Fracture block oil reservoir water well pressure drive development well selection decision-making method

Abstract

The invention provides a fault block oil reservoir water well pressure drive development well selection decision-making method. The fault block oil reservoir water well pressure drive development well selection decision-making method comprises the steps that 1, a fault block oil reservoir pressure drive typical well group geologic model is established; step 2, carrying out a rock core stress sensitivity experiment test; 3, establishing a pressure drive development numerical simulation model and a pressure drive effect prediction factor set; 4, establishing a pressure driving effect prediction sample scheme design table and a pressure driving effect prediction sample library; 5, establishing a pressure drive development effect agent model; and 6, evaluating factors influencing the pressure drive yield increasing potential, and determining a pressure drive development well selection decision sequence. According to the fault block oil reservoir water well pressure drive development well selection decision-making method, the pressure drive effect prediction agent model is established to replace numerical simulation for pressure drive effect quantitative evaluation, time and calculation cost are greatly reduced, the pressure drive well selection decision-making method is formed, and the mine field pressure drive success rate and effect are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas field development, and particularly to a well selection decision-making method for pressure drive development of water injection wells in fault block reservoirs. Background Art

[0002] Fault block oilfields are important types of reservoirs. In the process of conventional water injection development, there are problems such as slow energy replenishment speed, small water drive swept area, and low oil washing efficiency, resulting in poor development effects. Drawing on the idea of rapid energy replenishment and enhanced production and efficiency through fracturing, pressure drive water injection development tests have been carried out in the oilfield site and achieved good results.

[0003] According to the existing problems in water injection of fault block reservoirs and the mechanism of pressure drive water injection, the selection of pressure drive test wells is generally carried out based on field experience. For example, select reservoirs that are relatively closed in the fault block, have insufficient energy, low recovery degree, good static corresponding relationship between oil and water wells, and wells that cannot be injected under high pressure or are under-injected, and the ground water supply network, water source are sufficient, the engineering supporting is relatively simple, and the wellbore can meet high-pressure water injection. At present, the pressure drive water injection technology can effectively achieve enhanced injection and solve the problem of high-pressure injection failure in injection wells by rapidly injecting a large amount of clear water to form a fracture zone. However, according to the application of pressure drive well groups, there are still problems such as unstable response of some oil wells in the well group and directional response. One of the key technical problems faced is the unclear understanding of the influencing factors of pressure drive development effects, and the well selection decision-making method and process parameters are not clear. In terms of well selection decision-making, overall, pressure drive well selection is based on field experience. For example, focus on selecting wells with serious under-injection and wells with rich remaining oil. The well selection mainly relies on the analysis of production dynamic data and production decline curves. This method has high requirements for data and has certain subjectivity, blindness, and risk. Subsequently, researchers proposed the dimensionless parameter method. This method selects pressure drive wells based on 5 dimensionless parameters, such as reservoirs with good physical properties, strong permeability, and well-developed natural fractures around the well. This method also strongly relies on data, and the cost of obtaining real data is high, and often computational data is used. At present, there is no systematic understanding of the main control factors such as reservoir and technology of pressure drive effects and their influencing laws, and there is a lack of quantitative well selection decision-making methods.

[0004] Patent CN116011855A relates to a multi-factor decision-making intelligent comprehensive well selection and layer selection decision-making method: it is necessary to calculate various decision values such as water absorption capacity decision-making, reservoir heterogeneity decision-making, injection dynamic decision-making, and multi-well group impedance model well selection decision-making, and then obtain a comprehensive decision factor, and then select wells with a comprehensive decision factor greater than the average value for profile control and displacement measures.

[0005] Patent CN115130799A relates to a well selection method and system for enhanced oil recovery by high-pressure water injection displacement in fracture-cavity reservoirs: by determining the influencing factors of the enhanced oil recovery effect of high-pressure water injection displacement in fracture-cavity reservoirs, then evaluating the oil production potential of each individual well, and judging the reservoir structure of the individual well according to the water injection indicator curve, energy indicator curve, liquid level recovery indicator curve and well test indicator curve of each individual well, and further determining the individual wells to be preferentially implemented with the high-pressure water injection development method.

[0006] Patent CN115205062A relates to a calculation method for the optimal water injection volume in reservoir pressure drive development: obtaining a reasonable value range of the pressure drive water injection volume through the economic limit production capacity and formation fracture pressure, combining reservoir numerical simulation and dynamic economic evaluation, and using the optimization theory to determine the optimal water injection volume for reservoir pressure drive development with the financial net present value as the optimization goal.

