New methods, devices, media and equipment for improving oil and gas recovery in offshore reservoirs
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-20
- Publication Date
- 2026-08-14
AI Technical Summary
但在现有海上油气藏采收率类比实践中，仍存在诸多技术问题：一是海上油气藏的地质、油藏、流体条件复杂多样，不同油气藏之间的直接类比难度大；二是油气藏相关数据的质量与完整性有限，导致类比过程缺乏充足、可靠的数据依据；三是油气开发技术与工艺不断演变，新老技术的应用差异造成类比过程中出现冲突；四是传统类比多依赖经验方法，存在较强的主观偏差，类比结果的准确性和客观性难以保证
1、本发明构建了标准化、分类化的海上类比油气藏库，通过多维度严格筛选标准保证了库内数据的可靠性和有效性，为采收率类比提供了充足、高质量的数据支撑，解决了传统类比数据质量与完整性有限的问题。
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Abstract
Description
Technical Field
[0001] This invention relates to a novel method, apparatus, medium, and equipment for comparing the recovery rate of offshore oil and gas reservoirs, belonging to the field of offshore oil and gas reservoir development technology. Background Technology
[0002] Oil and gas recovery rate analogy is a key technology for predicting the potential of undeveloped or early-stage oilfields during oilfield development. It provides important basis for investment decisions, reserve assessments, and development strategy formulation, and is a highly empirical and practical technical means. However, in current offshore oil and gas reservoir recovery rate analogy practices, there are still many technical problems: First, the geological, reservoir, and fluid conditions of offshore oil and gas reservoirs are complex and diverse, making direct analogies between different reservoirs difficult; second, the quality and completeness of oil and gas reservoir-related data are limited, resulting in a lack of sufficient and reliable data basis for the analogy process; third, oil and gas development technologies and processes are constantly evolving, and the differences in the application of new and old technologies cause conflicts in the analogy process; fourth, traditional analogies rely heavily on empirical methods, which have strong subjective biases, making it difficult to guarantee the accuracy and objectivity of the analogy results.
[0003] To address the aforementioned issues, there is an urgent need to develop a new data-driven analogy method for offshore oil and gas reservoir recovery rates, which can overcome the limitations of traditional empirical analogies and improve the reliability and scientific validity of recovery rate analogy results. Summary of the Invention
[0004] To address the aforementioned technical problems, this invention provides a novel method, apparatus, medium, and equipment for analogy of offshore oil and gas reservoir recovery rates. This method, with data-driven approaches at its core and combined with physical constraints, solves the pain points of existing analogy methods through a full-process design that includes constructing a standardized analogy library, mining key control factors, establishing a similarity matrix, hierarchical calibration, and re-analysis of low similarity, thereby achieving accurate analogy of offshore oil and gas reservoir recovery rates.
[0005] To achieve the above objectives, the present invention adopts the following technical solution: A novel method for comparing the recovery rate of offshore oil and gas reservoirs includes: S1: Analyze offshore oil and gas field asset data, set multi-dimensional screening criteria to screen oil and gas reservoir data, classify the screened oil and gas reservoirs, and establish a standardized and classified offshore analog oil and gas reservoir database. S2: Based on offshore analog oil and gas reservoirs, conduct mining and weight analysis of the main control factors of recovery rate to determine the main control factors of recovery rate and the specific weight of each factor, and complete the ranking of the main control factors; S3: Based on the main control factors of recovery rate and the specific weights of each factor, the similarity between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir pool is calculated using the analogy coefficient formula. Based on the similarity calculation results, an offshore oil and gas reservoir recovery rate analogy similarity matrix is established. S4: Based on the similarity matrix of recovery rate of offshore oil and gas reservoirs, calculate the similarity coefficient between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir database, classify the similarity according to the similarity coefficient, and determine the range of recovery rate analogy values and preliminary analogy results accordingly. Perform technical and geological calibration on the preliminary analogy results, correct the analogy deviation, and obtain the final recovery rate analogy value.
