Bottom water reservoir extraction liquid yield increase well selection method, system, equipment, medium and product

Abstract

The invention belongs to the technical field of oilfield development, and discloses a bottom water reservoir extraction liquid yield increase well selection method, system and equipment, a medium and a product. The method comprises the steps that parameters related to the extraction liquid yield increasing effect are collected to serve as indexes to be selected, and extraction liquid yield increasing well selection related indexes of the bottom water reservoir are selected according to the correlation between the yield increasing effect of an applied extraction liquid well and the parameters of the well; taking the yield increase amplitude as a dependent variable, taking each related index as an independent variable, and determining a weight coefficient of each related index; determining a data classification boundary of each related index; according to the multiple between the expected beneficial variation and the actual beneficial variation of each related index, determining the classification coefficient of each related index classification; and carrying out weighted addition on the classification of each related index according to the classification coefficient, and accumulating results to obtain a comprehensive evaluation index of the liquid extraction well as a basis for selecting the liquid extraction well. Scientific well selection for bottom water reservoir liquid extraction and yield increase is realized by establishing a comprehensive index system.

Description

Technical Field

[0001] This invention belongs to the field of oilfield development technology, specifically relating to a well selection method, system, equipment, medium, and product for improving fluid production in bottom water reservoirs. Background Technology

[0002] As the development of edge-water reservoirs progresses, the problems of edge water intrusion and bottom water coning become unavoidable, leading to increased water cut in oil wells and consequently, a greater decline in production, resulting in oil production failing to meet expectations. Edge-water reservoirs possess certain natural energy reserves, and stable or increased production can be achieved for a certain period by increasing the fluid production rate of oil wells. Fluid extraction is one of the commonly used techniques for enhancing production in edge-water reservoirs.

[0003] Low-permeability edge-water reservoirs exhibit strong heterogeneity. As water cut increases, the dimensionless theoretical production and fluid production index (EPI) curves of most wells show a downward trend. Upon entering the high water-cut stage, the EPI rises slightly, but the production index continues to decline, indicating that the reservoir lacks overall fluid production potential. Implementing fluid production enhancement measures in wells without this potential will lead to intensified edge water intrusion or bottom water coning, accelerating the rise in water cut and further increasing the decline in well production. Therefore, proper well selection is crucial for increasing production in low-permeability edge-water reservoirs.

[0004] Currently, well selection for fluid extraction mainly relies on empirical methods and numerical simulation methods. The former determines the selection based on single or combined indicators such as reservoir thickness, production rate, and recovery degree, which is not comprehensive and results in a low success rate for fluid extraction and production enhancement. The latter, while providing relatively accurate predictions, depends on overall reservoir numerical simulation, which is labor-intensive, time-consuming, and not very practical for field production. Therefore, it is necessary to establish a well selection method that is suitable for the characteristics of low-permeability edge and bottom water reservoirs and can ensure a high success rate and effectiveness.

[0005] Chinese patent publication number CN117540895A, entitled "A Method for Selecting Wells and Layers for Water-Driven Injection Wells in Low-Permeability Turbidite Sandstone Reservoirs," describes a method comprising: Step 1, identifying key factors for improving the water drive index; Step 2, based on Step 1 and combined with dynamic analysis, preliminarily identifying factors influencing production improvement after water drive; Step 3, determining factors influencing water drive efficiency based on the water drive efficiency of the water-driven reservoir; Step 4, preprocessing raw data of the parameters of the influencing factors; Step 5, calculating the average correlation coefficient to obtain the correlation degree; and Step 6, ranking the correlation degrees to determine the primary and secondary relationships of each influencing factor and selecting the optimal well layers for water drive. This method for selecting wells and layers for water-drive in low-permeability turbidite sandstone reservoirs can accurately select wells and layers to rapidly increase reservoir pressure, improve seepage, and enhance the water drive sweep efficiency and oil displacement efficiency, thereby increasing well productivity and reservoir recovery. However, this patent application method cannot be applied to well selection for fluid extraction and production enhancement in bottom-water reservoirs. Summary of the Invention