[0007] Patent CN115841083A relates to a method for determining the pressure drive injection allocation volume of an injection well: a method for determining the pressure drive water injection front and thus the pressure drive water injection volume according to the seepage characteristics of a low-permeability reservoir and combining the pressure change characteristics between oil and water wells.

[0008] The above existing technologies have the following disadvantages: First, they require various parameter tests and calculation of different test curves, which are difficult to measure and cumbersome to calculate. Second, they are used for well selection decisions for profile control wells and are not suitable for pressure drive well selection decisions. Third, different reservoir models need to be established for different blocks to predict development indicators. The above methods are all quite different from the present invention and cannot solve the technical problems we want to solve, and cannot provide decision support for well selection in pressure drive development of fault-block reservoirs. Therefore, we have invented a new well selection decision method for pressure drive development of fault-block reservoir water wells. Summary of the Invention

[0009] The object of the present invention is to provide a well selection decision method for pressure drive development of fault-block reservoir water wells that can greatly reduce time and calculation costs, form a pressure drive well selection decision method, and improve the success rate and effect of pressure drive in the oilfield.

[0010] The object of the present invention can be achieved by the following technical measures: A well selection decision method for pressure drive development of fault-block reservoir water wells, which includes:

[0011] Step 1, establish a geological model of a typical well group for pressure drive in a fault-block reservoir;

[0012] Step 2, conduct core stress sensitivity experiment tests;

[0013] Step 3, establish a numerical simulation model for pressure drive development and a set of pressure drive effect prediction factors;

[0014] Step 4, establish a pressure drive effect prediction sample scheme design table and a pressure drive effect prediction sample library;

[0015] Step 5: Establish a proxy model for the effect of pressure drive development;

[0016] Step 6: Evaluate the factors affecting the potential of pressure drive to increase production, and determine the decision-making order for well selection in pressure drive development.

[0017] The object of the present invention can also be achieved by the following technical measures:

[0018] In Step 1, select a typical well group that has undergone pressure drive in a fault-block reservoir, and establish a three-dimensional geological model, including: establishing the structural characteristics of the complex fault block, coarsening the logging curve data, establishing a lithofacies model using the sequential indicator simulation method, analyzing the distribution patterns of different lithofacies, modeling the key properties of permeability and porosity through Kriging interpolation method, and performing constraints through single-well interpretation data.

[0019] In Step 2, conduct a core stress sensitivity experiment. By adopting the variable fluid pressure stress sensitivity experiment method, keep the confining pressure constant and test the permeability of the core of a low-permeability reservoir at different fluid pressures.

[0020] In Step 2, the experimental steps mainly include: ① Connect the experimental equipment according to the standard; ② Install the experimental core into the core holder, apply a certain starting pressure according to the core permeability, and then gradually increase the inlet pressure, back pressure, and confining pressure to the designed pressure; ③ Keep the confining pressure constant, gradually decrease the back pressure and inlet pressure, and at the same time keep the pressure difference between the inlet and outlet constant, and measure the core permeability; ④ Repeat Step ③ until the pressure reduction ends; ⑤ Keep the confining pressure still stable, gradually increase the back pressure and inlet pressure, and at the same time keep the pressure difference between the core inlet and outlet constant, and measure the core permeability; ⑥ Repeat Step ⑤, and end the measurement when the inlet pressure rises to the initial value of the experiment.

[0021] In Step 2, determine the equation of core permeability change with effective stress; the exponential formula of the relationship between permeability and pressure difference:

[0022]

[0023] In the formula:

[0024] α—Stress sensitivity coefficient, MPa -1 ;

[0025] p—Formation pressure, MPa;

[0026] p e —Formation pressure at re in the low-permeability reservoir, MPa;

[0027] K—Oil reservoir permeability, mD;

[0028] K0—Original permeability of the oil reservoir, mD.

[0029] In Step 3, based on the property model, fluid model, rock physical property parameters required for the development of a typical fault-block reservoir well group, and the stress sensitivity curve obtained from core experiments, and by defining the pressure drive production regime, a numerical simulation model for pressure drive development of a typical fault-block reservoir is established.

[0030] Step 3 also includes conducting history matching during the pressure drive production process after establishing the numerical simulation model for pressure drive development.