[0006] The novel method for comparing the recovery rate of offshore oil and gas reservoirs, preferably, further includes step S5: For oil and gas reservoirs with an analogy coefficient in the low similarity range of 0.2 ≤ analogy coefficient < 0.5, a re-analysis is performed by reducing the quantitative analogy conditions to ensure that reliable analogy results are obtained for low similarity oil and gas reservoirs.
[0007] The novel method for analogy of offshore oil and gas reservoir recovery rate, preferably, includes the following multi-dimensional screening criteria in step S1: oil and gas reservoir status, reserve level, recovery rate threshold, recovery rate reliability, drive type, and production time.
[0008] The novel method for comparing the recovery rate of offshore oil and gas reservoirs, preferably, involves classifying the screened oil and gas reservoirs in step S1 to establish a standardized and categorized offshore analog oil and gas reservoir database. Specifically, this includes the following steps: the screened oil and gas reservoirs are classified into seven types based on "physical properties + extraction method," including five types of oil reservoirs: medium-high permeability water injection, medium-high permeability natural energy development, low permeability natural energy development, low permeability water injection, and heavy oil water injection; and two types of gas reservoirs: elastic water-driven gas reservoirs and constant-volume gas reservoirs. A standardized and categorized offshore analog oil and gas reservoir database is thus established.
[0009] The novel method for comparing the recovery rate of offshore oil and gas reservoirs, preferably, includes the following specific steps in step S2: S21: Screening five categories of evaluation parameters that affect the recovery rate of offshore oil and gas reservoirs; S22: Perform independence analysis on the five categories of evaluation parameters, eliminate parameters with correlation, and screen out parameters without correlation to ensure that the main control factors affecting the recovery rate are not correlated in the final selection. S23: Select a matching modeling method based on the differences in data volume among different oil and gas reservoir types: for oil and gas reservoir types with a large amount of data, use deep neural network modeling; for oil and gas reservoir types with a small amount of data, use a BP neural network optimized by genetic algorithm for rapid modeling. S24: Substitute the uncorrelated parameters obtained from the screening into the selected modeling method to construct a data-driven recovery rate prediction model; S25: Design a multi-factor, multi-level orthogonal experiment, with a total of several sets of experiments. Substitute the parameters of each set of experiments into the recovery rate prediction model to predict the recovery rate and provide experimental data for screening the main control factors. S26: Based on the results of orthogonal experiments, a multi-method weight analysis approach combining analysis of variance, analytic hierarchy process, and fuzzy mathematical model is used to perform significance testing and weight calculation on unrelated parameters, determine the main control factors of recovery rate and the specific weights of each factor, and complete the ranking of the main control factors.
[0010] The novel method for analogy of offshore oil and gas reservoir recovery rates, preferably, in step S3, involves the analogy coefficient... The calculation formula is as follows:
[0011] Wherein, γ represents the weight of the influence of various parameters on the recovery rate; x ti and x i These represent a parameter of the target reservoir and the analogous reservoir, respectively. The larger the analogy coefficient, the higher the similarity between the target reservoir and the analogous reservoir. n The number of analogy parameters; i This represents the ordinal number of the analogy parameter involved in the calculation.
[0012] The novel method for comparing the recovery rate of offshore oil and gas reservoirs, preferably, includes the following steps in step S4: S41: Based on the similarity matrix of recovery rate of offshore oil and gas reservoirs, calculate the similarity coefficient between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir database; S42: Divide similarity into three levels according to analogy coefficient, and determine the range of analogy values for recovery rate and the preliminary analogy results accordingly; S43: Perform technical and geological calibration on the preliminary analogy results to correct analogy bias and obtain the final recovery rate analogy value. Technical calibration: Consider the advancement of the development technology to be adopted in the target oilfield, compare the development technology level of the offshore analog oil and gas reservoir, and adjust the recovery rate value range. Geological calibration: Make corrections based on the special geological conditions of the target oilfield, where the recovery rate range of fractured reservoirs is increased by 1%-3%, and the recovery rate range of fault-blocked reservoirs is decreased by 1%-3%.
