Methods, apparatus, media and equipment for evaluating priority of development of low-permeability oil and gas reservoirs at sea

Abstract

This invention relates to a method, apparatus, medium, and equipment for evaluating the priority of utilization of low-permeability offshore oil and gas reservoirs. The method includes: selecting core reservoir geological parameters of the low-permeability offshore oil and gas reservoir; firstly, using correlation coefficient method, multiple regression method, and coefficient of variation method to complete the sensitivity analysis of the influence of reservoir thickness, depth, abundance, and porosity on utilization rate, clarifying the main controlling geological parameters of utilization rate under different permeability grades; then, constructing a neural network model of permeability, depth, abundance, thickness, and porosity on utilization rate based on deep learning, and analyzing the single-factor cumulative probability distribution curve by combining orthogonal experimental results; finally, establishing a classification chart of utilization rate of low-permeability offshore reservoirs to achieve accurate determination of the priority of utilization of low-permeability offshore oil and gas reservoirs. The method of this invention has high determination accuracy and clarifies the main controlling parameters for different permeability ranges, adapting to the geological characteristics of low-permeability offshore oil and gas reservoirs, and providing data support for the efficient development of low-permeability offshore reserves.

Description

Technical Field

[0001] This invention relates to a method, apparatus, medium, and equipment for evaluating the priority of development of low-permeability oil and gas reservoirs at sea, belonging to the field of offshore oil and gas reservoir development technology. Background Technology

[0002] Offshore low-permeability oil and gas reservoirs are a crucial area for future offshore oil reserve and production increases. However, their poor reservoir properties and strong heterogeneity make it difficult to determine the utilization rate of reserves, which is crucial for efficient development. Existing methods for determining the utilization rate of low-permeability oil and gas reservoirs are mostly based on onshore reservoirs and do not fully consider the geological characteristics of offshore reservoirs. Furthermore, they often employ single analytical methods for parameter studies, failing to identify the main controlling geological parameters for utilization rates under different permeability levels. This results in low accuracy in utilization rate determination and a mismatch with the actual development needs of offshore reservoirs. While there have been attempts to use machine learning models for reservoir parameter prediction, these have not combined orthogonal experiments with deep learning models, nor have they established targeted hierarchical classification maps, making it impossible to achieve rapid and accurate utilization rate determination based on geological parameters. Therefore, there is an urgent need to develop a method adapted to the characteristics of offshore low-permeability oil and gas reservoirs, based on multi-method parameter analysis, deep learning models, and hierarchical classification maps for determining the utilization order. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides a method, apparatus, medium, and equipment for evaluating the priority of development of low-permeability oil and gas reservoirs at sea. The method of this invention has high accuracy and clearly defines the main control parameters for different permeability ranges, adapting to the geological characteristics of low-permeability oil and gas reservoirs at sea, and providing data support for the efficient development of low-permeability reserves at sea.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A method for evaluating the priority of development of low-permeability offshore oil and gas reservoirs includes: S1: Select the core reservoir geological parameters of low-permeability offshore oil and gas reservoirs; S2: Using quantitative analysis methods, calculate the correlation, regression coefficient and coefficient of variation between core reservoir geological parameters and utilization rate, complete the sensitivity analysis of the impact of core reservoir geological parameters on the utilization rate of offshore low-permeability oil and gas reservoirs, and clarify the main controlling geological parameters of utilization rate under different permeability grades; S3: Based on deep learning algorithms, construct a neural network model, use the core reservoir geological parameters as the model input parameters and the utilization rate as the model output parameters, collect geological parameters and corresponding utilization rate data of different low-permeability oil and gas reservoir blocks at sea, expand the analysis sample size, train and test the model, and ensure that the correlation coefficient R after model testing is ≥0.80; S4: Design an orthogonal experiment, taking the core reservoir geological parameters as several experimental factors, setting several levels for each factor, and obtaining several sets of experiments. Combined with permeability grading, several sets of orthogonal experimental results are formed. S5: Combine the results of several sets of orthogonal experiments with the test results of the neural network model to analyze the cumulative probability distribution curve of the single factor of the geological parameters of each core reservoir under different utilization rates, and clarify the impact of the single factor of the geological parameters of each core reservoir on the utilization rate under different numerical ranges. S6: Based on the cumulative probability distribution curve, and combined with the main geological parameters and parameter thresholds of the utilization rate under different permeability levels, establish a classification matrix for the utilization rate of low-permeability offshore reservoirs; S7: For the target offshore low-permeability oil and gas reservoir, first determine the permeability classification through its permeability data, then extract its core reservoir geological parameters, and then match the parameter thresholds under the corresponding permeability classification in the classification map based on the offshore low-permeability reservoir utilization rate classification matrix, and finally determine the utilization rate level and order of the target reservoir.