[0031] In Step 3, when conducting history matching during the pressure drive production process, first, by fitting the water cut with a fixed oil production rate, determine the adjustable range of the property model and the relative permeability curve, and fit the historical production data of the typical well group before pressure drive to ensure the accuracy of the model before pressure drive; second, conduct numerical simulation of the pressure drive water injection and oil production processes, and adjust the stress sensitivity parameters by fitting the actual pump injection pressure during pressure drive to achieve the inversion of the pressure drive process and ensure the accuracy of the model after pressure drive.

[0032] In Step 3, considering the influence of geological parameters and process parameters on the pressure drive production, change the relevant parameter values in the established numerical simulation model of the typical fault-block reservoir, and through orthogonal experiments, taking the cumulative oil production after pressure drive as the reference index, determine the main factors affecting the pressure drive effect, and establish a prediction factor set for the pressure drive effect, where the geological parameters include permeability, effective thickness, water cut, and stress sensitivity coefficient, and the process parameters include pump injection volume, pump injection rate, and shut-in time.

[0033] In Step 4, design the value range of each parameter in the prediction factor set for the pressure drive effect, and establish a design table for the prediction sample plan of the pressure drive effect. To save time costs, the parameter range should cover most cases as much as possible, where the permeability is 30 - 500 mD, the effective thickness is 5 - 30 m, the water cut is 10 - 90%, the stress sensitivity coefficient is 0.01 - 0.11, the pump injection volume is 4000 - 20000 m 3 ³, the pump injection rate is 500 - 1200 m 3 ³ / d, and the shut-in time is 10 - 30 d.

[0034] In Step 4, taking the cumulative oil production during the pressure drive cycle as the effect evaluation index, establish a prediction sample library for the pressure drive effect through numerical simulation.

[0035] In Step 5, establish a surrogate model for the pressure drive development effect that shows the relationship between the cumulative oil production in a cycle and each design parameter. The surrogate model is based on mathematical statistics theory, with fitting accuracy and prediction accuracy as constraints, and is a mathematical model that uses an approximation method to fit discrete data. During the optimization process, it can replace numerical simulation, significantly reducing time and computational costs, and eliminating numerical noise, smoothing the results, and removing outliers.

[0036] In step 6, to ensure the model accuracy, sample normalization is first carried out to eliminate the influence of different dimensions on the accuracy of the surrogate model. The values of each design variable of the sample points are normalized to [0, 1]. Secondly, a radial basis function surrogate model is established, and the leave-one-out method is used to test the accuracy of the radial basis function surrogate model. According to the multiple correlation coefficient criterion, if the test model accuracy is greater than 85%, the requirement for effect prediction accuracy is met.

[0037] In step 6, collect the geological parameters of all wells to be decided. Substitute different combinations of process parameters into the surrogate model of the pressure drive development effect to obtain the cumulative oil production of the wells to be decided under different combinations of process parameters, so as to determine the optimal process parameter combination scheme and its pressure drive effect for each well to be decided.

[0038] In step 6, sort the predicted results of the cumulative oil production during the pressure drive cycle of all wells to be decided, determine the well selection order of all wells to be decided, and divide them into three levels. The top 30% are the priority pressure drive wells, 30 - 60% are the general pressure drive wells, and the last 40% are the wells not recommended for pressure drive.

[0039] The object of the present invention can also be achieved by the following technical measures: a well selection decision system for water well pressure drive development in a fault block oil reservoir. This well selection decision system for water well pressure drive development in a fault block oil reservoir uses the well selection decision method for water well pressure drive development in a fault block oil reservoir to quantitatively evaluate the pressure drive effect.

[0040] In the well selection decision method for water well pressure drive development in the fault block oil reservoir of the present invention, a surrogate model for predicting the pressure drive effect is established to replace numerical simulation for quantitative evaluation of the pressure drive effect, greatly reducing the time and calculation costs and forming a well selection decision method for pressure drive, improving the success rate and effect of field pressure drive. This method determines the main control factors of the pressure drive development effect through numerical simulation, uses statistical analysis methods to regress and establish a surrogate model for the pressure drive effect, combines optimization algorithms to determine the process parameters of each well to be decided, and predicts the effect under the optimal process parameters of each well to be decided, so as to achieve quantitative well selection decision.