[0006] The purpose of this invention is to provide a method, system, equipment, medium, and product for well selection for fluid extraction and production enhancement in bottom-water reservoirs. The well selection method of this invention comprehensively considers multiple factors to select wells for fluid extraction and production enhancement in bottom-water reservoirs, achieving scientific well selection for this type of reservoir and ensuring implementation effectiveness.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a well selection method for improving fluid extraction and production enhancement in bottom-water reservoirs, comprising the following steps: Parameters related to the effect of fluid extraction and production enhancement are collected as candidate indicators. Based on the correlation between the production enhancement effect of the implemented fluid extraction well and the parameters of the well, relevant indicators for selecting wells for fluid extraction and production enhancement in bottom water reservoirs are selected. Using the increase in production as the dependent variable and each relevant indicator as the independent variable, the weight coefficients of each relevant indicator are determined. Determine the data classification boundaries for each relevant indicator, and divide the data of each relevant indicator into several categories; The classification coefficients for each relevant indicator are determined based on the multiple between the expected and actual beneficial changes of each indicator. The relevant indicators are classified and weighted according to the classification coefficients, and then the results are summed to obtain the comprehensive evaluation index of the wells to be extracted, which serves as the basis for selecting wells to be extracted.

[0008] Optionally, static and dynamic parameters related to the yield-increasing effect of liquid extraction can be collected as candidate indicators.

[0009] Optionally, parameters with a correlation coefficient greater than or equal to 0.6 can be selected as relevant indicators through multivariate correlation analysis.

[0010] Optionally, the grey relational analysis method can be used to determine the classification coefficients of each relevant indicator.

[0011] Optionally, K-means cluster analysis can be performed on each relevant indicator to divide the data of each relevant indicator into three categories.

[0012] Optionally, based on the correlation between the fluid extraction and production enhancement effect and the comprehensive evaluation index, the potential classification is determined, and wells to be extracted are selected based on the classification results.

[0013] Secondly, the present invention provides a well selection system for improving fluid extraction and enhancing production in bottom-water reservoirs, comprising: The index selection module is used to collect parameters related to the effect of fluid extraction and production enhancement as candidate indicators. Based on the correlation between the production enhancement effect of the implemented fluid extraction well and the parameters of the well, the relevant indicators for fluid extraction and production enhancement of bottom water reservoirs are selected. The weighting calculation module is used to determine the weighting coefficients of each relevant indicator, with the increase in production as the dependent variable and each relevant indicator as the independent variable. The classification module is used to determine the data classification boundaries of each relevant indicator and to divide the data of each relevant indicator into several categories; The classification coefficient calculation module is used to determine the classification coefficient of each relevant indicator based on the multiple between the expected beneficial change and the actual beneficial change of each relevant indicator. The comprehensive evaluation index calculation module is used to weight and sum the classifications of various relevant indicators according to the classification coefficients, and then accumulate the results to obtain the comprehensive evaluation index of the well for which the well is to be extracted, which serves as the basis for selecting wells to be extracted.

[0014] Thirdly, the present invention provides an electronic 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 select wells for fluid extraction and production enhancement in the bottom water reservoir.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the well selection method for improving fluid extraction and increasing production in bottom water reservoirs.

[0016] Fifthly, the present invention provides a computer program product including a computer-readable medium, wherein the computer-readable medium contains computer-readable program code, the program code executing the bottom water reservoir fluid extraction and production enhancement well selection method.

