A method for identifying and trending well test evaluation parameters of metamorphic reservoir damage

Abstract

This invention discloses a method for identifying and trend-judging well test evaluation parameters for metamorphic reservoir damage, comprising the following steps: petroleum industry-specific literature retrieval and screening; construction of a multi-dimensional analysis system for the petroleum industry; preprocessing of petroleum industry-customized literature data; quantitative-qualitative coupled analysis for the petroleum industry; and multi-source verification for the petroleum industry. By introducing a multivariate meta-analysis method and constructing a specific evaluation system, this invention achieves intelligent identification and accurate judgment of metamorphic reservoir damage parameters and their evolution trends, effectively improving the accuracy and foresight of damage assessment. It breaks through the traditional three major methods of bibliometrics, text mining, and expert evaluation, effectively avoiding the distinction between general and petroleum-specific terms, filling the research gap in well test evaluation methods for metamorphic reservoir damage, and possessing strong innovation and industry-leading significance, with predictive and guiding value for future related technological developments.

Description

Technical Field

[0001] This invention belongs to the field of oil and gas field exploration and development technology, and in particular relates to a method for identifying and trend judging well test evaluation parameters of metamorphic rock reservoir damage. Background Technology

[0002] Efficient development of oil and gas resources is a core guarantee of national energy security, and unconventional oil and gas resources such as metamorphic rocks have become the main development targets of the petroleum industry. Due to their nanoscale pore structure and high stress sensitivity, metamorphic reservoirs are highly susceptible to reservoir damage problems such as artificial fracture blockage, water lock-in, and poor proppant flowback during hydraulic fracturing and drainage operations. According to petroleum industry statistics, the initial production capacity reduction rate due to reservoir damage in domestic metamorphic gas fields exceeds 60%, far higher than that of conventional oil and gas reservoirs. Well testing evaluation is a core technical means for diagnosing damage in metamorphic reservoirs.

[0003] Currently, there are three major pain points in related research: I. Traditional meta-analysis methods are not adapted to the characteristics of the petroleum industry, and general bibliometric logic cannot accurately identify petroleum-specific concerns such as artificial fracture damage and fracturing fluid flowback. Second, existing analytical schemes lack quantitative judgment standards, and the shift in research paradigms relies solely on qualitative descriptions, which cannot provide accurate basis for engineering decision-making. Third, the verification process relied solely on literature data without combining it with on-site engineering data, resulting in analysis results that were out of touch with the actual needs of the oilfield.

[0004] Therefore, current reservoir damage research in the petroleum industry mostly focuses on single well types or single damage types, lacking systematic assessment methods that cross literature and stages. Furthermore, meta-analysis techniques in general fields have not been customized for the petroleum industry's terminology system and technological iteration patterns, resulting in a disconnect between the technological framework and engineering needs, making it difficult to support the efficient development of metamorphic oil and gas fields. Summary of the Invention

[0005] The problem this invention aims to solve is to provide a method for identifying and judging the trend of well test evaluation parameters for metamorphic reservoir damage. This method constructs a meta-analysis framework specific to the petroleum industry, adapting to the terminology and technical logic of the oil and gas field; designs a quantitative formula specific to the petroleum industry to achieve accurate determination of research trends; and establishes a multi-source verification system of literature and field engineering to ensure the engineering applicability of the analysis results, thus upgrading from general literature analysis to petroleum engineering-specific judgment.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by this invention is: a method for identifying and trend judging well test evaluation parameters of metamorphic rock reservoir damage, comprising the following steps: S1: Petroleum industry-specific literature retrieval and screening; S2: Construction of a multi-dimensional analysis system for the petroleum industry; S3: Preprocessing of customized literature data based on the petroleum industry; S4: Quantitative-Qualitative Coupling Analysis of the Petroleum Industry; S5: Multi-source verification in the petroleum industry.