[0013] The novel method for comparing the recovery rate of offshore oil and gas reservoirs, preferably, includes the following steps in step S5: S51: Remove dynamic data or low-weight parameters, and retain only high-weight main control factors including permeability, porosity, and well density; S52: Reconstruct the analogy matrix of offshore oil and gas reservoir recovery rates by combining qualitative analogy conditions including sedimentation, structure, and driving type. S53: Recalculate the analogy coefficient and recovery rate analogy value according to the method in step S4 to ensure that low similarity oil and gas reservoirs obtain reliable analogy results.
[0014] A second aspect of the present invention provides an analog device for offshore oil and gas reservoir recovery, comprising: The first processing unit is used to analyze offshore oil and gas field asset data, set multi-dimensional screening criteria to screen oil and gas reservoir data, classify the screened oil and gas reservoirs, and establish a standardized and classified offshore analog oil and gas reservoir database. The second processing unit is used to conduct the mining and weight analysis of the main control factors of recovery rate based on offshore analog oil and gas reservoirs, so as to determine the main control factors of recovery rate and the specific weight of each factor, and complete the ranking of the main control factors. The third processing unit is used to calculate the similarity between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir pool based on the main control factors of recovery rate and the specific weights of each factor, using the analogy coefficient formula. Based on the similarity calculation results, an offshore oil and gas reservoir recovery rate analogy similarity matrix is established. The fourth processing unit is used to calculate the analogy coefficient between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir database based on the similarity matrix of the recovery rate of offshore oil and gas reservoirs, classify the similarity according to the analogy coefficient, determine the range of recovery rate analogy values and the preliminary analogy results, perform technical and geological calibration on the preliminary analogy results, correct the analogy deviation, and obtain the final recovery rate analogy value.
[0015] 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 novel method for analogy of offshore oil and gas reservoir recovery described in any of the preceding claims.
[0016] 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 novel method for analogy of offshore oil and gas reservoir recovery described in any of the above-described embodiments.
[0017] The present invention has the following advantages due to the adoption of the above technical solutions: 1. This invention constructs a standardized and categorized offshore analog oil and gas reservoir. Through multi-dimensional and rigorous screening criteria, it ensures the reliability and validity of the data within the reservoir, providing sufficient and high-quality data support for recovery rate analogy and solving the problem of limited quality and completeness of traditional analog data.
[0018] 2. This invention adopts a combination of data-driven and physical constraints. Through independence analysis, targeted modeling, orthogonal experiments and multi-method weight analysis, it accurately identifies the main controlling factors of recovery rate and determines their weights, avoiding the subjective bias of traditional experience-based analogies and improving the scientific nature of the analogies.
[0019] 3. This invention introduces an analogy coefficient to achieve quantitative calculation of similarity, establishes an analogy similarity matrix, and designs a hierarchical value selection strategy based on the analogy coefficient, supplemented by technical calibration and geological calibration, to achieve accurate and personalized recovery rate analogy, effectively solving the problem of difficulty in direct analogy caused by the complex geological conditions of offshore oil and gas reservoirs.
[0020] 4. This invention designs a special re-analogy method for low-similarity oil and gas reservoirs. By removing low-weight parameters and combining qualitative analogy conditions, it achieves effective analogy of low-similarity oil and gas reservoirs, making up for the shortcomings of traditional analogy methods in terms of poor adaptability to special oil and gas reservoirs, and improving the applicability and comprehensiveness of the method.
[0021] 5. This invention effectively solves the analogy conflict problem caused by the evolution of new and old development technologies / processes. By incorporating the advanced nature of the target oilfield's development technology into the analogy consideration through technology calibration, the analogy results are more in line with the actual development plan of the target oilfield, providing a more scientific and reliable basis for investment decisions, reserve assessments and development strategy formulation for offshore oil and gas reservoirs. Attached Figure Description
[0022] Figure 1 This is an analogy matrix diagram of offshore oil and gas reservoir recovery rate provided in an embodiment of the present invention; Figure 2 This is an analogy similarity matrix diagram of low-similarity oil and gas reservoirs provided in this embodiment of the present invention (without development dynamic parameters). Figure 3 This is a low-similarity oil and gas reservoir analogy similarity matrix diagram (without low-weight parameters) provided in this embodiment of the present invention. Figure 4 This is an analogous oil and gas reservoir diagram provided in this embodiment of the invention; Figure 5 Figure a shows the training results of the deep neural network model provided in this embodiment of the present invention, wherein Figure a is the training results of the sample set model, Figure b is the training results of the validation set model, Figure c is the training results of the test set model, and Figure d is the training results of the model for all samples. Figure 6 This is a diagram illustrating the process of analyzing analogical parameter weights using the analytic hierarchy process in this embodiment of the invention. Detailed Implementation
[0023] 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.