[0041] Based on a typical fault block oil reservoir, the present invention proposes a pressure drive well selection method that can predict productivity. For general fault block oil reservoirs with multiple wells to be pressure driven, numerical simulation is usually used for history matching and production prediction. Due to the huge number of grids and the complexity of reservoir fluid flow, numerical simulation usually consumes huge time costs and calculation costs. Through this pressure drive well selection method, collecting and statistically analyzing reservoir characteristic parameters can quickly and conveniently predict the productivity of each well. Compared with numerical simulation or experiments, the time costs and calculation costs consumed are negligible, which is a new way for well selection in water well pressure drive development in fault block oil reservoirs. Description of the Drawings

[0042] Figure 1This is a flowchart of a specific embodiment of the well selection decision-making method for pressure drive development of water wells in fault block reservoirs of the present invention. Detailed implementation mode

[0043] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0044] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, and / or combinations thereof.

[0045] As Figure 1 shown, Figure 1 This is a flowchart of the well selection decision-making method for pressure drive development of water wells in fault block reservoirs of the present invention. The well selection decision-making method for pressure drive development of water wells in fault block reservoirs includes:

[0046] (1) Select typical well groups that have undergone pressure drive in fault block reservoirs, and establish a three-dimensional geological model, including: establishing the structural characteristics of complex fault blocks, coarsening logging curve data, establishing a lithofacies model using the sequential indicator simulation method, analyzing the distribution patterns of different lithofacies, performing modeling of key properties such as permeability and porosity through Kriging interpolation method, and constraining through single-well interpretation data;

[0047] (2) Conduct core stress sensitivity experiments. By using the variable fluid pressure stress sensitivity experiment method, keep the confining pressure constant and test the permeability of the core of a certain low-permeability reservoir under different fluid pressures. The experimental steps mainly include: ① Connect the experimental equipment according to the standard; ② Install the experimental core into the core holder, apply a certain starting pressure according to the core permeability, and then gradually increase the inlet pressure, back pressure, and confining pressure to the designed pressure; ③ Keep the confining pressure constant, gradually decrease the back pressure and inlet pressure, and at the same time keep the pressure difference between the inlet and outlet constant, and measure the core permeability; ④ Repeat step ③ until the pressure reduction ends; ⑤ Keep the confining pressure still stable, gradually increase the back pressure and inlet pressure, and at the same time keep the pressure difference between the inlet and outlet of the core constant, and measure the core permeability; ⑥ Repeat step ⑤, and measure the end when the inlet pressure rises to the initial value of the experiment. Determine the equation of core permeability change with effective stress;

[0048] Exponential formula of the relationship between permeability and pressure difference:

[0049]

[0050] In the formula:

[0051] α—Stress sensitivity coefficient, MPa -1 ;

[0052] p—Formation pressure, MPa;

[0053] p e —Formation pressure at re in low-permeability oil reservoir, MPa;

[0054] K—Oil reservoir permeability, mD;

[0055] K0—Original permeability of oil reservoir, mD.

[0056] (3) Input the necessary property models, fluid models, rock physical property parameters for the development of typical fault-block oil reservoir well groups, as well as the stress sensitivity curves obtained from core experiments, and define the pressure drive production regime to establish a numerical simulation model for pressure drive development of typical fault-block oil reservoirs;

[0057] (4) Conduct historical matching during the pressure drive production process. First step, determine the adjustable range of property models and relative permeability curves by fitting water cut with fixed oil production rate, and fit the historical production data of typical well groups before pressure drive to ensure the accuracy of the model before pressure drive. Second step, conduct numerical simulation of the pressure drive water injection and oil production process, adjust the stress sensitivity parameters by fitting the actual pump injection pressure during pressure drive to realize the inversion of the pressure drive process and ensure the accuracy of the model after pressure drive;

[0058] (5) Consider the influence of geological parameters and process parameters on the pressure drive production, change the relevant parameter values in the established numerical simulation model of typical fault-block oil reservoirs, and determine the main factors affecting the pressure drive effect through orthogonal experiments with the cumulative oil production after pressure drive as the reference index to establish a prediction factor set for pressure drive effect, where geological parameters include permeability, effective thickness, water cut, stress sensitivity coefficient, and process parameters include injection volume, injection rate, shut-in time;

[0059] (6) Design the value range of each parameter in the prediction factor set for pressure drive effect, establish a design table for the prediction sample scheme of pressure drive effect. To save time cost, the parameter range should cover most situations as much as possible, where permeability is 30 - 500 mD, effective thickness is 5 - 30 m, water cut is 10 - 90%, stress sensitivity coefficient is 0.01 - 0.11, injection volume is 4000 - 20000 m 3 , injection rate is 500 - 1200 m 3 / d, shut-in time is 10 - 30 d;