[0005] The method for evaluating the priority of development of low-permeability oil and gas reservoirs at sea, preferably, includes, in step S1, core reservoir geological parameters including permeability, reservoir thickness, reservoir depth, reservoir abundance, and reservoir porosity.

[0006] The method for evaluating the priority of development of low-permeability offshore oil and gas reservoirs, preferably, includes the following specific steps in step S2: Three quantitative analysis methods—correlation coefficient method, multiple regression method, and coefficient of variation method—were used to calculate the correlation, regression coefficient, and coefficient of variation of reservoir thickness, reservoir depth, reservoir abundance, and reservoir porosity with the utilization rate. This completed the sensitivity analysis of the impact of each parameter on the utilization rate of offshore low-permeability oil and gas reservoirs, and clarified the main controlling geological parameters of the utilization rate under different permeability grades.

[0007] The preferred method for evaluating the priority of development of low-permeability oil and gas reservoirs at sea is preferably defined by permeability classifications of 0-5mD, 5-10mD, and 10-15mD.

[0008] The method for evaluating the priority of utilization of low-permeability oil and gas reservoirs at sea, preferably, is that the utilization rate of 10-15mD is mainly affected by depth and abundance, the utilization rate of 5-10mD is mainly affected by abundance and thickness, and the utilization rate of 0-5mD is mainly affected by porosity and thickness.

[0009] The method for evaluating the priority of development of low-permeability offshore oil and gas reservoirs, preferably, includes the following specific steps in step S4: An orthogonal experiment was designed, with five experimental factors: permeability, reservoir depth, reservoir abundance, reservoir thickness, and reservoir porosity. Each factor had nine levels, resulting in a total of 81 experimental sets. These were combined with three permeability classifications: 0-5mD, 5-10mD, and 10-15mD, to form the 81 orthogonal experimental results.

[0010] A second aspect of the present invention provides a device for evaluating the priority of development of offshore low-permeability oil and gas reservoirs, comprising: The first processing unit is used to select the core reservoir geological parameters of low-permeability offshore oil and gas reservoirs. The second processing unit is used to calculate the correlation, regression coefficient and coefficient of variation between core reservoir geological parameters and utilization rate using quantitative analysis methods, complete the sensitivity analysis of the impact of core reservoir geological parameters on the utilization rate of offshore low-permeability oil and gas reservoirs, and clarify the main controlling geological parameters of utilization rate under different permeability grades. The third processing unit is used to construct a neural network model based on deep learning algorithms. It takes the core reservoir geological parameters as the model input parameters and the utilization rate as the model output parameters. It collects geological parameters and corresponding utilization rate data of different low-permeability oil and gas reservoir blocks at sea, expands the analysis sample size, trains and tests the model, and ensures that the correlation coefficient R after model testing is ≥0.80. The fourth processing unit is used to design orthogonal experiments. The core reservoir geological parameters are used as several experimental factors. Each factor is set with several levels, resulting in several sets of experiments. Combined with permeability grading, several sets of orthogonal experimental results are formed. The fifth processing unit is used to combine the results of several sets of orthogonal experiments with the test results of the neural network model to analyze the cumulative probability distribution curve of the single factor of the geological parameters of each core reservoir under different utilization rates, and to clarify the impact of the single factor of the geological parameters of each core reservoir on the utilization rate under different numerical ranges. The sixth processing unit is used to establish a classification matrix for the utilization rate of low-permeability offshore reservoirs based on the cumulative probability distribution curve and the main geological parameters and parameter thresholds of the utilization rate under different permeability grades. The seventh processing unit is used to determine the permeability grade of the target low-permeability offshore oil and gas reservoir by first using its permeability data, then extracting its core reservoir geological parameters, and then matching the parameter thresholds under the corresponding permeability grade in the classification map based on the offshore low-permeability reservoir utilization rate classification matrix, and finally determining the utilization rate level and order of the target reservoir.

[0011] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for evaluating the priority of use of low-permeability offshore oil and gas reservoirs as described above.