[0024] 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.
[0025] 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.
[0026] Existing practices for comparing the recovery rates of offshore oil and gas reservoirs still face numerous technical challenges: First, the geological, reservoir, and fluid conditions of offshore oil and gas reservoirs are complex and diverse, making direct comparisons between different reservoirs difficult. Second, the quality and completeness of reservoir-related data are limited, resulting in a lack of sufficient and reliable data for the comparison process. Third, the continuous evolution of oil and gas development technologies and processes, along with differences in the application of new and old technologies, leads to conflicts during the comparison process. Fourth, traditional comparisons rely heavily on empirical methods, resulting in significant subjective biases and making it difficult to guarantee the accuracy and objectivity of the comparison results.
[0027] Based on the above-mentioned technical problems, the present invention provides a new method, apparatus, medium and equipment for analogy of recovery rate of offshore oil and gas reservoirs. The method is designed with a special re-analysis method for oil and gas reservoirs with low similarity. By removing low weight parameters and combining qualitative analogy conditions, effective analogy of oil and gas reservoirs with low similarity is achieved, which makes up for the shortcomings of traditional analogy methods in poor adaptability to special oil and gas reservoirs and improves the applicability and comprehensiveness of the method.
[0028] like Figure 1 As shown, the novel method for analogy of offshore oil and gas reservoir recovery involved in this invention includes the following specific steps: S1: Constructing offshore analog oil and gas reservoirs A comprehensive review of offshore oil and gas field asset data was conducted, and strict multi-dimensional screening criteria were established to filter oil and gas reservoir data. The specific screening criteria are as follows: (1) Oil and gas reservoir status: producing reservoir; (2) Reserve level: proven reserves; (3) Recovery rate threshold: conventional water injection reservoir > 20%, heavy oil reservoir > 10%, ultra-high permeability reservoir > 30%; (4) Recovery rate reliability: decline method (cumulative production / technically recoverable > 0.6), analogy method, numerical modeling method (cumulative production / technically recoverable > 0.9); (5) Drive type: edge water, bottom water, elastic drive, artificial water injection; (6) Production time: greater than 3 years.
[0029] The selected oil and gas reservoirs are classified into seven types based on "physical properties + extraction method". These include five types of oil reservoirs: medium-high permeability water injection, medium-high permeability natural energy development, low permeability natural energy development, low permeability water injection, and heavy oil water injection; and two types of gas reservoirs: elastic water drive gas reservoirs and constant volume gas reservoirs. A standardized and classified offshore analog oil and gas reservoir database is established.
[0030] S2: Analyze the main controlling factors of oil recovery and the specific weights of each factor. Based on the constructed offshore analog oil and gas reservoir, the main controlling factors of recovery rate are identified and their weights are analyzed. The specific steps are as follows: S21: Screening and Evaluation Parameters Five categories of assessment parameters affecting the recovery rate of offshore oil and gas reservoirs were selected, including: (1) Geological parameters: geological strata, geological structure, sedimentary environment; (2) Reservoir parameters: formation temperature, pressure, burial depth, oil-bearing area, effective thickness, porosity, permeability, oil saturation; (3) Development methods: natural energy development, artificial water injection development; (4) Fluid properties: density, viscosity, volume coefficient, dissolved gas-oil ratio; (5) Well network conditions: well type, well network density, injection-production ratio.
[0031] S22: Independence Analysis Independence analysis was performed on the five categories of evaluation parameters after screening to eliminate parameters with correlation, ensuring that the main control factors affecting the recovery rate selected in the end are uncorrelated and avoiding parameter redundancy from interfering with the modeling results.