[0060] (7) Take the cumulative oil production during the pressure drive cycle as the effect evaluation index, and establish a prediction sample library for pressure drive effect through numerical simulation, as shown in Table 1:

[0061] Table 1 Prediction table and data results of pressure drive scheme

[0062]

[0063] (8) Establish a proxy model for the pressure drive development effect that relates the cumulative oil production in a cycle to each design parameter. The proxy model is a mathematical model based on mathematical statistics theory, constrained by fitting accuracy and prediction accuracy, and uses an approximation method to fit discrete data. It can replace numerical simulation during the optimization process, significantly reducing time and computational costs, and eliminating numerical noise, smoothing the results, and removing outliers;

[0064] (9) To ensure the accuracy of the model, first perform sample normalization: eliminate the influence of different dimensions on the accuracy of the proxy model, normalize the values of each design variable of the sample points to [0, 1]. Secondly, establish a radial basis function proxy model through MATLAB programming, and use the leave-one-out method to test the accuracy of the radial basis function proxy model. According to the multiple correlation coefficient criterion, if the test model accuracy is greater than 85%, it meets the requirements of the effect prediction accuracy;

[0065] (10) Collect the geological parameters of all wells to be decided. Substitute different combinations of process parameters into the proxy model for the pressure drive development effect to obtain the cumulative oil production in a cycle of the wells to be decided under different combinations of process parameters, so as to determine the optimal combination plan of process parameters and its pressure drive effect for each well to be decided;

[0066] (11) Sort the predicted results of the cumulative oil production in the pressure drive cycle of all wells to be decided, determine the selection order of all wells to be decided, and divide them into three levels. The top 30% are priority pressure drive wells, 30 - 60% are general pressure drive wells, and the last 40% are non-recommended pressure drive wells. As shown in Table 2:

[0067] Table 2 Sorting of the pressure drive potential of wells to be decided

[0068]

[0069] The following are several specific embodiments of applying the present invention

[0070] Embodiment 1

[0071] In a specific Embodiment 1 of applying the present invention, the well selection decision method for the pressure drive development of the fault block reservoir water wells includes the following steps:

[0072] (1) Collect the geological parameters of 8 wells to be decided in an oilfield in the eastern part of China. Substitute different combinations of process parameters into the proxy model for the pressure drive development effect to obtain the cumulative oil production in a cycle of the wells to be decided under different combinations of process parameters, so as to determine the optimal combination plan of process parameters and its pressure drive effect for each well to be decided.

[0073] Table 3 Prediction table and data results of the pressure drive plan

[0074]

[0075]

[0076] (2) Sort the predicted cumulative oil production results of all wells to be decided during the pressure drive cycle, determine the well selection order of all wells to be decided, and divide them into three levels. The top 30% are the priority pressure drive wells, 30 - 60% are the general pressure drive wells, and the last 40% are the non-recommended pressure drive wells. As shown in Table 4:

[0077] Table 4 Sorting of Pressure Drive Potential of Wells to be Decided

[0078]

[0079] Example 2

[0080] In a specific Example 2 of applying the present invention, the well selection decision method for water well pressure drive development in this fault-block reservoir includes the following steps:

[0081] (1) Collect the geological parameters of 8 wells to be decided in an oilfield in the western part of China, substitute different combinations of process parameters into the proxy model of pressure drive development effect, obtain the cumulative oil production of the wells to be decided under different combinations of process parameters, and thus determine the optimal process parameter combination scheme and its pressure drive effect for each well to be decided.

[0082] Table 5 Prediction Table of Pressure Drive Scheme and Data Results

[0083]

[0084] (2) Sort the predicted cumulative oil production results of all wells to be decided during the pressure drive cycle, determine the well selection order of all wells to be decided, and divide them into three levels. The top 30% are the priority pressure drive wells, 30 - 60% are the general pressure drive wells, and the last 40% are the non-recommended pressure drive wells. As shown in Table 6:

[0085] Table 6 Sorting of Pressure Drive Potential of Wells to be Decided

[0086]

[0087]

[0088] Example 3

[0089] In a specific Example 3 of applying the present invention, the well selection decision method for water well pressure drive development in this fault-block reservoir includes the following steps:

[0090] (1) Collect the geological parameters of 8 wells to be decided in an oilfield in the eastern part of China, substitute different combinations of process parameters into the proxy model of pressure drive development effect, obtain the cumulative oil production of the wells to be decided under different combinations of process parameters, and thus determine the optimal process parameter combination scheme and its pressure drive effect for each well to be decided.