[0012] A fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for evaluating the priority of use of low-permeability oil and gas reservoirs described in any of the above-described methods.

[0013] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention employs three quantitative analysis methods for parameter sensitivity analysis, clarifying the main controlling geological parameters of the utilization rate of low-permeability oil and gas reservoirs at sea under different permeability grades. This avoids the limitations of single-method analysis and makes parameter research more closely aligned with the characteristics of offshore reservoirs.

[0014] 2. This invention combines a deep learning neural network model with orthogonal experiments, achieving a model correlation coefficient of over 0.8, which significantly improves the accuracy of mobilization rate determination. Furthermore, the design of orthogonal experiments enables comprehensive analysis of multiple factors and levels, compensating for the shortcomings of single-model analysis.

[0015] 3. This invention establishes a classification matrix for the utilization rate of low-permeability offshore reservoirs, clarifies the geological parameter thresholds for different utilization rate levels under each permeability level, and realizes rapid and intuitive determination of utilization priority based on geological parameters. It is easy to operate and can be directly applied to the development practice of low-permeability offshore oil and gas reservoirs.

[0016] 4. The method of this invention provides a precise determination of the priority level of utilization for the reserve evaluation and development plan formulation of offshore low-permeability oil and gas reservoirs, which helps to achieve efficient utilization of offshore low-permeability reserves and improve the economic benefits of developing offshore low-permeability oil and gas reservoirs. Attached Figure Description

[0017] Figure 1 A sensitivity analysis chart of the 10-15mD mobilization rate parameter provided in an embodiment of the present invention; Figure 2 This is a sensitivity analysis chart of the 5-10mD mobilization rate parameter provided in this embodiment of the invention; Figure 3 This is a sensitivity analysis chart of the 0-5mD mobilization rate parameter provided in this embodiment of the present invention; Figure 4 This is a cumulative probability distribution curve of each single factor under different mobilization rates provided in this embodiment of the invention; Figure 5 This is a classification matrix diagram of the utilization rate of low-permeability offshore reservoirs provided in this embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention are described clearly and completely below. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," "third," "fourth," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0020] For ease of description, spatial relative terms may be used in the text to describe the relationship of one element or feature relative to another element or feature as shown in the figure. These relative terms include, for example, "inside," "outside," "middle," "outer," "below," "above," etc. Such spatial relative terms are intended to include different orientations of the device in use or operation, other than those depicted in the figure.

[0021] Existing methods for determining the utilization rate of low-permeability oil and gas reservoirs are mostly based on onshore reservoir development, failing to fully consider the geological characteristics of offshore reservoirs. Furthermore, they often employ single analytical methods for parameter studies, without clearly defining the key geological parameters controlling the utilization rate under different permeability levels. This results in low accuracy in utilization rate determination and a mismatch with the actual development needs of offshore reservoirs. While existing technologies have attempted to use machine learning models for reservoir parameter prediction, they have not combined orthogonal experiments with deep learning models, nor have they established targeted hierarchical classification maps, making it impossible to achieve rapid and accurate utilization rate determination based on geological parameters. Therefore, there is an urgent need to develop a method adapted to the characteristics of low-permeability offshore oil and gas reservoirs, based on multi-method parameter analysis, deep learning models, and hierarchical classification maps to determine the utilization order.

[0022] Based on the above-mentioned technical problems, the present invention provides a method, apparatus, medium and equipment for evaluating the priority of utilization of offshore low-permeability oil and gas reservoirs. This method solves the problems of low accuracy and poor adaptability of existing methods, and realizes accurate and rapid determination of the utilization rate of offshore low-permeability oil and gas reservoirs.

[0023] like Figure 1As shown, the method for evaluating the priority of development of low-permeability offshore oil and gas reservoirs provided by this invention includes the following specific steps: S1: Select the core reservoir geological parameters of low-permeability oil and gas reservoirs at sea, including permeability, reservoir thickness, reservoir depth, reservoir abundance, and reservoir porosity. These parameters are key parameters affecting the utilization rate of low-permeability oil and gas reservoirs at sea and can be obtained through conventional technical means such as offshore logging, core testing, and reservoir characterization.

