Oil and gas industry chain decision optimization method and device

Abstract

The invention discloses an oil and gas industry chain decision optimization method and apparatus. The method comprises the steps of obtaining a task set of an oil and gas industry chain, a dependency relationship between tasks, a real-time resource state and a preset resource allocation function; generating a task graph according to the dependency relationship between the task set and the tasks; decomposing the task graph according to the type of the task in the task graph to generate a plurality of sub-task graphs; according to a preset prior probability algorithm, generating a plurality of candidate task sequences for each subtask graph; determining the posterior probability of each candidate task sequence according to a preset posterior probability algorithm; the preset posterior probability algorithm is set according to a Bayesian reasoning algorithm; comparing the posterior probabilities of the plurality of candidate task sequences corresponding to each sub-task graph, and determining a final task sequence corresponding to the sub-task graph; and real-time resources are allocated, so that the decision-making efficiency of an oil and gas industry chain can be improved, and resource consumption and operation cost are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas industry chain decision optimization, and in particular to an oil and gas industry chain decision optimization method and device. Background Art

[0002] The oil and gas industry chain encompasses multiple complex links, spanning exploration, development, production, transportation, processing, and sales. It encompasses the upstream, midstream, and downstream sectors of the oil and gas industry, each of which involves numerous resource allocation and task planning issues. The oil and gas industry chain is capital-intensive, technology-intensive, and labor-intensive, requiring multifaceted collaboration to form a complete industry chain. Optimizing and integrating the oil and gas industry chain can improve industry efficiency, reduce costs, enhance energy security, and enhance competitiveness.

[0003] Traditional oil and gas industry chain decision-making methods often ignore the importance of resource rationality and hierarchical task decomposition when dealing with complex decision-making problems, resulting in resource consumption, low efficiency and high operating costs, affecting the overall competitiveness and economic benefits of oil and gas companies. Summary of the Invention

[0004] An embodiment of the present invention provides a method for optimizing oil and gas industry chain decision-making, which is used to improve the decision-making efficiency of the oil and gas industry chain and reduce resource consumption and operating costs. The method includes:

[0005] Obtaining a set of tasks in the oil and gas industry chain, dependencies between tasks, real-time resource status, and preset resource allocation functions; a task set includes multiple tasks; a task is a business operation performed in the oil and gas industry chain;

[0006] Generate a task graph based on the task set and the dependencies between tasks;

[0007] According to the types of tasks in the task graph, the task graph is decomposed to generate multiple subtask graphs;

[0008] Generate multiple candidate task sequences for each subtask graph according to a preset prior probability algorithm; the preset prior probability algorithm is set according to a minimum description length criterion and a reuse bias prior algorithm; the reuse bias prior algorithm is used to screen tasks and / or determine the execution order of tasks in the candidate task sequence according to the execution frequency and execution success rate of the tasks;

[0009] Determine the posterior probability of each candidate task sequence according to a preset posterior probability algorithm; the preset posterior probability algorithm is set according to the Bayesian inference algorithm;

[0010] Compare the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph, and determine the candidate task sequence with the highest posterior probability as the final task sequence corresponding to the subtask graph;

[0011] Allocate real-time resources according to the final task sequence, real-time resource status and preset resource allocation function.

[0012] An embodiment of the present invention further provides an oil and gas industry chain decision optimization device for improving the decision efficiency of the oil and gas industry chain and reducing resource consumption and operating costs. The device includes:

[0013] The acquisition module is used to obtain the task set of the oil and gas industry chain, the dependencies between tasks, the real-time resource status and the preset resource allocation function; the task set includes multiple tasks; tasks are business operations performed in the oil and gas industry chain;

[0014] The task graph generation module is used to generate a task graph based on the task set and the dependencies between tasks;

[0015] A subtask graph generation module is used to decompose the task graph according to the types of tasks in the task graph and generate multiple subtask graphs;

[0016] A candidate task sequence generation module is configured to generate multiple candidate task sequences for each subtask graph based on a preset prior probability algorithm; the preset prior probability algorithm is set based on a minimum description length criterion and a reuse bias prior algorithm; the reuse bias prior algorithm is configured to screen tasks and / or determine the execution order of tasks in the candidate task sequence based on their execution frequency and execution success rate;

[0017] A posterior probability determination module is used to determine the posterior probability of each candidate task sequence according to a preset posterior probability algorithm; the preset posterior probability algorithm is set according to the Bayesian inference algorithm;

[0018] The final task sequence determination module is used to compare the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph, and determine the candidate task sequence with the highest posterior probability as the final task sequence corresponding to the subtask graph;

[0019] The resource allocation module is used to allocate real-time resources according to the final task sequence, real-time resource status and preset resource allocation function.