[0017] Compared with the prior art, the present invention has the following beneficial effects: This invention determines well selection indicators by correlating the production enhancement effect of previous fluid extraction wells with static and dynamic parameters. It then uses grey relational analysis and cluster analysis to determine the weighting and classification coefficients of the selected indicators, thereby obtaining a comprehensive evaluation index for the fluid extraction wells. Target wells are then screened using this comprehensive evaluation index. This is a method for selecting wells for fluid extraction and production enhancement in bottom-water reservoirs that considers multiple influencing factors and provides rapid, accurate, standardized, and quantitative analysis. Compared to existing methods, this invention has two advantages: First, it comprehensively considers various static and dynamic parameters affecting the fluid extraction effect of oil wells, establishing standardized and quantitative indicators, thus solving the problems of relatively singular factors, lack of quantitative standards, and low success rate of production enhancement after implementation in empirical methods. Second, this method is simple and easy to operate, solving the problems of large workload, long time consumption, and weak practicality in field production of well selection through numerical simulation methods. This invention achieves scientific well selection for fluid extraction and production enhancement in bottom-water reservoirs, ensuring effective implementation.

[0018] The well selection method for fluid extraction and production enhancement in bottom-water reservoirs according to this invention was applied to 91 wells in a certain oilfield, achieving an efficiency of 87.9% and an average daily fluid production per well of 3.61 m³. 3 Increased to 6.68m 3The average daily oil production per well increased from 1.22t to 2.21t, with stable water cut; the cumulative oil production increased by 2.48×104t, achieving good field application results. Attached Figure Description

[0019] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way.

[0020] In the attached diagram: Figure 1 This is a flowchart of a specific embodiment of the well selection method for improving fluid extraction and increasing production in bottom-water reservoirs according to the present invention; Figure 2 This is a diagram illustrating the well selection and classification for fluid extraction and production enhancement according to the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0023] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. This application can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified. The present invention will now be described in detail with reference to the accompanying drawings.

[0024] like Figure 1 As shown, the present invention provides a well selection method for improving fluid extraction and production enhancement in bottom water reservoirs, comprising the following steps: Parameters related to the effect of fluid extraction and production enhancement are collected as candidate indicators. Based on the correlation between the production enhancement effect of the implemented fluid extraction well and the parameters of the well, relevant indicators for selecting wells for fluid extraction and production enhancement in bottom water reservoirs are selected. Using the increase in production as the dependent variable and each relevant indicator as the independent variable, the weight coefficients of each relevant indicator are determined. Determine the data classification boundaries for each relevant indicator, and divide the data of each relevant indicator into several categories; The classification coefficients for each relevant indicator are determined based on the multiple between the expected and actual beneficial changes of each indicator. The relevant indicators are classified and weighted according to the classification coefficients, and then the results are summed to obtain the comprehensive evaluation index of the wells to be extracted, which serves as the basis for selecting wells to be extracted.

[0025] The present invention provides a well selection method for fluid extraction and production enhancement in bottom-water reservoirs, which takes into account a variety of influencing factors and can quickly, accurately, and quantitatively select wells for fluid extraction and production enhancement in bottom-water reservoirs.

[0026] Example 1 This embodiment of a well selection method for improving fluid extraction and production enhancement in bottom water reservoirs includes the following steps: Step 1: Determine the relevant indicators for selecting wells for fluid extraction and production enhancement in bottom water reservoirs based on the correlation between the production enhancement effect of the implemented fluid extraction wells and the static and dynamic parameters of the wells; Step 2: Determine the weighting coefficients of each relevant indicator; Step 3: Determine the classification boundaries of each relevant indicator; Step 4: Determine the classification coefficient of the classification boundary; Step 5: Calculate the comprehensive evaluation index of the well based on the weighting coefficients and classification coefficients of the relevant indicators; Step Six: Based on the correlation between the fluid extraction and production enhancement effect and the comprehensive evaluation index, determine the potential classification and use the classification results as the basis for selecting wells to be extracted.

[0027] The method for determining well selection indicators for fluid extraction and production enhancement in bottom water reservoirs in step one includes the following steps: Step 101: Obtain static and dynamic parameters related to the effect of liquid extraction on yield increase.