[0024] S2: Three quantitative analysis methods—correlation coefficient method, multiple regression method, and coefficient of variation method—were used to calculate the correlation, regression coefficient, and coefficient of variation of reservoir thickness, reservoir depth, reservoir abundance, reservoir porosity, and utilization rate, respectively, to complete the sensitivity analysis of the impact of each parameter on the utilization rate of low-permeability offshore oil and gas reservoirs. The main controlling geological parameters of utilization rate were identified for different permeability grades (0-5mD, 5-10mD, 10-15mD): the 10-15mD utilization rate is mainly affected by depth and abundance; the 5-10mD utilization rate is mainly affected by abundance and thickness; and the 0-5mD utilization rate is mainly affected by porosity and thickness. Figure 1 , Figure 2 and Figure 3 As shown.

[0025] S3: Based on deep learning algorithms, a neural network model is constructed, using permeability, reservoir depth, reservoir abundance, reservoir thickness, and reservoir porosity as input parameters, and utilization rate as the output parameter. Geological parameters and corresponding utilization rate data of different low-permeability oil and gas reservoir blocks at sea are collected to expand the sample size for analysis. The model is then trained and tested to ensure that the correlation coefficient R after testing is ≥0.80, thus guaranteeing the model's prediction accuracy.

[0026] S4: Design an orthogonal experiment, setting permeability, reservoir depth, reservoir abundance, reservoir thickness and reservoir porosity as 5 experimental factors, with 9 levels for each factor, resulting in a total of 81 sets of experiments. Combined with three permeability classifications of 0-5mD, 5-10mD and 10-15mD, the results of the 81 orthogonal experiments were formed.

[0027] S5: Combine the results of orthogonal experiments with the test results of the neural network model to analyze the cumulative probability distribution curves of each single factor (reservoir depth, reservoir abundance, reservoir thickness, reservoir porosity) under different utilization rates (see...). Figure 4 This clarifies the impact of each individual factor on the mobilization rate under different numerical ranges.

[0028] S6: Based on the cumulative probability distribution curves of each single factor, and combined with the main controlling geological parameters and parameter thresholds under different permeability grades, a classification matrix for the utilization rate of low-permeability offshore reservoirs is established, such as... Figure 5As shown, the utilization rate is divided into three levels: high utilization rate (70%-100%), medium utilization rate (50%-70%), and low utilization rate (<50%), and the geological parameter thresholds corresponding to different utilization rate levels under each permeability classification are clearly defined.

[0029] S7: For the target low-permeability offshore oil and gas reservoir, first determine the permeability classification through its permeability data, then extract its geological parameters such as reservoir depth, reservoir abundance, reservoir thickness, and reservoir porosity. Then, based on the offshore low-permeability reservoir utilization rate classification matrix, match the parameter thresholds under the corresponding permeability classification in the classification chart, and finally determine the utilization rate level and order of the target reservoir.

[0030] The technical solution of the present invention will be described in detail below with reference to specific examples.

[0031] Example 1 Taking a certain offshore low-permeability oil field as the research object, the method of this invention is used to determine its utilization priority level. The steps are as follows: This oil and gas field contains three low-permeability reservoirs. The reservoir geological parameters of the reservoirs obtained by well logging and core testing are shown in Table 1:

[0032] Based on reservoir parameters and the utilization rate classification matrix, it can be seen that reservoir A has a 90% probability of reaching a utilization rate of 50%-70%, reservoir B has a 90% probability of reaching a utilization rate of 0%-50%, and reservoir C has a 90% probability of reaching a utilization rate of 50%-70%. From the perspective of oil and gas reservoir development effectiveness, reservoirs A and C should be prioritized for development before development. From the perspective of the ease of oil and gas reservoir development, reservoir A is shallow and thick, making it easier to develop. Therefore, the reasonable order of development for the three reservoirs in this low-permeability oilfield is finally determined to be: reservoir A, reservoir C, and reservoir B.

[0033] The present invention has the following advantages due to the adoption of the above technical solutions: This invention first uses correlation coefficient method, multiple regression method, and coefficient of variation method to complete the sensitivity analysis of the influence of reservoir thickness, reservoir depth, reservoir abundance, and reservoir porosity on the utilization rate, clarifying the main controlling geological parameters of the utilization rate under different permeability grades; then, based on deep learning, it constructs a neural network model of permeability, reservoir depth, reservoir abundance, reservoir thickness, and reservoir porosity on the utilization rate, and analyzes the single-factor cumulative probability distribution curves in combination with orthogonal experimental results; finally, it establishes a classification map of the utilization rate of low-permeability offshore reservoirs, realizing the accurate determination of the priority utilization of low-permeability offshore oil and gas reservoirs. The method of this invention has high determination accuracy and clarifies the main controlling parameters for different permeability ranges, adapting to the geological characteristics of low-permeability offshore oil and gas reservoirs, and providing data support for the efficient development of low-permeability offshore reserves.