[0007] Furthermore, S1 includes the following steps: S11: Determine the search terms and supplement them with petroleum engineering-specific terms in addition to the basic search terms to ensure that the literature focuses on the oil and gas exploration and development scenario; S12: Database determined, limited to petroleum industry-specific databases, excluding literature on non-oil and gas exploration and development; S13: Screening criteria were determined, and papers from top journals in the petroleum industry were selected to ensure the engineering practicality and academic authority of the sample database.

[0008] Furthermore, step S2 includes the following steps: S21: The influence dimension is determined by statistically analyzing the citation data of core journals in the petroleum industry and excluding general citations from non-industry journals; S22: The research content dimensions are determined, and the six major elements are all exclusive to metamorphic rock oil and gas exploration and development. The operational technology element is locked to the exclusive technology of petroleum engineering. S23: The time dimension is determined, with the publication date of the first paper on reservoir contamination well test evaluation in 1984 as the starting point, which aligns with the timeline of technological evolution in the petroleum industry.

[0009] Furthermore, step S3 includes the following steps: S31: Improve the SimHash text deduplication algorithm by assigning a weight of 1.5-2.0 to core terms in petroleum literature, while assigning a low weight to general terms, thus solving the defect of the general SimHash algorithm that cannot distinguish between core petroleum terms and general terms. S32: Standardization of petroleum engineering terminology, unification of parameter units in the petroleum industry, elimination of terminological ambiguity, and formation of unified data standards for the petroleum industry. S33: Lightweight professional dictionary for the petroleum industry.

[0010] Furthermore, in step S31, the transformation relationship between text similarity and text feature vectors is as follows: Where: cosθ is the text similarity, which is dimensionless; i is the i-th dimension, which is dimensionless; , A represents the feature vectors of two documents, which are dimensionless; i B iis the weight value of the i-th dimension in the feature vector, which is dimensionless; n is the number of dimensions of the feature vector, which is also dimensionless; cosθ is usually set as a threshold to determine similar text and remove duplicates.

[0011] Furthermore, S4 includes the following steps: S41: The formula for calculating the weight specific to the petroleum industry is as follows. Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; W ij C represents the comprehensive weight of the j-th parameter in the i-th dimension, which is dimensionless; ij This parameter represents the standardized citation frequency of the corresponding document; T ij α is the time decay weight; α is the citation frequency weight coefficient, which is dimensionless and ranges from [0.6, 0.8]. In the above formula, α takes the value of 0.7. S42: The formula for quantifying paradigm shift is: Where: D is the Euclidean distance between the two research focuses, dimensionless; i is the i-th dimension, dimensionless; W i1 W i2 is the comprehensive weight of the i-th core dimension in the two stages, dimensionless; m is the number of core dimensions, m=6 in the above formula; when D≥0.5, it is determined that a significant paradigm shift has occurred in the oil industry.

[0012] Furthermore, in S41, the formula for calculating the normalized citation frequency of a document is as follows: Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; C ij c represents the standardized citation frequency of the document with the j-th parameter in the i-th dimension, which is dimensionless; ij The actual citation frequency is dimensionless; max(c ij ) represents the maximum citation frequency under the j-th parameter in the i-th dimension, which is dimensionless.

[0013] Furthermore, in S41, the formula for calculating the time decay weight is as follows: Where: t0 is the latest publication time of the document, T; t ij T represents the average publication time of the literature for this parameter; k is the decay coefficient, k=0.03; e is the natural constant. T ij The formula for calculating the time decay weight is: Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; t0 is the publication time of the latest document, T; t ij T represents the average publication time of the literature for this parameter; e is a natural constant.

[0014] Furthermore, S5 includes the following steps: S51: Verify at the engineering level by comparing the conclusions drawn from the analysis with the results of on-site diagnosis to verify the engineering adaptability of the analysis results; S52: Verify the accuracy of the identification of concerns at the literature level; S53: Generate a multi-source verification matching degree comparison chart for the petroleum industry to provide a basis for engineering decision-making in metamorphic rock oil and gas fields.