[0020] Compared with the technical solution for oil and gas industry chain decision optimization in the prior art, the embodiment of the present invention obtains the task set of the oil and gas industry chain, the dependency relationship between tasks, the real-time resource status and the preset resource allocation function; the task set includes multiple tasks; the task is a business operation performed in the oil and gas industry chain; a task graph is generated according to the task set and the dependency relationship between tasks; the task graph is decomposed according to the type of task in the task graph to generate multiple subtask graphs; multiple candidate task sequences are generated for each subtask graph according to a preset prior probability algorithm; the preset prior probability algorithm is set according to the minimum description length criterion and the reuse bias prior algorithm; the reuse bias The a priori algorithm is used to screen tasks and / or determine the execution order of tasks in the candidate task sequence according to the execution frequency and execution success rate of the tasks; the posterior probability of each candidate task sequence is determined according to the preset posterior probability algorithm; the preset posterior probability algorithm is set according to the Bayesian inference algorithm; the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph are compared, and the candidate task sequence with the highest posterior probability is determined as the final task sequence corresponding to the subtask graph; real-time resources are allocated according to the final task sequence, real-time resource status and preset resource allocation function, which can improve the decision-making efficiency of the oil and gas industry chain and reduce resource consumption and operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0022] Figure 1 This is a flow chart of a method for optimizing oil and gas industry chain decision-making provided in an embodiment of the present invention;

[0023] Figure 2 This is a flowchart of a specific example of a method for optimizing oil and gas industry chain decision-making provided in an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of an oil and gas industry chain decision optimization device provided in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of a specific example of an oil and gas industry chain decision optimization device provided in an embodiment of the present invention;

[0026] Figure 5 A schematic diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] To make the purpose, technical solutions and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. Here, the exemplary embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0028] The acquisition, storage, use, and processing of data in the technical solution of this application comply with relevant laws and regulations.

[0029] The term "and / or" herein simply describes an association relationship, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, and the existence of B alone. In addition, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0030] In the description of this specification, the terms "include", "including", "have", "contain", etc. are all open terms, which mean including but not limited to. The descriptions with reference to the terms "one embodiment", "a specific embodiment", "some embodiments", "for example", etc. mean that the specific features, structures or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures or characteristics described can be combined in a suitable manner in any one or more embodiments or examples. The order of steps involved in each embodiment is used to schematically illustrate the implementation of the present application, and the order of steps therein is not limited and can be appropriately adjusted as needed.

[0031] To address the problems of the prior art, embodiments of the present invention aim to optimize the decision-making process for oil and gas exploration, production, processing, and transportation within the oil and gas industry chain by introducing hierarchical planning and grammatical induction methods. Embodiments of the present invention utilize an innovative approach to transform complex decision-making problems into a more manageable and resolvable form, thereby improving the efficiency and quality of decision-making within the oil and gas industry chain. Embodiments of the present invention utilize hierarchical planning to decompose large decision-making tasks into multiple hierarchical subtasks, making each subtask easier to handle and facilitating overall coordination and optimization.

[0032] Figure 1 Flowchart of a method for optimizing oil and gas industry chain decision-making provided in an embodiment of the present invention. Figure 1 As shown, the method may include:

[0033] Step 101: Obtain a task set, dependencies between tasks, real-time resource status, and a preset resource allocation function for the oil and gas industry chain; the task set includes multiple tasks; and tasks are business operations performed in the oil and gas industry chain.

[0034] Step 102: Generate a task graph based on the task set and the dependencies between tasks;

[0035] Step 103: Decompose the task graph according to the types of tasks in the task graph to generate multiple subtask graphs;

[0036] Step 104: Generate multiple candidate task sequences for each subtask graph based on a preset prior probability algorithm; the preset prior probability algorithm is set based on a minimum description length criterion and a reuse bias prior algorithm; the reuse bias prior algorithm is used to screen tasks and / or determine the execution order of tasks in the candidate task sequence based on the execution frequency and execution success rate of the tasks;

[0037] Step 105, determining the posterior probability of each candidate task sequence according to a preset posterior probability algorithm; the preset posterior probability algorithm is set according to the Bayesian inference algorithm;

[0038] Step 106 , comparing the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph, and determining the candidate task sequence with the highest posterior probability as the final task sequence corresponding to the subtask graph;

[0039] Step 107 : Allocate real-time resources according to the final task sequence, the real-time resource status and the preset resource allocation function.