[0032] S23: Screening Modeling Methods Based on the differences in data volume among different oil and gas reservoir types, a matching modeling method is selected: for oil and gas reservoir types with abundant data (such as medium-to-high permeability water injection reservoirs and medium-to-high permeability natural energy development reservoirs), deep neural network modeling is adopted to improve model accuracy; for oil and gas reservoir types with limited data, to avoid overfitting of deep learning, a BP neural network optimized by a genetic algorithm is used for rapid modeling.
[0033] S24: Data Modeling Substitute the filtered, irrelevant parameters into the selected modeling method to construct a data-driven recovery prediction model. Taking medium-to-high permeability natural energy development reservoirs as an example, the modeling parameters include core parameters such as crude oil volume factor, dissolved gas-oil ratio, reservoir depth, original formation pressure, and original formation temperature.
[0034] S25: Constructing an orthogonal experiment A 10-factor, 4-level orthogonal experiment was designed, comprising a total of 64 groups of experiments. The parameters of each group of experiments were substituted into the constructed data-driven model to predict the recovery rate, providing experimental data for the screening of the main control factors.
[0035] S26: Data Analysis and Weight Determination Based on the results of orthogonal experiments, a multi-method weight analysis approach combining analysis of variance, analytic hierarchy process (AHP), and fuzzy mathematical model was used to perform significance tests and weight calculations on the parameters, determine the main control factors of recovery rate and the specific weights of each factor, and complete the ranking of the main control factors.
[0036] S3: Establish an analogy matrix for offshore oil and gas reservoir recovery rates Based on the main control factors of recovery rate and the specific weights of each factor determined in step S2, the similarity between the target oil and gas reservoir and each oil and gas reservoir in the analog oil and gas reservoir pool is calculated using the analogy coefficient formula. The formula for calculating the analogy coefficient is as follows:
[0037] Where γ is the weight of the influence of various parameters on the recovery rate, x ti and x i These represent a parameter of the target reservoir and the analogous reservoir, respectively. The larger the analogy coefficient, the higher the similarity between the target reservoir and the analogous reservoir. n The number of analogy parameters; i This represents the ordinal number of the analogy parameter involved in the calculation.
[0038] Based on the similarity calculation results, an analogy similarity matrix for the recovery rate of offshore oil and gas reservoirs is established, such as... Figure 1 As shown, the similarity between the target oil and gas reservoir and the analogous reservoir is quantitatively characterized.
[0039] S4: Recovery Rate Analog Calculation and Classification Calibration S41: Calculation of Analogy Coefficient Based on the analogy similarity matrix of offshore oil and gas reservoir recovery rate, the analogy coefficient between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir database is calculated. The analogy coefficient is a quantitative representation of similarity and reflects the degree of analogy matching between the two.
[0040] S42: Determine the range of values by level The similarity is divided into three levels based on the analogy coefficient, and the corresponding range of analogy values for the recovery rate is determined: High similarity: analogy coefficient ≥ 0.8, the recovery rate ranges from 90% to 100% of the highest similarity analogy value, and the recommended value within this range is taken as the preliminary analogy result; For intermediate similarity: 0.5 ≤ analogy coefficient < 0.8, the analogy value of the corresponding similarity interval is taken as the preliminary analogy result; Low similarity: 0.2 ≤ analogy coefficient < 0.5, analogy parameters need to be changed and analogy needs to be performed again.
[0041] S43: Multidimensional Calibration The preliminary analogy results were technically and geologically calibrated to correct for analogy biases and obtain the final recovery rate analogy values. Technical calibration: Considering the advancement of the development technology to be adopted in the target oilfield, the recovery rate value range was adjusted by comparing it with the development technology level in the analogy library. Geological calibration: Corrections were made based on the specific geological conditions of the target oilfield, with the recovery rate range for fractured reservoirs increased by 1%-3% and the recovery rate range for fault-blocked reservoirs decreased by 1%-3%.
