Proppant optimization method considering full-life-cycle effective closing stress change

Abstract

The invention belongs to the technical field of hydraulic fracturing in petroleum development, and discloses a proppant optimization method considering full-life-cycle effective closing stress variation, which comprises the following steps: constructing a reservoir stratum geomechanical model of a target area; constructing a geomechanics-seepage coupling model; hydrofracture dynamic expansion simulation is carried out; dynamically representing a crustal stress field in a flow-back process; characterizing a crustal stress field in the production process; testing the long-term flow conductivity of the proppant according to the effective closing stress of the crack obtained in the flowback and production processes; carrying out full-life-cycle oil reservoir numerical simulation and proppant optimization. According to the method, a fracturing-flowback-production integrated numerical simulation model based on fluid-solid coupling is constructed, a reservoir crustal stress field change rule and a proppant-flow conductivity change rule in the whole life cycle are obtained, and a yield optimal solution is obtained through simulation, so that the optimal proppant type, mesh number and sand laying concentration are obtained, and the yield of the reservoir is improved. The method is of great significance in reducing the hydraulic fracturing cost and improving the hydraulic fracturing effect.

Description

Technical Field

[0001] This invention belongs to the field of hydraulic fracturing technology in oil development, and particularly relates to a method for predicting and optimizing the proppant type and sand concentration in the hydraulic fracturing process based on the evolution law of the geostress field in the "fracturing-flowback-production" process. Specifically, it relates to a proppant selection method that considers the changes in effective closure stress throughout the entire life cycle. Background Technology

[0002] Hydraulic fracturing technology, as a primary technique for unconventional oil and gas resource development, plays a crucial role in the development of tight oil and gas, shale oil and gas, and other similar resources. Selecting an appropriate fracture conductivity is essential for improving hydraulic fracturing effectiveness and reducing costs. Currently, optimizing fracture conductivity during hydraulic fracturing primarily involves using single-well production as the objective function and comparing production rates under different fracture conductivity levels. However, this method sets the hydraulic fracture conductivity as a static value, failing to consider issues such as insufficient fluid supply to the well due to changes in the stress field and pore pressure field during fracturing and well production. This leads to the designed fracture conductivity exceeding actual production needs in the later stages of production, resulting in wasted conductivity (the greater the conductivity, the more proppant is required, and the higher the production costs).

[0003] Numerical simulation studies of reservoirs show that horizontal well fracturing operations, often described as "factory-style," cause formation pressure fluctuations of 4-7 MPa; similarly, high flowback rates during fracturing fluid flowback also lead to significant formation pressure fluctuations. Both of these factors significantly impact proppant conductivity. Therefore, establishing a numerical simulation and prediction model of the in-situ stress field and pore pressure field throughout the entire fracturing-flowback-production lifecycle, quantitatively characterizing the stress field changes during this process, and predicting the conductivity of hydraulic fractures to meet production requirements are crucial for reducing hydraulic fracturing costs and improving fracturing efficiency. Currently, no well-developed hydraulic fracturing numerical simulation model can accurately predict the reservoir in-situ stress field and pore pressure field throughout the entire fracturing-flowback-production lifecycle, making it difficult to optimize the dynamic conductivity of hydraulic fracturing fractures.

[0004] Chinese patent CN117113717A discloses a method for evaluating the proppant conductivity under stress disturbance, comprising the following steps: Step 1: Constructing a high-precision geomechanical model to determine the stress disturbance value during fracturing; Step 2: Determining the long-term conductivity test closure pressure value of the proppant based on the stress disturbance value; Step 3: Conducting long-term conductivity experiments on the proppant and comparing the changes in proppant conductivity under different test closure pressures; Step 4: Evaluating the impact of stress disturbance on the long-term conductivity of the proppant. This patent clarifies the stress disturbance law during the fracturing process of layered shale oil by constructing a geological model suitable for layered shale oil fracturing; and clarifies the influence of stress disturbance on proppant conductivity by conducting long-term conductivity tests on the proppant under different closure pressures, thus providing support for optimizing the proppant dosage in layered shale hydraulic fracturing. However, the method in this patent only involves the change in proppant conductivity under the influence of stress disturbance during fracturing operations, and does not consider the impact of changes in seepage field and stress field during fracturing fluid flowback and oil well production on proppant conductivity. Therefore, it is difficult to predict proppant conductivity under the conditions of changes in reservoir geostress field and pore pressure field throughout the entire life cycle of "fracturing-flowback-production". Summary of the Invention