[0040] An embodiment of the present invention provides an oil and gas industry chain decision optimization method based on hierarchical planning and grammatical induction. By decomposing complex decision problems into hierarchical subtasks and using a grammatical induction model to generate and optimize task sequences, an efficient and low-cost decision-making process is achieved, thereby improving the decision-making efficiency of the oil and gas industry chain and reducing resource consumption and operating costs.

[0041] First, the defined oil and gas industry chain task graph is decomposed into hierarchical tasks.

[0042] You can obtain the task set T of the oil and gas industry chain, , where task t i Represents a specific business operation (such as exploration, mining, etc.).

[0043] In one embodiment, the task types include one or any combination of exploration, production, processing, and transportation. The oil and gas industry chain includes multiple complex links, such as exploration, production, processing, and transportation, each of which involves a large number of resource allocation and task planning issues.

[0044] In one embodiment, the task graph is decomposed according to the type of task in the task graph to generate multiple subtask graphs, which may include: decomposing the task graph according to the type of task in the task graph to generate one or any combination of an exploration subtask graph, a mining subtask graph, a processing subtask graph and a transportation subtask graph.

[0045] Construct a task graph G, where V is a set of nodes representing tasks; E is a set of edges representing the dependencies between tasks. Decompose the task graph G into multiple sub-task graphs G1, G2, ..., G i ,…,G k ,Each subtask graph represents an independent task module, such as exploration ,module, mining module, and so on.

[0046] In one embodiment, the exploration subtask graph includes one or any combination of geological survey subtasks, drilling design subtasks, and sample analysis subtasks; the mining subtask graph includes one or any combination of equipment deployment subtasks, production monitoring subtasks, and operation adjustment subtasks. i Further decompose the task to obtain a subtask set .

[0047] The following describes the constructed Bayesian program induction model and how to select the optimal task sequence based on the generated task sequence.

[0048] In one embodiment, the preset prior probability algorithm is:

[0049] p(π) ∝ ;

[0050] p(ρ i ) ∝ ;

[0051] Where π is the task sequence; p(π) is the probability distribution of the task sequence, reflecting the priority and / or feasibility of the task plan; DL(π) represents the minimum description length of the task sequence π, which is proportional to ρ i is the i-th task in the task sequence; p(ρ i ) is the probability distribution of the i-th task; λ is the regularization parameter used to control the preference for reusing the i-th task; is an exponential function used to calculate the probability of the i-th task.

[0052] Define the probability distribution of task sequences p(π), where π represents a task sequence. This distribution reflects the priority or probability of different task plans. Introduce the minimum description length (MDL) prior. The MDL principle tends to choose concise and efficient plans to reduce the overall description length. Define the reuse bias prior p(ρi ), λ is a regularization parameter used to control the preference for reusing the i-th task. Frequently used tasks have a higher probability of being reused. In short, p(ρ i ) is designed to encourage the system to select and reuse tasks that have been proven to be effective and efficient, thereby improving the efficiency and effectiveness of the overall task sequence. By adjusting the value of λ, the degree of this bias can be controlled to suit different application scenarios.

[0053] Generate candidate task sequences based on the task graph G and prior distribution p(π) Each candidate task sequence is a feasible order of task execution.

[0054] In one embodiment, the preset posterior probability algorithm is:

[0055] ;

[0056] ;

[0057] in, is a normalization constant; π' is a candidate task sequence; Π is a set of candidate task sequences, .

[0058] Calculate the posterior probability of each candidate task sequence. This step evaluates the effectiveness of each candidate task sequence through Bayesian inference, taking into account the success rate and prior distribution of the tasks. π' represents any candidate task sequence, which is used to compare with other possible task sequences to determine which one is the optimal execution plan. Among them, p(π) can be defined by combining the minimum description length prior and the reuse bias prior. Specifically, for a task sequence π containing multiple tasks, its prior probability p(π) can be calculated based on the probability p(ρ i ) to adjust.

[0059] In one embodiment, comparing the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph and determining the candidate task sequence with the highest posterior probability as the final task sequence corresponding to the subtask graph may include: comparing the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph and determining the candidate task sequence π* with the highest posterior probability as the final task sequence corresponding to the subtask graph;

[0060] ;

[0061] in, is to make Get the variable point corresponding to the maximum value.