[0028] Static parameters include: oil layer thickness, average porosity, average permeability, oil saturation, single-well controlled reserves, oil-water contact relationship, oil-water viscosity ratio, bottom water thickness, interlayer distribution frequency, permeability gradient, permeability surge coefficient, and permeability variation coefficient.

[0029] Dynamic parameters include: perforation degree, initial production capacity, current fluid production, current production capacity, water cut, geological reserve recovery degree, recoverable reserve recovery degree, formation energy level, bottom hole flowing pressure, and remaining oil saturation.

[0030] Step 102: Apply multivariate correlation analysis to plot scatter plots of the increased oil production effect of the implemented fluid extraction wells against the static and dynamic parameters of the oil wells, and perform trend analysis to select the correlation coefficient R. 2 Static parameters and production dynamic parameters of oil wells with a value ≥0.6 are used as well selection indicators.

[0031] Step two, the method for determining the weight coefficients of each relevant indicator, includes the following steps: Step 201: Using the increase in production as the parent sequence and the relevant indicators as child sequences, determine the analysis sequence. Based on the obtained parent and child sequences, construct the original data matrix. The calculation formula is as follows:

[0032] Step 202: Preprocess the initial sequence data using the following formula:

[0033] Step 203: Calculate the correlation coefficient between the subsequence and the parent sequence. The calculation formula is as follows:

[0034] Step 204: Calculate the correlation between the subsequence and the parent sequence. The calculation formula is as follows:

[0035] Step 205: Based on the analysis sequence and the correlation between the relevant indicators, determine the weight coefficient of each evaluation indicator. The calculation formula is as follows:

[0036] In the formula: x0 is the parent sequence; xi is a subsequence; xi(k)′ is the dimensionless transformation of xi(k); Δi(k) is the absolute difference between the k-th element of x0 and xi; ξi(k) is the correlation coefficient; ρ is the resolution coefficient, which generally takes a value in the range of (0, 1), and is usually taken as 0.5; γk represents the correlation degree of the evaluation parameters; Wk is the weighting coefficient of the evaluation parameter.

[0037] Step three, determining the classification boundaries of each relevant indicator, includes the following steps: Import the evaluation index data determined in Step 1 into SPSS software. To eliminate differences between data, standardization is performed first, and K-means cluster analysis is performed on each evaluation factor. The variables are the standardized relevant indicators, the number of clusters is 3, and after iterative processing, the relevant index data are divided into three categories, A, B and C, based on the actual situation of the mine, and the classification boundaries of each relevant indicator are determined.

[0038] Step four, the method for determining the classification coefficients of the classification boundary, includes the following steps: The classification coefficients are determined based on the multiple between the expected and actual beneficial changes of each relevant indicator, where the classification coefficient for category A is 1, the classification coefficient for category B is 0.5, and the classification coefficient for category C is 0.

[0039] In step five, based on the weighting coefficient Wk and classification coefficient Nk of each relevant indicator, the formula for calculating the comprehensive evaluation index of the fluid extraction well is as follows:

[0040] In step six, based on the correlation between the fluid extraction and production enhancement effect and the comprehensive evaluation index, the potential classification is determined, and the classification results are used as the basis for selecting wells to be extracted. This includes the following steps: A scatter plot of the extraction yield enhancement effect and comprehensive evaluation index was drawn. Based on the extraction oil enhancement effect, it was divided into three levels: Level I, Level II and Level III. Level I is characterized by good oil enhancement effect and has extraction potential, and should be promoted and applied. Level II is characterized by moderate oil enhancement effect, and should be subject to dynamic tracking and timely optimization and control after extraction. Level III is characterized by poor oil enhancement effect and no extraction potential, and should be avoided in extraction production.

[0041] Based on the classification, the corresponding comprehensive evaluation index is read to establish well selection standards for improving fluid production.

[0042] Example 2 Between 2017 and 2019, 29 production wells were successively drilled using conventional well selection methods in this reservoir. After the production process, the water cut of nearly one-third of the wells increased rapidly, and the effect of increasing oil production through production was not ideal.