[0042] S5: Re-analysis of low-similarity oil and gas reservoirs For oil and gas reservoirs with analogy coefficients in the low similarity range of 0.2 ≤ analogy coefficient < 0.5, a method of reducing quantitative analogy conditions is used for re-analysis: (1) Remove dynamic data or low-weight parameters, and retain only high-weight main control factors such as permeability, porosity, and well density; (2) Combine qualitative analogy conditions such as sedimentation, structure, and driving type to reconstruct the similarity matrix of offshore oil and gas reservoir recovery rate. Figure 2 , Figure 3 (3) Recalculate the analogy coefficient and recovery rate analogy value according to the S4 method to ensure that reliable analogy results can be obtained for oil and gas reservoirs with low similarity.
[0043] The technical solution of the present invention will be described in detail below with reference to specific examples.
[0044] Example 1 A novel method for comparing the recovery rate of offshore oil and gas reservoirs, applied to the recovery rate prediction of reservoir A, includes the following steps: (1) Constructing an analog offshore oil and gas reservoir database: Data from 252 oilfields and nearly 18,000 oil and gas reservoirs were analyzed. Based on screening criteria, 556 eligible oil and gas reservoirs were selected and categorized into 7 types according to "physical properties + extraction method." An analog offshore oil and gas reservoir database was then established. (See...) Figure 4 .
[0045] (2) Identify the main controlling factors of oil recovery and determine the specific weight of each factor: Five categories of evaluation parameters were selected, including geology, reservoir, development mode, fluid properties, and well network conditions. After eliminating relevant parameters through independence analysis, deep neural network modeling was adopted for medium-to-high permeability natural energy development reservoirs (reservoir type A). Figure 5 A 10-factor, 4-level orthogonal experiment was designed and recovery rate prediction was completed, combining analysis of variance and analytic hierarchy process (AHP). Figure 6 The main controlling factors were determined to be ranked as follows: permeability > well network density > porosity > original formation pressure > crude oil volume factor > effective thickness > crude oil viscosity > oil-bearing area > oil saturation. The specific weights of each factor were obtained, and the details of the weights of each parameter are shown in Table 1.
[0046] Table 1 Parameter Weight Details
[0047] (3) Establish an analogy similarity matrix for the recovery rate of offshore oil and gas reservoirs: Taking reservoir A as the target oil and gas reservoir, the similarity between it and the candidate oil and gas reservoirs in the analogy library is calculated according to the main control factors and weights, and a similarity matrix is established. The candidate oil and gas reservoirs include reservoirs B, C, D, and E. The analogy coefficients are shown in Table 2.
[0048] Table 2 Analogy Coefficients
[0049] (4) Comparison calculation and classification calibration of recovery rate: The comparison coefficient between the target oil and gas reservoir and each candidate oil and gas reservoir is calculated. The comparison coefficient of reservoir B is 0.87 (high similarity), and the preliminary comparison recovery rate is 71%. Combined with the development technology plan and geological conditions of reservoir A, technical calibration and geological calibration are carried out (this oil and gas reservoir has no special fractures / faults, and the geological calibration is not adjusted) to determine the final comparison recovery rate of 71%.
[0050] (5) Comparative verification: The analogy results of this method show that reservoir B is the best analog reservoir with a recovery rate of 71%; the analogy results of expert experience show that reservoir C is the analog reservoir with a recovery rate of 69%. The analogy results of this method are similar to the results of expert experience and are more supported by data, which verifies the reliability of this method.
[0051] This invention effectively solves the analogy conflict problem caused by the evolution of new and old development technologies / processes. By incorporating the advanced nature of the target oilfield's development technology into the analogy consideration through technology calibration, the analogy results are more in line with the actual development plan of the target oilfield, providing a more scientific and reliable basis for investment decisions, reserve assessments and development strategy formulation for offshore oil and gas reservoirs.