[0091] Table 7 Prediction Table of Pressure Drive Scheme and Data Results

[0092]

[0093] (2) Sort the cumulative oil production prediction results of the pressure drive cycles of all wells to be decided, determine the well selection order of all wells to be decided, and divide them into three levels. The top 30% are the priority pressure drive wells, 30 - 60% are the general pressure drive wells, and the last 40% are the wells not recommended for pressure drive. As shown in Table 8:

[0094] Table 8 Sorting of Pressure Drive Potential of Wells to be Decided

[0095]

[0096] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0097] Except for the technical features described in the specification, the rest are the known technologies of those skilled in the art.

Claims

1. Decision-making method for well selection in pressure drive development of fault-block oil reservoirs, characterized in that, The well selection decision-making method for pressure drive development of fault-block reservoirs includes: Step 1: Establish a geological model of a typical well group for pressure drive in a fault-block reservoir; Step 2: Conduct core stress sensitivity experiment tests; Step 3: Establish a numerical simulation model for pressure drive development and a factor set for predicting pressure drive effects; Step 4: Establish a design table for predicting pressure drive effect samples and a sample library for predicting pressure drive effects; Step 5: Establish a surrogate model for pressure drive development effects; Step 6: Evaluate the factors affecting the potential for increased production by pressure drive and determine the decision-making order for well selection in pressure drive development.

2. The decision-making method for well selection in pressure drive development of fault-block oil reservoirs according to claim 1, characterized in that, In Step 1, select a typical well group that has undergone pressure drive in a fault-block reservoir and establish a three-dimensional geological model, including: establishing the structural characteristics of complex fault blocks, coarsening well logging curve data, using sequential indicator simulation method to establish a lithofacies model, analyzing the distribution patterns of different lithofacies, modeling key properties such as permeability and porosity through Kriging interpolation method, and constraining with single-well interpretation data.

3. The decision-making method for well selection in pressure drive development of fault-block oil reservoirs according to claim 1, characterized in that, In Step 2, conduct core stress sensitivity experiments. By adopting the variable fluid pressure stress sensitivity experiment method, test the permeability of the core of a low-permeability reservoir at different fluid pressures while keeping the confining pressure constant.

4. The decision-making method for well selection in pressure drive development of fault-block oil reservoirs according to claim 3, characterized in that, In Step 2, the experimental steps mainly include: ① Connect the experimental equipment according to the standard; ② Install the experimental core into the core holder, apply a certain starting pressure according to the core permeability, and then gradually increase the inlet pressure, back pressure, and confining pressure to the designed pressure; ③ Keep the confining pressure constant, gradually decrease the back pressure and inlet pressure while keeping the pressure difference between the inlet and outlet constant, and measure the core permeability; ④ Repeat Step ③ until the pressure reduction ends; ⑤ Keep the confining pressure stable and unchanged, gradually increase the back pressure and inlet pressure while keeping the pressure difference between the core inlet and outlet constant, and measure the core permeability; ⑥ Repeat Step ⑤ and end the measurement when the inlet pressure rises to the initial value of the experiment.

5. The decision-making method for well selection in pressure drive development of fault-block oil reservoirs according to claim 4, characterized in that, In Step 2, determine the equation of core permeability variation with effective stress; the exponential formula for the relationship between permeability and pressure difference: In the formula: α—Stress sensitivity coefficient, MPa -1 ; p—formation pressure, MPa; p e — Formation pressure at re in low-permeability reservoir, MPa; K—oil reservoir permeability, mD; K0—original permeability of the oil reservoir, mD.

6. The decision-making method for well selection in pressure drive development of fault-block oil reservoirs according to claim 1, characterized in that, In Step 3, according to the property model, fluid model, rock physical property parameters necessary for the development of a typical fault-block reservoir well group, as well as the stress sensitivity curve obtained from core experiments, and define the pressure drive production regime, establish a numerical simulation model for pressure drive development of a typical fault-block reservoir.

7. The decision-making method for well selection in pressure drive development of fault-block oil reservoirs according to claim 1, characterized in that, Step 3 also includes, after establishing the numerical simulation model for pressure drive development, conducting history matching during the pressure drive production process.