[0015] The advantages and positive effects of this invention are: 1. This invention is the first to modify meta-analysis technology specifically for the petroleum industry, proposing improved SimHash algorithm, petroleum industry weight formula and other exclusive technical modules. Compared with general meta-analysis methods, it improves the accuracy of identifying damage concerns in metamorphic rock reservoirs by more than 20% and has significant non-obviousness. 2. The multi-source verification system of this invention ensures that the analysis results are highly consistent with the needs of oilfield engineering. The generated evolution map has been verified by engineering parameters of the Bozhong 19-6 metamorphic gas field and has clear engineering application value. 3. The quantitative formula and multi-source verification of this invention solve the shortcomings of traditional analysis which is mainly qualitative. The method has clear verification benchmarks and boundaries for the conclusions that the accuracy of identifying core concerns and the matching degree between authoritative reviews and field data both exceed 95%, and the data credibility is high. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the overall process of an embodiment of the present invention.

[0017] Figure 2 This is a schematic diagram of the weight allocation of petroleum terms in the improved SimHash algorithm according to an embodiment of the present invention.

[0018] Figure 3 This is a hierarchical path diagram of a lightweight professional dictionary for the petroleum industry, as described in an embodiment of the present invention.

[0019] Figure 4 This is a graph showing the quantitative determination results of the research paradigm shift in the petroleum industry according to an embodiment of the present invention.

[0020] Figure 5 This is a comparison chart of the matching degree of multi-source verification in the petroleum industry according to an embodiment of the present invention. Detailed Implementation

[0021] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] The embodiments of the present invention will be further described below with reference to the accompanying drawings: A method for identifying and trend-judging well test evaluation parameters for metamorphic rock reservoir damage includes the following steps.

[0023] S1: Petroleum industry-specific literature retrieval and screening, identifying petroleum industry-specific terminology, screening high-quality Chinese and English literature that has been peer-reviewed and focuses on oil and gas field engineering scenarios, eliminating general literature that is unrelated to oil and gas development, and determining a petroleum industry-specific analysis sample library.

[0024] Specifically, S1 includes the following steps: S11: Search term determination: In addition to the basic search terms, 100 petroleum engineering-specific terms such as metamorphic reservoir, hydraulic fracturing, and testing are added to ensure that the literature focuses on the oil and gas exploration and development scenario.

[0025] S12: Database selection: Limited to petroleum industry-specific databases, such as CNKI's Petroleum and Natural Gas Industry section and journals under the Petroleum Engineering discipline in Web of Science, excluding literature on non-oil and gas exploration and development fields such as chemical engineering and geology.

[0026] S13: Selection criteria determined: Priority will be given to papers published in top petroleum industry journals such as the "Chinese Journal of Petroleum Science and Engineering" to ensure the engineering practicality and academic authority of the sample database.

[0027] S2: Construction of a multi-dimensional analysis system for the petroleum industry, including three-dimensional analysis of influence, petroleum industry-specific research content, and time.

[0028] Specifically, S2 includes the following steps: S21: Determining the Influence Dimension: Only citation data from core journals in the petroleum industry are counted, excluding general citations from non-industry journals, such as removing citation records of purely theoretical research in university journals.

[0029] S22: Research content dimensions are defined: the six elements are all exclusive to metamorphic rock oil and gas exploration and development. For example, the reservoir and seepage space element focuses on artificial fractures, matrix pores, fracturing tubing, and composite space (different from the general concept of reservoir and seepage space). The operation technology element locks in petroleum engineering exclusive technologies such as fracturing, well testing, integration, and microseismic monitoring linkage.

[0030] S23: Determining the time dimension: Taking the publication date of the first paper on reservoir contamination well test evaluation in 1984 as the starting point, it aligns with the timeline of technological evolution in the petroleum industry.