[0043] The well selection process is as follows: First, using multivariate correlation analysis, scatter plots were drawn showing the oil production increase effect of implemented fluid lifting wells in relation to the well's static and dynamic parameters, and trend analysis was performed to select the correlation coefficient R. 2 Static parameters and dynamic production parameters of oil wells with a value ≥0.6 are used as well selection indicators. There are 4 static parameters and 5 dynamic parameters, as shown in Table 1 below.

[0044] Table 1

[0045] The weight coefficients of the nine indicators were calculated using the grey relational analysis method, and the results are shown in Table 2 below.

[0046] Table 2

[0047] K-means cluster analysis was used to classify the relevant index data into three categories: A, B, and C. The classification boundaries and classification coefficients of each relevant index were determined. The classification coefficient of category A was 1, the classification coefficient of category B was 0.5, and the classification coefficient of category C was 0. The results are shown in Table 3 below.

[0048] Table 3

[0049] Based on the weighting coefficients and classification coefficients of each relevant indicator, a comprehensive evaluation index for fluid extraction wells is calculated. A scatter plot of the fluid extraction and production increase effect versus the comprehensive evaluation index is plotted. Based on the fluid extraction and oil increase effect, wells are classified into three levels: Level I, Level II, and Level III. Figure 2 As shown in Table 4 below, a well selection standard for fluid extraction and production enhancement was established.

[0050] Table 4

[0051] The comprehensive evaluation index of all oil wells in the reservoir was calculated. Class I and Class II oil wells were selected for fluid extraction production. After fluid extraction, Class II oil wells were subject to enhanced monitoring and control.

[0052] Example 3 Based on Example 1, a well selection method for fluid extraction and production enhancement in bottom-water reservoirs is disclosed, comprising: The index selection module is used to collect parameters related to the effect of fluid extraction and production enhancement as candidate indicators. Based on the correlation between the production enhancement effect of the implemented fluid extraction well and the parameters of the well, the relevant indicators for fluid extraction and production enhancement of bottom water reservoirs are selected. The weighting calculation module is used to determine the weighting coefficients of each relevant indicator, with the increase in production as the dependent variable and each relevant indicator as the independent variable. The classification module is used to determine the data classification boundaries of each relevant indicator and to divide the data of each relevant indicator into several categories; The classification coefficient calculation module is used to determine the classification coefficient of each relevant indicator based on the multiple between the expected beneficial change and the actual beneficial change of each relevant indicator. The comprehensive evaluation index calculation module is used to weight and sum the classifications of various relevant indicators according to the classification coefficients, and then accumulate the results to obtain the comprehensive evaluation index of the well for which the well is to be extracted, which serves as the basis for selecting wells to be extracted.

[0053] Example 4 The purpose of this embodiment is to provide an electronic 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 select wells for fluid extraction and production enhancement in the bottom water reservoir.

[0054] Example 5 The purpose of this embodiment is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the bottom water reservoir fluid extraction and production enhancement well selection method.

[0055] Example 6 The purpose of this embodiment is to provide a computer program product including a computer-readable medium, wherein the computer-readable medium contains computer-readable program code, and the program code executes the bottom water reservoir fluid extraction and production enhancement well selection method.

[0056] The steps and methods involved in the apparatuses of the above embodiments 3, 4, 5 and 6 correspond to those in embodiment 1. For specific implementation details, please refer to the relevant description section of embodiment 1.

[0057] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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 apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes. 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. 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] Unless otherwise specified, the working methods or control methods involved in the above embodiments are conventional working methods or control methods in the art.

[0059] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Any other modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention, as long as they do not depart from the spirit and scope of the technical solutions of the present invention, should be covered within the scope of the claims of the present invention.