[0052] A second aspect of the present invention provides an analog device for offshore oil and gas reservoir recovery, comprising: The first processing unit is used to analyze offshore oil and gas field asset data, set multi-dimensional screening criteria to screen oil and gas reservoir data, classify the screened oil and gas reservoirs, and establish a standardized and classified offshore analog oil and gas reservoir database. The second processing unit is used to conduct the mining and weight analysis of the main control factors of recovery rate based on offshore analog oil and gas reservoirs, so as to determine the main control factors of recovery rate and the specific weight of each factor, and complete the ranking of the main control factors. The third processing unit is used to calculate the similarity between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir pool based on the main control factors of recovery rate and the specific weights of each factor, using the analogy coefficient formula. Based on the similarity calculation results, an offshore oil and gas reservoir recovery rate analogy similarity matrix is established. The fourth processing unit is used to calculate the analogy coefficient between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir database based on the similarity matrix of the recovery rate of offshore oil and gas reservoirs, classify the similarity according to the analogy coefficient, determine the range of recovery rate analogy values and the preliminary analogy results, perform technical and geological calibration on the preliminary analogy results, correct the analogy deviation, and obtain the final recovery rate analogy value.
[0053] 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 novel method for analogy of offshore oil and gas reservoir recovery described in any of the preceding claims.
[0054] 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 novel method for analogy of offshore oil and gas reservoir recovery described in any of the above-described embodiments.
[0055] 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.
[0056] 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.
[0057] 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.
[0058] 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 novel method for comparing the recovery rate of offshore oil and gas reservoirs, characterized in that, include: S1: Analyze offshore oil and gas field asset data, set multi-dimensional screening criteria to screen oil and gas reservoir data, classify the screened oil and gas reservoirs, and establish a standardized and classified offshore analog oil and gas reservoir database. S2: Based on offshore analog oil and gas reservoirs, conduct mining and weight analysis of the main control factors of recovery rate to determine the main control factors of recovery rate and the specific weight of each factor, and complete the ranking of the main control factors; S3: Based on the main control factors of recovery rate and the specific weights of each factor, the similarity between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir pool is calculated using the analogy coefficient formula. Based on the similarity calculation results, an offshore oil and gas reservoir recovery rate analogy similarity matrix is established. S4: Based on the similarity matrix of recovery rate of offshore oil and gas reservoirs, calculate the similarity coefficient between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir database, classify the similarity according to the similarity coefficient, and determine the range of recovery rate analogy values and preliminary analogy results accordingly. Perform technical and geological calibration on the preliminary analogy results, correct the analogy deviation, and obtain the final recovery rate analogy value.
2. The novel method for analogy of offshore oil and gas reservoir recovery rate according to claim 1, characterized in that, It also includes step S5: For oil and gas reservoirs with an analogy coefficient in the low similarity range of 0.2 ≤ analogy coefficient < 0.5, a re-analysis is performed by reducing the quantitative analogy conditions to ensure that reliable analogy results are obtained for low similarity oil and gas reservoirs.
3. The novel method for analogy of offshore oil and gas reservoir recovery rate according to claim 1, characterized in that, In step S1, the multi-dimensional screening criteria include: reservoir status, reserve level, recovery rate threshold, recovery rate reliability, drive type, and production time.
4. The novel method for analogy of offshore oil and gas reservoir recovery rate according to claim 1, characterized in that, In step S1, the screened oil and gas reservoirs are classified to establish a standardized and categorized offshore analog oil and gas reservoir database. Specifically, the screened oil and gas reservoirs are classified into 7 types based on "physical properties + development method". These include 5 types of oil reservoirs: medium-high permeability water injection, medium-high permeability natural energy development, low permeability natural energy development, low permeability water injection, and heavy oil water injection; and 2 types of gas reservoirs: elastic water drive gas reservoirs and constant volume gas reservoirs. A standardized and categorized offshore analog oil and gas reservoir database is established.
5. The novel method for analogy of offshore oil and gas reservoir recovery rate according to claim 1, characterized in that, Step S2 includes the following specific steps: S21: Screening five categories of evaluation parameters that affect the recovery rate of offshore oil and gas reservoirs; S22: Perform independence analysis on the five categories of evaluation parameters, eliminate parameters with correlation, and screen out parameters without correlation to ensure that the main control factors affecting the recovery rate are not correlated in the final selection. S23: Select a matching modeling method based on the differences in data volume among different oil and gas reservoir types: for oil and gas reservoir types with a large amount of data, use deep neural network modeling; for oil and gas reservoir types with a small amount of data, use a BP neural network optimized by genetic algorithm for rapid modeling. S24: Substitute the uncorrelated parameters obtained from the screening into the selected modeling method to construct a data-driven recovery rate prediction model; S25: Design a multi-factor, multi-level orthogonal experiment, with a total of several sets of experiments. Substitute the parameters of each set of experiments into the recovery rate prediction model to predict the recovery rate and provide experimental data for screening the main control factors. S26: Based on the results of orthogonal experiments, a multi-method weight analysis approach combining analysis of variance, analytic hierarchy process, and fuzzy mathematical model is used to perform significance testing and weight calculation on unrelated parameters, determine the main control factors of recovery rate and the specific weights of each factor, and complete the ranking of the main control factors.