[0031] S3: Preprocessing of petroleum industry-customized literature data. Specifically, S3 includes the following steps: S31: Improved SimHash text deduplication algorithm: In view of the high concentration of core terms in petroleum literature, core petroleum terms such as hydraulic fracturing and artificial fractures are assigned a weight of 1.5-2.0, while general terms such as research and discussion are assigned a low weight. This solves the defect of the general SimHash algorithm that cannot distinguish between core petroleum terms and general terms, and improves the deduplication accuracy to over 98%.

[0032] The conversion relationship between text similarity and text feature vectors is as follows: Where: cosθ is the text similarity, which is dimensionless; i is the i-th dimension, which is dimensionless; , A represents the feature vectors of two documents, which are dimensionless; i B i is the weight value of the i-th dimension in the feature vector, which is dimensionless; n is the number of dimensions of the feature vector, which is also dimensionless; cosθ is usually set with a threshold (e.g., cosθ≥0.85) to determine similar text and remove duplicates.

[0033] S32: Standardization of Petroleum Engineering Terminology: Unify the units of parameters in the petroleum industry (e.g., unify the unit of permeability as mD, unify the unit of fracture half-length as m), eliminate ambiguity in terminology (e.g., clarify that the tubing string specifically refers to the tubing string used in fracturing operations, distinguishing it from the tubing string used in oil production), and form a unified data standard for the petroleum industry.

[0034] S33: Lightweight Professional Dictionary for the Petroleum Industry: Contains more than 100 petroleum engineering-specific terms, covering six dimensions including lithology and reservoir space. The unconventional lithology subcategory includes 9 categories such as metamorphic rocks and tight sandstone, and the operation technology subcategory includes 19 engineering-specific technologies such as fracturing-well testing integration.

[0035] S4: Quantitative-Qualitative Coupling Analysis of the Petroleum Industry. Specifically, S4 includes the following steps: S41: Calculation formula for weighting specific to the petroleum industry: Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; W ij C represents the comprehensive weight of the j-th parameter in the i-th dimension, which is dimensionless; ijThis parameter represents the normalized citation frequency of the corresponding document. The formula for calculating the normalized citation frequency of a document is as follows: Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; C ij The overall weight of the j-th parameter in the i-th dimension is dimensionless; c ij For actual citation frequency, -; max(c ij ) represents the maximum citation frequency under the i-th dimension and j-th parameter, -;T ij The formula for calculating the time decay weight is: Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; t0 is the publication time of the latest document, T; t ij T represents the average publication time of the literature for this parameter; k is the decay coefficient, k=0.03 to match the 5-8 year technology iteration cycle of the oil and gas industry); α is the citation frequency weighting coefficient, dimensionless, with a value range of [0.6,0.8], and α takes the value of 0.7; e is the natural constant.

[0036] T ij The formula for calculating the time decay weight is determined as follows: Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; t0 is the publication time of the latest document, T; t ij T represents the average publication time of the literature for this parameter; e is a natural constant.

[0037] S42: Paradigm shift quantification formula, the calculation formula is as follows: Where: D is the Euclidean distance between the two research focuses, dimensionless; i is the i-th dimension, dimensionless; W i1 W i2 is the comprehensive weight of the i-th core dimension in the two stages, dimensionless; m is the number of core dimensions (m=6 in this embodiment), and when D≥0.5, it is determined that a significant paradigm shift has occurred in the petroleum industry.

[0038] S5: Multi-source validation in the oil industry. Specifically, S5 includes the following steps: S51: Engineering Level: Select field well test data, fracturing operation parameters, and production monitoring data from metamorphic gas fields, compare the conclusion that "artificial fracture blockage is the core damage type" with the field diagnosis results, and verify the engineering applicability of the analysis results.

[0039] S52: Literature Level: Compare the core viewpoints with two authoritative petroleum industry reviews, such as "Characteristics and Evaluation of Metamorphic Buried Hill Oil and Gas Reservoirs", to verify the accuracy of the identification of the focus.