[0005] This invention aims to address the technical problems existing in the background art by providing a proppant optimization method that considers the effective closure stress changes throughout the entire life cycle. Combining the actual production process of fracturing wells, an integrated numerical simulation model of "fracturing-flowback-production" based on fluid-structure interaction is constructed to obtain the reservoir stress field variation law and proppant-conductivity variation law throughout the entire life cycle. The optimal production solution is obtained through simulation, thereby obtaining the optimal proppant type, mesh size and sand concentration in the hydraulic fracturing process. This is of great significance for reducing hydraulic fracturing costs and improving hydraulic fracturing effects.

[0006] To achieve the above technical objectives, the present invention adopts the following technical solution:

[0007] A method for selecting a proppant that considers changes in effective closure stress throughout its entire life cycle, the method comprising the following steps:

[0008] Step S1: Construct a reservoir geomechanical model for the target area;

[0009] Step S2: Based on the reservoir geomechanical model of the target area, construct a geomechanical-seepage coupling model based on fluid-structure interaction and porosity elasticity.

[0010] Step S3: Dynamic propagation simulation of hydraulic fracturing;

[0011] Based on the geomechanics-seepage coupling model, the design parameters of oil well fracturing engineering are extracted to carry out dynamic numerical simulation of oil well hydraulic fracturing, and the basic geomechanics-seepage parameters and geostress field distribution of the reservoir after fracturing are obtained.

[0012] Step S4: Construct a fracturing fluid flowback model for fracturing wells and perform dynamic characterization of the geostress field during the flowback process;

[0013] Based on the geomechanics-seepage coupling model in step S2, the basic parameters of post-fracturing reservoir geomechanics-seepage and the distribution state of geostress field are extracted in step S3 as the initial conditions of the fracturing well flowback model. Combined with the fracturing well flowback regime, dynamic numerical simulation of fracturing well flowback is carried out to quantitatively characterize the changes in effective closure stress of fractures during the flowback process with fixed flowback volume and well simmering time.

[0014] Step S5: Dynamic characterization of the geostress field during the production process;

[0015] Based on the geomechanics-seepage coupling model, the reservoir stress field after backflow is used as the initial boundary condition to simulate the changes in reservoir geostress field and effective fracture closure stress during the production process.

[0016] Step S6: Long-term proppant conductivity test;

[0017] The effective closure stress of the cracks obtained during the backflow process and the production process was used as the closure stress loading value for proppant conductivity testing. The long-term conductivity of the proppant was then tested to obtain the relationship between the effective closure stress of the cracks and the long-term conductivity.

[0018] Step S7: Full life cycle reservoir numerical simulation and proppant selection;

[0019] The relationship between effective fracture closure stress and long-term conductivity in step S6 is extracted. Based on the changes in effective fracture closure stress during the flowback and production processes, the proppant type and sand concentration are initially screened. With the goal of maximizing cumulative oil production, reservoir numerical simulations of effective fracture closure stress throughout the entire life cycle of "fracturing-flowback-production" are conducted under different proppant types and sand concentrations to obtain the preferred proppant type, mesh size, and sand concentration.

[0020] Further, step S1 specifically includes:

[0021] Based on well logging data and rock mechanics test results, a reservoir geomechanical model is constructed, taking into account rock tensile failure, shear failure, and fracturing fluid loss.

[0022] Furthermore, the reservoir porosity, permeability, oil saturation, vertical stress, maximum horizontal principal stress, and minimum horizontal principal stress required in the reservoir geomechanical modeling process are obtained from well logging curves, while Young's modulus, Poisson's ratio, and fracture toughness parameters are obtained from rock mechanics tests.

[0023] Furthermore, in step S3, during the dynamic propagation simulation of hydraulic fracturing, the solid stress field in the geomechanics-seepage coupling model is solved and calculated using the dynamic relaxation method, and the flow pressure field within the fracture is calculated using the finite element method. Both are solved using the Picard iteration method.