[0034] A second aspect of the present invention provides a device for evaluating the priority of development of offshore low-permeability oil and gas reservoirs, comprising: The first processing unit is used to select the core reservoir geological parameters of low-permeability offshore oil and gas reservoirs. The second processing unit is used to calculate the correlation, regression coefficient and coefficient of variation between core reservoir geological parameters and utilization rate using quantitative analysis methods, complete the sensitivity analysis of the impact of core reservoir geological parameters on the utilization rate of offshore low-permeability oil and gas reservoirs, and clarify the main controlling geological parameters of utilization rate under different permeability grades. The third processing unit is used to construct a neural network model based on deep learning algorithms. It takes the core reservoir geological parameters as the model input parameters and the utilization rate as the model output parameters. It collects geological parameters and corresponding utilization rate data of different low-permeability oil and gas reservoir blocks at sea, expands the analysis sample size, trains and tests the model, and ensures that the correlation coefficient R after model testing is ≥0.80. The fourth processing unit is used to design orthogonal experiments. The core reservoir geological parameters are used as several experimental factors. Each factor is set with several levels, resulting in several sets of experiments. Combined with permeability grading, several sets of orthogonal experimental results are formed. The fifth processing unit is used to combine the results of several sets of orthogonal experiments with the test results of the neural network model to analyze the cumulative probability distribution curve of the single factor of the geological parameters of each core reservoir under different utilization rates, and to clarify the impact of the single factor of the geological parameters of each core reservoir on the utilization rate under different numerical ranges. The sixth processing unit is used to establish a classification matrix for the utilization rate of low-permeability offshore reservoirs based on the cumulative probability distribution curve and the main geological parameters and parameter thresholds of the utilization rate under different permeability grades. The seventh processing unit is used to determine the permeability grade of the target low-permeability offshore oil and gas reservoir by first using its permeability data, then extracting its core reservoir geological parameters, and then matching the parameter thresholds under the corresponding permeability grade in the classification map based on the offshore low-permeability reservoir utilization rate classification matrix, and finally determining the utilization rate level and order of the target reservoir.

[0035] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method for evaluating the priority of use of low-permeability offshore oil and gas reservoirs as described above.

[0036] A fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method for evaluating the priority of use of low-permeability oil and gas reservoirs described in any of the above-described methods.

[0037] This invention is described based on flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to specific embodiments. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of 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, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing device, generate instructions for implementing the flowcharts and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0038] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0039] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications 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 the present invention.

Claims

1. A method for evaluating the priority of exploitation of low-permeability offshore oil and gas reservoirs, characterized in that, include: S1: Select the core reservoir geological parameters of low-permeability offshore oil and gas reservoirs; S2: Using quantitative analysis methods, calculate the correlation, regression coefficient and coefficient of variation between core reservoir geological parameters and utilization rate, complete the sensitivity analysis of the impact of core reservoir geological parameters on the utilization rate of offshore low-permeability oil and gas reservoirs, and clarify the main controlling geological parameters of utilization rate under different permeability grades; S3: Based on deep learning algorithms, construct a neural network model, use the core reservoir geological parameters as the model input parameters and the utilization rate as the model output parameters, collect geological parameters and corresponding utilization rate data of different low-permeability oil and gas reservoir blocks at sea, expand the analysis sample size, train and test the model, and ensure that the correlation coefficient R after model testing is ≥0.80; S4: Design an orthogonal experiment, taking the core reservoir geological parameters as several experimental factors, setting several levels for each factor, and obtaining several sets of experiments. Combined with permeability grading, several sets of orthogonal experimental results are formed. S5: Combine the results of several sets of orthogonal experiments with the test results of the neural network model to analyze the cumulative probability distribution curve of the single factor of the geological parameters of each core reservoir under different utilization rates, and clarify the impact of the single factor of the geological parameters of each core reservoir on the utilization rate under different numerical ranges. S6: Based on the cumulative probability distribution curve, and combined with the main geological parameters and parameter thresholds of the utilization rate under different permeability levels, establish a classification matrix for the utilization rate of low-permeability offshore reservoirs; S7: For the target offshore low-permeability oil and gas reservoir, first determine the permeability classification through its permeability data, then extract its core reservoir geological parameters, and then match the parameter thresholds under the corresponding permeability classification in the classification map based on the offshore low-permeability reservoir utilization rate classification matrix, and finally determine the utilization rate level and order of the target reservoir.