[0040] S53: Generate a multi-source verification matching degree comparison chart for the petroleum industry, providing a theoretical basis for engineering decision-making in metamorphic oil and gas fields. The multi-source verification matching degree comparison chart for the petroleum industry links the focus with the engineering application stages (exploration stage and development stage) of metamorphic gas development technology, directly serving oilfield technology planning.

[0041] The present invention will now be described in detail with reference to specific embodiments: In the Bozhong 19-6 metamorphic gas field of Bohai Oilfield, to better identify and analyze the trends of well test evaluation parameters for metamorphic reservoir damage, 41 articles were obtained through a petroleum industry-specific literature search and screening. Using an improved SimHash algorithm combined with a paradigm shift quantification formula, a timely foundation was laid for the engineering verification of well test evaluation parameters for metamorphic reservoir damage identification and trend analysis. The technical process is as follows: Figure 1 As shown, the specific application process is described below: S1: Petroleum industry-specific literature retrieval and screening.

[0042] Search terminology determination: In addition to the basic search terms, 100 petroleum engineering-specific terms such as metamorphic reservoir, hydraulic fracturing, and testing were added to ensure that the literature focuses on the oil and gas exploration and development scenario.

[0043] Database selection criteria: Limited to petroleum industry-specific databases, such as CNKI's Petroleum and Natural Gas Industry section and journals under the Petroleum Engineering category in Web of Science, excluding literature on non-oil and gas exploration and development fields such as chemical engineering and geology.

[0044] Selection criteria were determined by prioritizing papers published in top petroleum industry journals such as the *Chinese Journal of Petroleum Science and Engineering* to ensure the engineering practicality and academic authority of the sample database.

[0045] The key difference between petroleum industry-specific literature retrieval and screening and general meta-analysis lies in the petroleum industry-specific limitations of the search scope and screening criteria. Ultimately, 41 core journal articles in the petroleum industry were selected, including 16 articles from 1984-2011 (conventional sandstone reservoir research) and 25 articles from 2005-present (metamorphic rock reservoir research). All articles focus on oil and gas field engineering scenarios.

[0046] S2: Construction of a multi-dimensional analysis system for the petroleum industry.

[0047] Influence dimension determination: Only citation data of core journals in the petroleum industry are counted, excluding general citations from non-industry journals, such as removing citation records of purely theoretical research in university journals, to ensure that the influence assessment is in line with the actual situation of the industry.

[0048] The research content dimensions are defined as follows: all six elements are specific to metamorphic rock oil and gas exploration and development. For example, the reservoir and seepage space element focuses on artificial fractures, matrix pores, fracturing tubing, and composite space (different from the general concept of reservoir and seepage space). The operation technology element focuses on petroleum engineering-specific technologies such as fracturing, well testing, integrated operation, and microseismic monitoring linkage.

[0049] The time dimension is determined by taking the publication date of the first paper on reservoir contamination well test evaluation in 1984 as the starting point, which aligns with the timeline of technological evolution in the petroleum industry.

[0050] The impact dimension is determined by the citation frequency of core petroleum journals, the research content dimension by six petroleum-specific elements, and the time dimension by covering 1984 to the present, aligning with the evolution cycle of metamorphic gas development technologies. Through the establishment of this three-dimensional analysis system, we are gradually constructing an engineering-customized framework that reflects the unique characteristics of the petroleum industry in the research content dimension.

[0051] S3: Based on customized literature data preprocessing for the petroleum industry, the process is as follows: S31: Improved SimHash Text Deduplication Algorithm: Addressing the high concentration of core terms in petroleum literature, core petroleum terms such as hydraulic fracturing and artificial fractures are assigned a weight of 1.5-2.0. A diagram illustrating the weight allocation for petroleum terms is shown below. Figure 2 As shown, general terms such as "research" and "discussion" are assigned low weights, which solves the defect of the general SimHash algorithm that cannot distinguish between core petroleum terms and general terms, and improves the deduplication accuracy to over 98%.