[0062] The task sequence π* with the highest posterior probability is selected as the final execution plan, and the optimal task execution order is found by comparing the posterior probabilities of all candidate sequences.

[0063] Figure 2 This is a flowchart of a specific example of an oil and gas industry chain decision optimization method provided in an embodiment of the present invention. Figure 2 As shown, in one embodiment, the oil and gas industry chain decision optimization method further includes: step 201, obtaining a task execution order;

[0064] The default resource allocation function is:

[0065] ;

[0066] in, is the real-time resource status, indicating the resource status at time t; S is the task execution order, indicating the position of the task in the task sequence; is the real-time resource status after allocation; f is a resource allocation function that is adjusted according to task requirements and real-time resource status.

[0067] In step 101 , a real-time resource state R(t) is defined, which represents the resource state at time t. Resources may include manpower, equipment, funds, etc.

[0068] In step 201, define the task execution order , s j ∈T represents the position of the task in the task sequence. In the method, the execution order of step 201 only needs to be before step 107.

[0069] Based on the task execution sequence S and the real-time resource status R(t), a preset resource allocation function is defined to dynamically adjust resource allocation. f is a resource allocation function that adjusts based on task requirements and current resource status. For example, if a task requires more human resources, the system automatically allocates the corresponding manpower.

[0070] To optimize the decision-making process for an oil and gas exploration project, the specific steps are as follows:

[0071] (1) In the data input interface, the user inputs the task graph G of exploration tasks (geological survey, drilling design, sample analysis) and mining tasks (equipment deployment, production monitoring); the dependencies between tasks are verified to ensure that the geological survey is completed before the drilling design.

[0072] (2) In the task decomposition part, the task graph G is decomposed into the exploration module and the mining module. The geological survey task in the exploration module can be further decomposed into two tasks: data collection and data analysis.

[0073] (3) In the Bayesian program induction module, the system generates candidate task sequences.

[0074] Sequence 1: geological survey data collection → drilling design → data sample analysis;

[0075] Sequence 2: Drilling design data collection → geological survey → data sample analysis.

[0076] Calculate the posterior probability of each candidate task sequence and select the optimal task sequence as sequence 1.

[0077] (4) Monitor the current resource status in real time and increase human resources in the geological survey phase to speed up the progress based on the task execution sequence and real-time resource status.

[0078] (5) Provide feedback and adjust the task execution sequence and resource allocation based on actual conditions, regenerate a new task sequence and adjust resource allocation.

[0079] The embodiment of the present invention further proposes an oil and gas industry chain decision optimization device, the principle of which is similar to the oil and gas industry chain decision optimization method, and will not be repeated here.

[0080] Figure 3 Schematic diagram of an oil and gas industry chain decision optimization device provided in an embodiment of the present invention, such as Figure 3 As shown, the oil and gas industry chain decision optimization device may include:

[0081] Acquisition module 301 is used to acquire a task set of the oil and gas industry chain, dependencies between tasks, real-time resource status, and preset resource allocation functions; a task set includes multiple tasks; a task is a business operation performed in the oil and gas industry chain;

[0082] A task graph generation module 302 is used to generate a task graph based on a task set and dependencies between tasks;

[0083] A subtask graph generating module 303 is used to decompose the task graph according to the types of tasks in the task graph to generate multiple subtask graphs;

[0084] The candidate task sequence generation module 304 is configured to generate a plurality of candidate task sequences for each subtask graph according to a preset prior probability algorithm; the preset prior probability algorithm is configured based on a minimum description length criterion and a reuse bias prior algorithm; the reuse bias prior algorithm is configured to screen tasks and / or determine the execution order of tasks in the candidate task sequence according to their execution frequency and execution success rate;

[0085] The posterior probability determination module 305 is used to determine the posterior probability of each candidate task sequence according to a preset posterior probability algorithm; the preset posterior probability algorithm is set according to the Bayesian inference algorithm;

[0086] The final task sequence determination module 306 is used to compare the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph, and determine the candidate task sequence with the highest posterior probability as the final task sequence corresponding to the subtask graph;

[0087] The resource allocation module 307 is used to allocate real-time resources according to the final task sequence, the real-time resource status and the preset resource allocation function.

[0088] In one embodiment, the type of task includes one or any combination of an exploration task, a mining task, a processing task, and a transportation task.

[0089] In one embodiment, the subtask graph generation module 303 is specifically configured to:

[0090] According to the types of tasks in the task graph, the task graph is decomposed to generate one or any combination of exploration subtask graph, mining subtask graph, processing subtask graph and transportation subtask graph.

