.

[0060] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0061] In addition to the above embodiments, this application also provides a computing device. Figure 7 A block diagram is shown of an exemplary computing device 60 suitable for implementing embodiments of the present application. The computing device 60 may be a computer system or a server. Figure 7 The computing device 60 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0062] like Figure 7 As shown, the components of computing device 60 may include, but are not limited to: one or more processors or processing units 601, system memory 602, and bus 603 connecting different system components (including system memory 602 and processing unit 601).

[0063] The computing device 60 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computing device 60, including volatile and non-volatile media, removable and non-removable media.

[0064] System memory 602 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 6021 and / or cache memory 6022. Computing device 60 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, ROM 6023 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 7 (Not shown in the image, usually referred to as "hard drive"). Although not shown in... Figure 7 The diagram illustrates that a disk drive for reading and writing to removable non-volatile disks (e.g., "floppy disks") and an optical disk drive for reading and writing to removable non-volatile optical disks (e.g., CD-ROMs, DVD-ROMs, or other optical media) can be provided. In these cases, each drive can be connected to a bus 603 connecting different system components via one or more data media interfaces. The system memory 602 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0065] A program / utility 6025 having a set (at least one) of program modules 6024 may be stored, for example, in system memory 602, and such program modules 6024 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment. Program modules 6024 typically perform the functions and / or methods described in the embodiments of this application.

[0066] The computing device 60 can also communicate with one or more external devices 604 (such as a keyboard, pointing device, display, etc.). This communication can be performed via input / output (I / O) interface 605. Furthermore, the computing device 60 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 606. Figure 7 As shown, network adapter 606 communicates with other modules of computing device 60 (such as processing unit 601, etc.) via bus 603, which connects different system components. It should be understood that, although... Figure 7 Other hardware and / or software modules may be used in conjunction with computing device 60, as not shown in the diagram.

[0067] The processing unit 601 executes various functional applications and data processing by running programs stored in the system memory 602. For example, it constructs a drilling operation tracking subject library to obtain data on all available drilling rigs and well sites. This drilling operation tracking subject library includes a drilling dynamic database containing daily drilling reports. It extracts the main work content from the daily drilling reports, performs data preprocessing on the extracted content, and filters out drilling rigs in a state of no progress. Based on bilateral matching and the GSA algorithm, it outputs a drilling rig operation scheduling plan, thereby intelligently allocating the drilling rigs in a state of no progress. The specific implementation methods of each step will not be repeated here. It should be noted that although several units / modules or sub-units / sub-modules of the intelligent drilling rig allocation device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules for embodiment.

[0068] In the description of this application, it should be noted that the terms "first", "second", and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0069] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0070] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0071] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0072] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0073] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the technical scope disclosed in this application. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

[0075] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

Claims

1. A method for intelligent allocation of drilling rigs, characterized in that, The method for intelligent drilling rig allocation includes the following steps: S10, Construct a drilling operation tracking subject library to obtain data on all available drilling rigs and well sites. The drilling operation tracking subject library includes a drilling dynamic database, which stores daily drilling reports. S20 extracts the main work content of the daily drilling report, performs data preprocessing on the extracted content, and filters out drilling rigs with no progress. S30, based on bilateral matching and GSA algorithm, outputs drilling rig operation arrangement plan, thereby intelligently allocating drilling rigs in the no-foot-progress state.

2. The method for intelligent drilling rig allocation according to claim 1, characterized in that, In step S10, the drilling operation tracking subject database also includes a drilling information database and a drilling plan database. The drilling information database stores basic drilling rig data, and the drilling plan database stores drilling rig operation schedules.

3. The method for intelligent drilling rig allocation according to claim 2, characterized in that, Step S20 includes the following steps: S210 extracts the main work content of the daily drilling report; S220, perform data preprocessing on the extracted content, including data cleaning, data integration, data transformation, and data reduction; S230 is a drilling rig that has no progress, selected based on the drilling information database.

4. The method for intelligent allocation of drilling rigs according to claim 2, characterized in that, Step S30 includes the following steps: S310, Obtain the drilling rig operation schedule plan for the no-foot-progress state based on the drilling plan database, and perform initialization operations; S320, based on the GSA algorithm, calculates the gravitational force between the drilling rig and the well site in the no-foot-progress state, according to their respective masses; S330, based on the bilateral matching algorithm, evaluates the satisfaction of the matching combination obtained by the current gravity calculation according to the preference list and matching rules of the drilling rig and well site in the no-foot-progress state; S340, update the acceleration, velocity, and position of the drilling rig and well site in the no-foot-progress state; S350, calculate fitness and perform selection operations based on fitness values; S360 repeats the process of gravity calculation, updating, evaluation, and selection until the preset maximum number of iterations T is reached; S370 outputs the rig and well site matching combination with the highest fitness obtained from the final iteration for the no-foot-progress state.

5. The method for intelligent drilling rig allocation according to claim 4, characterized in that, Step S310 includes the following steps: S3110, Obtain the drilling rig operation schedule plan for the no-feeding state based on the drilling plan database; S3120, the drilling rig and well site in the state of no progress are defined as individuals in the GSA algorithm; S3130, Randomly generate an initial population and set the population size to N. The initial population includes a certain number of the drilling rigs and well site individuals in the no-foot-progress state. S3140, set the initial gravity constant G to 10, the maximum number of iterations T to the total number of drilling rigs in the no-foot-progress state, and the initial mass M to 0; S3150, Build a preference list.

6. The method for intelligent allocation of drilling rigs according to claim 4, characterized in that, Step S340 includes the following steps: S3410, based on the resultant force calculated by gravity, update the acceleration of the drilling rig and well site in the no-foot-progress state; S3420 updates the velocity and position of the drilling rig and well site in the no-foot-progress state based on Newton's second law and acceleration, simulating their movement in the search space.

7. The method for intelligent drilling rig allocation according to claim 5, characterized in that, Step S350 includes the following steps: S3510, Calculate the fitness value of each individual in the current population based on the evaluation index in the bilateral matching algorithm; S3520, a selection operation is performed based on the fitness value, retaining individuals with high fitness to enter the next generation of the population.

8. A device for intelligent dispatching of drilling rigs, characterized in that, include: The building module is used to build a drilling operation tracking subject library to obtain data on all available drilling rigs and well sites. The drilling operation tracking subject library includes a drilling dynamic database, which stores daily drilling reports. The preprocessing module is used to extract the main work content of the drilling daily report, perform data preprocessing on the extracted content, and filter out drilling rigs with no progress. The intelligent allocation module is used to output a drilling rig operation schedule based on bilateral matching and GSA algorithm, thereby intelligently allocating drilling rigs in the no-foot-progress state.

9. A computer-readable storage medium, characterized in that, It includes instructions that, when executed on a computer, cause the computer to perform the method of intelligent rig allocation as described in any one of claims 1 to 7.

10. A computing device, characterized in that, The computing device includes: At least one processor, memory, and input / output unit; The memory is used to store computer programs, and the processor is used to call the computer programs stored in the memory to execute the drilling rig intelligent dispatching method according to any one of claims 1 to 7.

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