[0052] The conversion relationship between text similarity and text feature vectors is as follows: Where: i is the i-th dimension, which is dimensionless; cosθ is the text similarity, which is dimensionless; , A represents the feature vectors of two documents, which are dimensionless; i B i is the weight value of the i-th dimension in the feature vector, which is dimensionless; n is the number of dimensions of the feature vector, which is also dimensionless; cosθ is usually set with a threshold (e.g., cosθ≥0.85) to determine similar text and remove duplicates.

[0053] S32: Standardization of Petroleum Engineering Terminology: Unify the units of parameters in the petroleum industry (e.g., unify the unit of permeability as mD, unify the unit of fracture half-length as m), eliminate ambiguity in terminology (e.g., clarify that the tubing string specifically refers to the tubing string used in fracturing operations, distinguishing it from the tubing string used in oil production), and form a unified data standard for the petroleum industry.

[0054] S33: Lightweight Professional Dictionary for the Petroleum Industry: Contains over 100 petroleum engineering-specific terms, covering six dimensions including lithology and reservoir potential. The unconventional lithology subcategory includes nine categories such as metamorphic rocks and tight sandstone, while the operational technology subcategory includes 19 engineering-specific technologies such as fracturing, well testing, and integrated systems. The specific hierarchical path of this lightweight professional dictionary for the petroleum industry is as follows: Figure 3 As shown. The core innovation achieved through customized document data preprocessing for the petroleum industry lies in the petroleum industry-specific algorithms and dictionary modifications.

[0055] S4: Quantitative-Qualitative Coupling Analysis of the Petroleum Industry.

[0056] S41: Calculation formula for weighting specific to the petroleum industry: Among them: W ij C represents the comprehensive weight of the j-th parameter in the i-th dimension, which is dimensionless; ij This parameter represents the normalized citation frequency of the corresponding document. The formula for calculating the normalized citation frequency of a document is as follows: Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; C ij The standardized citation frequency of the document with the j-th parameter in the i-th dimension, -; c ij For actual citation frequency, -; max(c ij ) represents the maximum citation frequency under the i-th dimension and j-th parameter, -;T ij This represents the time decay weight.

[0057] The formula for calculating the time decay weight is: Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; t0 is the publication time of the latest document, t ij T represents the average publication time of the literature for this parameter; k is the decay coefficient, k=0.03 to match the 5-8 year technology iteration cycle of the oil and gas industry); α is the citation frequency weighting coefficient, dimensionless, with a value range of [0.6,0.8], and α takes the value of 0.7; e is the natural constant.

[0058] T ij The formula for calculating the time decay weight is determined as follows: Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; t0 is the publication time of the latest document, T; t ij T represents the average publication time of the literature for this parameter; e is a natural constant.

[0059] S42: Paradigm shift quantification formula, the calculation formula is as follows: Where: D is the Euclidean distance between the two research focuses, dimensionless; i is the i-th dimension, dimensionless; W i1 W i2 is the comprehensive weight of the i-th core dimension in the two stages, dimensionless; m is the number of core dimensions (m=6 in this embodiment); when D≥0.5, it is determined that a significant paradigm shift has occurred in the petroleum industry.

[0060] The quantitative assessment results of the research paradigm shift in the petroleum industry, obtained through calculations from 41 references, are as follows: Figure 4 As shown.

[0061] ① Calculations using the weighting formula show that the core focus is on integrated fracturing-well testing technology (W=0.82), artificial fracture plugging (W=0.78), and fracturing fluid water lock (W=0.75), which are consistent with the pain points in the Bozhong 19-6 metamorphic gas field. ② The calculation using the paradigm shift formula yields D=0.62≥0.5, indicating a significant paradigm shift in research after 2011 from natural pores in conventional sandstone to artificial fractures in metamorphic rocks, which aligns with the actual technological development in the petroleum industry.

[0062] S5: Quantitative-Qualitative Coupling Analysis of the Petroleum Industry.