2. The method for evaluating the priority of development of low-permeability offshore oil and gas reservoirs according to claim 1, characterized in that, In step S1, the core reservoir geological parameters include permeability, reservoir thickness, reservoir depth, reservoir abundance, and reservoir porosity.

3. The method for evaluating the priority of development of low-permeability offshore oil and gas reservoirs according to claim 1, characterized in that, Step S2 includes the following specific steps: Three quantitative analysis methods—correlation coefficient method, multiple regression method, and coefficient of variation method—were used to calculate the correlation, regression coefficient, and coefficient of variation of reservoir thickness, reservoir depth, reservoir abundance, and reservoir porosity with the utilization rate. This completed the sensitivity analysis of the impact of each parameter on the utilization rate of offshore low-permeability oil and gas reservoirs, and clarified the main controlling geological parameters of the utilization rate under different permeability grades.

4. The method for evaluating the priority of development of low-permeability offshore oil and gas reservoirs according to claim 3, characterized in that, The penetration rates are graded as follows: 0-5mD, 5-10mD, and 10-15mD.

5. The method for evaluating the priority of development of low-permeability offshore oil and gas reservoirs according to claim 4, characterized in that, The utilization rate of 10-15mD is mainly affected by depth and abundance, the utilization rate of 5-10mD is mainly affected by abundance and thickness, and the utilization rate of 0-5mD is mainly affected by porosity and thickness.

6. The method for evaluating the priority of development of low-permeability offshore oil and gas reservoirs according to claim 5, characterized in that, Step S4 includes the following specific steps: An orthogonal experiment was designed, with five experimental factors: permeability, reservoir depth, reservoir abundance, reservoir thickness, and reservoir porosity. Each factor had nine levels, resulting in a total of 81 experimental sets. These were combined with three permeability classifications: 0-5mD, 5-10mD, and 10-15mD, to form the 81 orthogonal experimental results.

7. A device for evaluating the priority of development of low-permeability offshore oil and gas reservoirs, characterized in that, include: The first processing unit is used to select the core reservoir geological parameters of low-permeability offshore oil and gas reservoirs. The second processing unit is used to calculate the correlation, regression coefficient and coefficient of variation between core reservoir geological parameters and utilization rate using quantitative analysis methods, complete the sensitivity analysis of the impact of core reservoir geological parameters on the utilization rate of offshore low-permeability oil and gas reservoirs, and clarify the main controlling geological parameters of utilization rate under different permeability grades. The third processing unit is used to construct a neural network model based on deep learning algorithms. It takes the core reservoir geological parameters as the model input parameters and the utilization rate as the model output parameters. It collects geological parameters and corresponding utilization rate data of different low-permeability oil and gas reservoir blocks at sea, expands the analysis sample size, trains and tests the model, and ensures that the correlation coefficient R after model testing is ≥0.

80. The fourth processing unit is used to design orthogonal experiments. The core reservoir geological parameters are used as several experimental factors. Each factor is set with several levels, resulting in several sets of experiments. Combined with permeability grading, several sets of orthogonal experimental results are formed. The fifth processing unit is used to combine the results of several sets of orthogonal experiments with the test results of the neural network model to analyze the cumulative probability distribution curve of the single factor of the geological parameters of each core reservoir under different utilization rates, and to clarify the impact of the single factor of the geological parameters of each core reservoir on the utilization rate under different numerical ranges. The sixth processing unit is used to establish a classification matrix for the utilization rate of low-permeability offshore reservoirs based on the cumulative probability distribution curve and the main geological parameters and parameter thresholds of the utilization rate under different permeability grades. The seventh processing unit is used to determine the permeability grade of the target low-permeability offshore oil and gas reservoir by first using its permeability data, then extracting its core reservoir geological parameters, and then matching the parameter thresholds under the corresponding permeability grade in the classification map based on the offshore low-permeability reservoir utilization rate classification matrix, and finally determining the utilization rate level and order of the target reservoir.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for evaluating the priority of use of offshore low-permeability oil and gas reservoirs as described in any one of claims 1-6.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method for evaluating the priority of use of low-permeability oil and gas reservoirs at sea as described in any one of claims 1-6.

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