[0091] In one embodiment, the exploration subtask graph includes one or any combination of a geological survey subtask, a drilling design subtask, and a sample analysis subtask;

[0092] The mining subtask graph includes one or any combination of equipment deployment subtasks, production monitoring subtasks, and operation adjustment subtasks.

[0093] In one embodiment, the preset prior probability algorithm is:

[0094] p(π) ∝ ;

[0095] p(ρ i ) ∝ ;

[0096] Where π is the task sequence; p(π) is the probability distribution of the task sequence, reflecting the priority and / or feasibility of the task plan; DL(π) represents the minimum description length of the task sequence π, which is proportional to ρ i is the i-th task in the task sequence; p(ρ i ) is the probability distribution of the i-th task; λ is the regularization parameter used to control the preference for reusing the i-th task; is an exponential function used to calculate the probability of the i-th task.

[0097] In one embodiment, the preset posterior probability algorithm is:

[0098] ;

[0099] ;

[0100] in, is a normalization constant; π' is a candidate task sequence; Π is a set of candidate task sequences, .

[0101] In one embodiment, the final task sequence determination module 306 is specifically configured to:

[0102] Compare the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph, and select the candidate task sequence with the highest posterior probability. Determining a final task sequence corresponding to the subtask graph;

[0103] ;

[0104] in, is to make Get the variable point corresponding to the maximum value.

[0105] Figure 4 This is a schematic diagram of a specific example of an oil and gas industry chain decision optimization device provided in an embodiment of the present invention, such as Figure 4 As shown, in one embodiment, the oil and gas industry chain decision optimization device further includes: a task execution sequence acquisition module 401, which is used to acquire the task execution sequence;

[0106] The default resource allocation function is:

[0107] ;

[0108] in, is the real-time resource status, indicating the resource status at time t; S is the task execution order, indicating the position of the task in the task sequence; is the real-time resource status after allocation; f is a resource allocation function that is adjusted according to task requirements and real-time resource status.

[0109] Compared with the technical solution for oil and gas industry chain decision optimization in the prior art, the embodiment of the present invention obtains the task set of the oil and gas industry chain, the dependency relationship between tasks, the real-time resource status and the preset resource allocation function; the task set includes multiple tasks; the task is a business operation performed in the oil and gas industry chain; a task graph is generated according to the task set and the dependency relationship between tasks; the task graph is decomposed according to the type of task in the task graph to generate multiple subtask graphs; multiple candidate task sequences are generated for each subtask graph according to a preset prior probability algorithm; the preset prior probability algorithm is set according to the minimum description length criterion and the reuse bias prior algorithm; the reuse bias The a priori algorithm is used to screen tasks and / or determine the execution order of tasks in the candidate task sequence according to the execution frequency and execution success rate of the tasks; the posterior probability of each candidate task sequence is determined according to the preset posterior probability algorithm; the preset posterior probability algorithm is set according to the Bayesian inference algorithm; the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph are compared, and the candidate task sequence with the highest posterior probability is determined as the final task sequence corresponding to the subtask graph; real-time resources are allocated according to the final task sequence, real-time resource status and preset resource allocation function, which can improve the decision-making efficiency of the oil and gas industry chain and reduce resource consumption and operating costs.

[0110] An embodiment of the present invention provides a decision-making optimization method for the oil and gas industry chain based on hierarchical planning and grammatical induction. This method can effectively improve decision-making efficiency within the oil and gas industry chain and reduce resource consumption and operating costs. By decomposing complex decision-making problems into hierarchical subtasks and utilizing a grammatical induction model to generate and optimize task sequences, an efficient and cost-effective decision-making process is achieved. The system architecture design enables this method to be easily applied in real-world scenarios, enhancing the overall competitiveness and economic benefits of oil and gas companies.

[0111] An embodiment of the present invention further provides a computer device, Figure 5 This is a schematic diagram of a computer device in an embodiment of the present invention. The computer device 500 includes a memory 510, a processor 520, and a computer program 530 stored in the memory 510 and executable on the processor 520. When the processor 520 executes the computer program 530, the above-mentioned oil and gas industry chain decision optimization method is implemented.

[0112] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, which implements the above-mentioned oil and gas industry chain decision optimization method when executed by a processor.

[0113] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the above-mentioned oil and gas industry chain decision optimization method.