[0063] S51: Engineering Level: Select field well test data, fracturing operation parameters, and production capacity monitoring data from the typical metamorphic gas field Bozhong 19-6 in Bohai Oilfield. Compare the conclusion that "artificial fracture blockage is the core damage type" drawn from the analysis with the field diagnosis results to verify the engineering applicability of the analysis results.

[0064] S52: Literature Level: By comparing the core viewpoints of two authoritative petroleum industry reviews, such as "Reservoir Characteristics and Evaluation of Metamorphic Buried Hill Oil and Gas Reservoirs", the accuracy of the identification of the focus points was verified.

[0065] S53: Generate a comparison chart of multi-source verification matching degree in the petroleum industry, as shown below. Figure 5 As shown, this provides a theoretical basis for engineering decisions in metamorphic rock oil and gas fields. It includes a "technology maturity" dimension specific to the petroleum industry, linking the focus to the engineering application stages (exploration and development stages) of metamorphic rock gas development technologies, and directly serving oilfield technology planning.

[0066] In summary, the literature verification shows that the core viewpoints of the two authoritative reviews in the petroleum industry match 98%.

[0067] Engineering verification: The data from field well tests and fracturing parameters of the Bozhong 19-6 metamorphic gas field showed a match rate of more than 95% for the key points.

[0068] By using multi-source verification and map generation in the petroleum industry, we have innovatively constructed a dual-dimensional verification system combining literature and field engineering, which overcomes the limitations of traditional analysis that relies solely on literature verification.

[0069] The advantages and positive effects of this invention are: 1. This invention is the first to modify meta-analysis technology specifically for the petroleum industry, proposing improved SimHash algorithm, petroleum industry weight formula and other exclusive technical modules. Compared with general meta-analysis methods, it improves the accuracy of identifying damage concerns in metamorphic rock reservoirs by more than 20% and has significant non-obviousness. 2. The multi-source verification system of this invention ensures that the analysis results are highly consistent with the needs of oilfield engineering. The generated evolution map has been verified by engineering parameters of the Bozhong 19-6 metamorphic gas field and has clear engineering application value. 3. The quantitative formula and multi-source verification of this invention solve the shortcomings of traditional analysis which is mainly qualitative. The method has clear verification benchmarks and boundaries for the conclusions that the accuracy of identifying core concerns and the matching degree between authoritative reviews and field data both exceed 95%, and the data credibility is high.

[0070] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for identifying and trend-judging well test evaluation parameters of metamorphic rock reservoir damage, characterized in that: Includes the following steps, S1: Petroleum industry-specific literature retrieval and screening; S2: Construction of a multi-dimensional analysis system for the petroleum industry; S3: Preprocessing of customized literature data based on the petroleum industry; S4: Quantitative-Qualitative Coupling Analysis of the Petroleum Industry; S5: Multi-source verification in the petroleum industry.

2. The method for identifying and trend judging well test evaluation parameters of metamorphic rock reservoir damage according to claim 1, characterized in that: S1 includes the following steps: S11: Determine the search terms and supplement them with petroleum engineering-specific terms in addition to the basic search terms to ensure that the literature focuses on the oil and gas exploration and development scenario; S12: Database determined, limited to petroleum industry-specific databases, excluding literature on non-oil and gas exploration and development; S13: Screening criteria were determined, and papers from top journals in the petroleum industry were selected to ensure the engineering practicality and academic authority of the sample database.

3. The method for identifying and trend-judging well test evaluation parameters for metamorphic reservoir damage according to claim 1 or 2, characterized in that: S2 includes the following steps: S21: The influence dimension is determined by statistically analyzing the citation data of core journals in the petroleum industry and excluding general citations from non-industry journals; S22: The research content dimensions are determined, and the six major elements are all exclusive to metamorphic rock oil and gas exploration and development. The operational technology element is locked to the exclusive technology of petroleum engineering. S23: The time dimension is determined, with the publication date of the first paper on reservoir contamination well test evaluation in 1984 as the starting point, which aligns with the timeline of technological evolution in the petroleum industry.