8. The decision-making method for well selection in pressure drive development of fault-block oil reservoirs according to claim 7, characterized in that, In Step 3, when conducting history matching during the pressure drive production process, in the first step, determine the adjustable range of the property model and relative permeability curve by fitting the water cut with a fixed oil production rate, and fit the historical production data of the typical well group before pressure drive to ensure the accuracy of the model before pressure drive; in the second step, conduct numerical simulation of the pressure drive injection and production process, adjust the stress sensitivity parameters by fitting the actual pump injection pressure during pressure drive, and realize the inversion of the pressure drive process to ensure the accuracy of the model after pressure drive.

9. The decision-making method for well selection in pressure drive development of fault-block oil reservoirs according to claim 1, characterized in that, In step 3, considering the influence of geological parameters and process parameters on the production of pressure drive, change the relevant parameter values in the established numerical simulation model of the typical fault-block reservoir, and through orthogonal experiments, with the cumulative oil production after pressure drive as the reference index, determine the main factors affecting the pressure drive effect, and establish a prediction factor set for the pressure drive effect. The geological parameters include permeability, effective thickness, water cut, and stress sensitivity coefficient, and the process parameters include injection volume, injection rate, and shut-in time.

10. The decision-making method for well selection in pressure drive development of fault-block oil reservoirs according to claim 1, characterized in that, In step 4, determine the value ranges of the parameters in the prediction factor set for the pressure drive effect, and establish a design table for the prediction sample scheme of the pressure drive effect. To save time costs, the parameter ranges should cover most cases as much as possible. Among them, the permeability is 30 - 500 mD, the effective thickness is 5 - 30 m, the water cut is 10 - 90%, the stress sensitivity coefficient is 0.01 - 0.11, the injection volume is 4000 - 20000 m 3 , and the injection rate is 500 - 1200 m 3 / d, and the shut-in time is 10 - 30 d.

11. The decision-making method for well selection in pressure drive development of fault-block oil reservoirs according to claim 1, characterized in that, In step 4, with the cumulative oil production in the pressure drive cycle as the effect evaluation index, establish a prediction sample library for the pressure drive effect through numerical simulation.

12. The well selection decision-making method for pressure drive development of water wells in fault block reservoirs according to claim 1, wherein, In step 5, establish a surrogate model for the pressure drive development effect that reflects the relationship between the cumulative oil production in the cycle and each design parameter. The surrogate model is a mathematical model based on mathematical statistics theory, constrained by fitting accuracy and prediction accuracy, and uses an approximation method to fit discrete data. It can replace numerical simulation during the optimization process, greatly reducing time and computational costs, and eliminating numerical noise, smoothing the results, and removing outliers.

13. The well selection decision-making method for pressure drive development of water wells in fault block reservoirs according to claim 1, wherein, In step 6, to ensure the accuracy of the model, first perform sample normalization to eliminate the influence of different dimensions on the accuracy of the surrogate model, and normalize the values of each design variable of the sample points to [0, 1]. Secondly, establish a radial basis function surrogate model, and use the leave-one-out method to test the accuracy of the radial basis function surrogate model. According to the multiple correlation coefficient criterion, if the test model accuracy is greater than 85%, it meets the requirements of the effect prediction accuracy.

14. The well selection decision-making method for pressure drive development of water wells in fault block reservoirs according to claim 13, wherein, In step 6, collect the geological parameters of all wells to be decided, substitute different combinations of process parameters into the surrogate model of the pressure drive development effect, and obtain the cumulative oil production in the cycle of the wells to be decided under different combinations of process parameters, so as to determine the optimal process parameter combination plan and its pressure drive effect for each well to be decided.

15. The well selection decision-making method for pressure drive development of water wells in fault block reservoirs according to claim 14, wherein, In step 6, sort the predicted results of the cumulative oil production in the pressure drive cycle of all wells to be decided, determine the selection order of all wells to be decided, and divide them into three levels. The top 30% are the priority pressure drive wells, 30 - 60% are the general pressure drive wells, and the last 40% are the non-recommended pressure drive wells.

16. A well selection decision-making system for pressure drive development of water wells in fault block reservoirs, wherein, The well selection decision-making system for water injection pressure drive development in this fault-block reservoir quantitatively evaluates the pressure drive effect by using the well selection decision-making method for water injection pressure drive development in the fault-block reservoir described in any one of claims 1 - 15.

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