Claims

1. A method for selecting wells for fluid extraction and production enhancement in bottom-water reservoirs, characterized in that, Includes the following steps: Parameters related to the effect of fluid extraction and production enhancement are collected as candidate indicators. Based on the correlation between the production enhancement effect of the implemented fluid extraction well and the parameters of the well, relevant indicators for selecting wells for fluid extraction and production enhancement in bottom water reservoirs are selected. Using the increase in production as the dependent variable and each relevant indicator as the independent variable, the weight coefficients of each relevant indicator are determined. Determine the data classification boundaries for each relevant indicator and divide the data of each relevant indicator into several categories; The classification coefficients for each relevant indicator are determined based on the multiple between the expected and actual beneficial changes of each indicator. The relevant indicators are classified and weighted according to the classification coefficients, and then the results are summed to obtain the comprehensive evaluation index of the wells to be extracted, which serves as the basis for selecting wells to be extracted.

2. The well selection method for improving fluid extraction and increasing production in bottom water reservoirs according to claim 1, characterized in that, The candidate indicators include static and dynamic parameters related to the effect of fluid extraction and production enhancement. Static parameters include: reservoir thickness, average porosity, average permeability, oil saturation, controlled reserves per well, oil-water contact relationship, oil-water viscosity ratio, bottom water thickness, interlayer distribution frequency, permeability gradient, permeability surge coefficient, and permeability variation coefficient. Dynamic parameters include: perforation degree, initial production capacity, current fluid production, current production capacity, water cut, geological reserve recovery degree, recoverable reserve recovery degree, formation energy retention level, bottom hole flowing pressure, and remaining oil saturation.

3. The well selection method for improving fluid extraction and production enhancement in bottom water reservoirs according to claim 1, characterized in that, Parameters with a correlation coefficient greater than or equal to 0.6 were selected as relevant indicators using multivariate correlation analysis.

4. The well selection method for improving fluid extraction and increasing production in bottom water reservoirs according to claim 1, characterized in that, The grey relational analysis method was used to determine the classification coefficients of each relevant indicator.

5. The well selection method for improving fluid extraction and increasing production in bottom water reservoirs according to claim 1, characterized in that, K-means cluster analysis was performed on each relevant indicator, and the data of each relevant indicator were divided into three categories.

6. The well selection method for improving fluid extraction and production enhancement in bottom water reservoirs according to claim 1, characterized in that, Based on the correlation between the fluid extraction and production enhancement effect and the comprehensive evaluation index, potential classification is determined, and wells to be extracted are selected based on the classification results.

7. A well selection system for improving fluid extraction and enhancing production in bottom-water oil reservoirs, characterized in that, include: The index selection module is used to collect parameters related to the effect of fluid extraction and production enhancement as candidate indicators. Based on the correlation between the production enhancement effect of the implemented fluid extraction well and the parameters of the well, the relevant indicators for fluid extraction and production enhancement of bottom water reservoirs are selected. The weighting calculation module is used to determine the weighting coefficients of each relevant indicator, with the increase in production as the dependent variable and each relevant indicator as the independent variable. The classification module is used to determine the data classification boundaries of each relevant indicator and to divide the data of each relevant indicator into several categories; The classification coefficient calculation module is used to determine the classification coefficient of each relevant indicator based on the multiple between the expected beneficial change and the actual beneficial change of each relevant indicator. The comprehensive evaluation index calculation module is used to weight and sum the classifications of various relevant indicators according to the classification coefficients, and then accumulate the results to obtain the comprehensive evaluation index of the well for which the well is to be extracted, which serves as the basis for selecting wells to be extracted.

8. An electronic device, characterized in that, It includes 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 bottom water reservoir fluid extraction and production enhancement well selection according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the well selection method for improving fluid production and enhancing oil production in bottom water reservoirs as described in any one of claims 1-6.

10. A computer program product comprising a computer-readable medium, characterized in that, The computer-readable medium contains computer-readable program code that performs the well selection method for improving fluid production in bottom water reservoirs as described in any one of claims 1-6.

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