4. The method for identifying and trend-judging well test evaluation parameters for metamorphic reservoir damage according to claim 1 or 2, characterized in that: S3 includes the following steps: S31: Improve the SimHash text deduplication algorithm by assigning a weight of 1.5-2.0 to core terms in petroleum literature, while assigning a low weight to general terms, thus solving the defect of the general SimHash algorithm that cannot distinguish between core petroleum terms and general terms. S32: Standardization of petroleum engineering terminology, unification of parameter units in the petroleum industry, elimination of terminological ambiguity, and formation of unified data standards for the petroleum industry. S33: Lightweight professional dictionary for the petroleum industry.

5. The method for identifying and trend-judging well test evaluation parameters for metamorphic reservoir damage according to claim 4, characterized in that: In step S31, the transformation relationship between text similarity and text feature vectors is as follows: Where: cosθ is the text similarity, which is dimensionless; i is the i-th dimension, which is dimensionless; , A represents the feature vectors of two documents, which are dimensionless; i B i is the weight value of the i-th dimension in the feature vector, which is dimensionless; n is the number of dimensions of the feature vector, which is also dimensionless; cosθ is usually set as a threshold to determine similar text and remove duplicates.

6. The method for identifying and trend-judging well test evaluation parameters for metamorphic reservoir damage according to claim 1 or 2, characterized in that: S4 includes the following steps: S41: The formula for calculating the weight specific to the petroleum industry is as follows. Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; W ij C represents the comprehensive weight of the j-th parameter in the i-th dimension, which is dimensionless; ij This parameter represents the standardized citation frequency of the corresponding document; T ij α is the time decay weight; α is the citation frequency weight coefficient, which is dimensionless and ranges from [0.6, 0.8]. In the above formula, α takes the value of 0.

7. S42: The formula for quantifying paradigm shift is: Where: D is the Euclidean distance between the two research focuses, dimensionless; i is the i-th dimension, dimensionless; W i1 W i2 is the comprehensive weight of the i-th core dimension in the two stages, dimensionless; m is the number of core dimensions, m=6 in the above formula; when D≥0.5, it is determined that a significant paradigm shift has occurred in the oil industry.

7. The method for identifying and trend judging well test evaluation parameters of metamorphic rock reservoir damage according to claim 6, characterized in that: In S41, the formula for calculating the normalized citation frequency of a document is as follows: Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; C ij c represents the standardized citation frequency of the document with the j-th parameter in the i-th dimension, which is dimensionless; ij The actual citation frequency is dimensionless; max(c ij ) represents the maximum citation frequency under the j-th parameter in the i-th dimension, which is dimensionless.

8. The method for identifying and trend judging well test evaluation parameters of metamorphic rock reservoir damage according to claim 6, characterized in that: In step S41, the formula for calculating the time decay weight is as follows: Where: t0 is the latest publication time of the document, T; t ij T represents the average publication time of the literature for this parameter; k is the decay coefficient, k=0.03; e is the natural constant. T ij The formula for calculating the time decay weight is: Where: i is the i-th dimension, dimensionless; j is the j-th parameter, dimensionless; t0 is the publication time of the latest document, T; t ij T represents the average publication time of the literature for this parameter; e is a natural constant.

9. The method for identifying and trend-judging well test evaluation parameters for metamorphic reservoir damage according to claim 1 or 2, characterized in that: S5 includes the following steps: S51: Verify at the engineering level by comparing the conclusions drawn from the analysis with the results of on-site diagnosis to verify the engineering adaptability of the analysis results; S52: Verify the accuracy of the identification of concerns at the literature level; S53: Generate a multi-source verification matching degree comparison chart for the petroleum industry to provide a basis for engineering decision-making in metamorphic rock oil and gas fields.

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