6. The novel method for analogy of offshore oil and gas reservoir recovery rate according to claim 1, characterized in that, In step S3, the analogy coefficient The calculation formula is as follows: ； Wherein, γ represents the weight of the influence of various parameters on the recovery rate; x ti and x i These represent a parameter of the target reservoir and the analogous reservoir, respectively. The larger the analogy coefficient, the higher the similarity between the target reservoir and the analogous reservoir. n The number of analogy parameters; i This represents the ordinal number of the analogy parameter involved in the calculation.
7. The novel method for analogy of offshore oil and gas reservoir recovery rate according to claim 1, characterized in that, Step S4 includes the following steps: S41: Based on the similarity matrix of recovery rate of offshore oil and gas reservoirs, calculate the similarity coefficient between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir database; S42: Divide similarity into three levels according to analogy coefficient, and determine the range of analogy values for recovery rate and the preliminary analogy results accordingly; S43: Perform technical and geological calibration on the preliminary analogy results to correct analogy bias and obtain the final recovery rate analogy value. Technical calibration: Consider the advancement of the development technology to be adopted in the target oilfield, compare the development technology level of the offshore analog oil and gas reservoir, and adjust the recovery rate value range. Geological calibration: Make corrections based on the special geological conditions of the target oilfield, where the recovery rate range of fractured reservoirs is increased by 1%-3%, and the recovery rate range of fault-blocked reservoirs is decreased by 1%-3%.
8. The novel method for analogy of offshore oil and gas reservoir recovery rate according to claim 2, characterized in that, Step S5 includes the following steps: S51: Remove dynamic data or low-weight parameters, and retain only high-weight main control factors including permeability, porosity, and well density; S52: Reconstruct the analogy matrix of offshore oil and gas reservoir recovery rates by combining qualitative analogy conditions including sedimentation, structure, and driving type. S53: Recalculate the analogy coefficient and recovery rate analogy value according to the method in step S4 to ensure that low similarity oil and gas reservoirs obtain reliable analogy results.
9. A device for simulating the recovery rate of offshore oil and gas reservoirs, characterized in that, include: The first processing unit is used to analyze offshore oil and gas field asset data, set multi-dimensional screening criteria to screen oil and gas reservoir data, classify the screened oil and gas reservoirs, and establish a standardized and classified offshore analog oil and gas reservoir database. The second processing unit is used to conduct the mining and weight analysis of the main control factors of recovery rate based on offshore analog oil and gas reservoirs, so as to determine the main control factors of recovery rate and the specific weight of each factor, and complete the ranking of the main control factors. The third processing unit is used to calculate the similarity between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir pool based on the main control factors of recovery rate and the specific weights of each factor, using the analogy coefficient formula. Based on the similarity calculation results, an offshore oil and gas reservoir recovery rate analogy similarity matrix is established. The fourth processing unit is used to calculate the analogy coefficient between the target oil and gas reservoir and each oil and gas reservoir in the offshore analog oil and gas reservoir database based on the similarity matrix of the recovery rate of offshore oil and gas reservoirs, classify the similarity according to the analogy coefficient, determine the range of recovery rate analogy values and the preliminary analogy results, perform technical and geological calibration on the preliminary analogy results, correct the analogy deviation, and obtain the final recovery rate analogy value.
10. 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 novel method for analogous recovery of offshore oil and gas reservoirs as described in any one of claims 1-8.
11. 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 novel method for analogous recovery of offshore oil and gas reservoirs as described in any one of claims 1-8.