[0114] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0116] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0118] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A decision optimization method for an oil and gas industry chain, characterized in that: include: Obtaining a task set of the oil and gas industry chain, dependencies between tasks, real-time resource status, and a preset resource allocation function; the task set includes multiple tasks; The tasks are business operations performed in the oil and gas industry chain; Generate a task graph based on the task set and the dependencies between tasks; According to the types of tasks in the task graph, the task graph is decomposed to generate multiple subtask graphs; Generate multiple candidate task sequences for each subtask graph according to a preset prior probability algorithm; the preset prior probability algorithm is set according to a minimum description length criterion and a reuse bias prior algorithm; the reuse bias prior algorithm is used to screen tasks and / or determine the execution order of tasks in the candidate task sequence according to the execution frequency and execution success rate of the tasks; Determine the posterior probability of each candidate task sequence according to a preset posterior probability algorithm; the preset posterior probability algorithm is set according to the Bayesian inference algorithm; Comparing the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph, and determining the candidate task sequence with the highest posterior probability as the final task sequence corresponding to the subtask graph; Allocate real-time resources according to the final task sequence, real-time resource status and preset resource allocation function.

2. The method according to claim 1, wherein The types of tasks include one or any combination of exploration tasks, mining tasks, processing tasks and transportation tasks.

3. The method according to claim 2, wherein According to the type of tasks in the task graph, the task graph is decomposed to generate multiple subtask graphs, including: According to the types of tasks in the task graph, the task graph is decomposed to generate one or any combination of exploration subtask graph, mining subtask graph, processing subtask graph and transportation subtask graph.

4. The method according to claim 3, wherein The exploration subtask graph includes one or any combination of a geological survey subtask, a drilling design subtask, and a sample analysis subtask; The mining subtask graph includes one or any combination of equipment deployment subtasks, production monitoring subtasks, and operation adjustment subtasks.

5. The method according to claim 1, wherein The preset prior probability algorithm is: p(π) ∝ ； p(r i ) ∝ ; Where π is the task sequence; p(π) is the probability distribution of the task sequence, reflecting the priority and / or feasibility of the task plan; DL(π) represents the minimum description length of the task sequence π, which is proportional to ρ i is the i-th task in the task sequence; p(ρ i ) is the probability distribution of the i-th task; λ is the regularization parameter used to control the preference for reusing the i-th task; is an exponential function used to calculate the probability of the i-th task.

6. The method according to claim 1, wherein The preset posterior probability algorithm is: ； ； in, is a normalization constant; π' is a candidate task sequence; Π is a set of candidate task sequences, .

7. The method according to claim 6, wherein Comparing the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph and determining the candidate task sequence with the highest posterior probability as the final task sequence corresponding to the subtask graph includes: Compare the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph, and select the candidate task sequence with the highest posterior probability. Determining a final task sequence corresponding to the subtask graph; ； in, is to make Get the variable point corresponding to the maximum value.

8. The method according to claim 1, wherein Also includes: Get the task execution order; The preset resource allocation function is: ； in, is the real-time resource status, indicating the resource status at time t; S is the task execution order, indicating the position of the task in the task sequence; is the real-time resource status after allocation; f is a resource allocation function that is adjusted according to task requirements and real-time resource status.

9. An oil and gas industry chain decision optimization device, characterized in that: include: An acquisition module is used to acquire a task set of the oil and gas industry chain, dependencies between tasks, real-time resource status, and a preset resource allocation function; the task set includes multiple tasks; and the tasks are business operations performed in the oil and gas industry chain; A task graph generation module, configured to generate a task graph based on the task set and the dependencies between the tasks; A subtask graph generation module is used to decompose the task graph according to the types of tasks in the task graph and generate multiple subtask graphs; A candidate task sequence generation module is configured to generate a plurality of candidate task sequences for each subtask graph according to a preset prior probability algorithm; the preset prior probability algorithm is set according to a minimum description length criterion and a reuse bias prior algorithm; the reuse bias prior algorithm is configured to screen tasks and / or determine the execution order of tasks in the candidate task sequence according to their execution frequency and execution success rate; A posterior probability determination module is used to determine the posterior probability of each candidate task sequence according to a preset posterior probability algorithm; the preset posterior probability algorithm is set according to the Bayesian inference algorithm; A final task sequence determination module is used to compare the posterior probabilities of multiple candidate task sequences corresponding to each subtask graph, and determine the candidate task sequence with the highest posterior probability as the final task sequence corresponding to the subtask graph; The resource allocation module is used to allocate real-time resources according to the final task sequence, real-time resource status and preset resource allocation function.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

12. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

Images