[0024] Furthermore, the iterative calculation formula for the Picard iterative method is as follows:

[0025]

[0026] In the above formula: p k+1 / 2 The fluid pressure at step k+1 / 2 is MPa; A is the overall stiffness matrix; α is an empirical coefficient; F is the crack closure pressure, MPa; p k+1 The fluid pressure at step k+1 is in MPa; w k The seam width at step k is in mm; w k+1 The seam width at step k+1, in mm; Δw k Let Δt be the dynamic seam width at step k, in mm; k Let be the dynamic time of the k-th step, s; u k+1 Let be the displacement at step k+1, in mm.

[0027] Furthermore, in step S4, the backflow system includes: determining a well-clogging time and determining the daily backflow liquid volume.

[0028] Furthermore, in step S4, when performing dynamic numerical simulation of flowback in fractured wells, a fixed stress iterative coupling method is used to carry out geomechanical-seepage coupling simulation, and an explicit coupling method is used to simulate the fracture system to obtain the basic geomechanical-seepage parameters and geostress field distribution of the reservoir after fractured wells with a fixed flowback volume and well shut-in time.

[0029] Extract the reservoir stress field change value after flowback and quantitatively characterize the effective closure stress change of fractures during the flowback process with a fixed flowback volume and well shut-in time.

[0030] Furthermore, in the solution process of step S4, the finite element method is used to discretize the geomechanical equations, the finite difference method is used to discretize the fluid flow equations, and the displacement discontinuity method is used to discretize the crack propagation equations.

[0031] Furthermore, step S5 specifically includes:

[0032] Based on the geomechanics-seepage coupling model in step S2, the reservoir stress field after backflow is used as the initial boundary condition, and the goal is to maximize the cumulative oil production over three years. The oil well production process is simulated to obtain dynamic production values ​​and to obtain the changes in reservoir geostress field and effective fracture closure stress during the production process.

[0033] Further, step S7 specifically includes:

[0034] Extract the relationship between the effective closure stress of the crack and the long-term conductivity in step S6. Based on the changes in the effective closure stress of the crack during the backflow process and the production process, preliminarily screen the proppant type and sand concentration.

[0035] Based on the preliminary screening of proppant type and sand concentration, the relationship between effective fracture closure stress and long-term conductivity in step S6 is extracted. Numerical simulation of effective fracture closure stress in reservoirs is carried out under different proppant types and sand concentrations throughout the entire life cycle of "fracturing-flowback-production" with a three-year cycle. The goal is to maximize the cumulative oil production over three years to obtain the optimal production solution, thereby obtaining the preferred proppant type, mesh size and sand concentration.

[0036] Compared with the prior art, the beneficial effects of the present invention are:

[0037] This invention provides a proppant optimization method that considers the effective closure stress changes throughout the entire life cycle. Combined with the actual production process of fractured wells, an integrated numerical simulation model of "fracture-flowback-production" based on fluid-structure interaction is constructed to obtain the reservoir stress field variation law throughout the entire life cycle of the three stages of "fracture-flowback-production". Then, long-term conductivity tests of the proppant are conducted. By changing the closure pressure value of the long-term conductivity test of the proppant, the variation law of proppant-conductivity (type-particle size-sanding concentration-dynamic closure stress) throughout the entire life cycle is obtained. Finally, with the goal of maximizing cumulative oil production, the optimal production solution is obtained, thus yielding the optimized proppant type, mesh size, and sanding concentration.

[0038] The method of this invention comprehensively considers the influence of changes in the seepage field and stress field during the fracturing fluid flowback and oil well production on the long-term conductivity of the proppant, thus providing a new method for selecting the appropriate proppant type and sand concentration for fracturing wells. This is of great significance for reducing hydraulic fracturing costs and improving hydraulic fracturing effects. It also provides theoretical support for optimizing the reasonable fracture conductivity during hydraulic fracturing and guiding oilfield development. Attached Figure Description

[0039] Figure 1 This is a flowchart of a proppant selection method considering the changes in effective closure stress throughout the entire life cycle, according to an embodiment of the present invention.

[0040] Figure 2 This is the stress field distribution state of a fracturing well according to an embodiment of the present invention;

[0041] Figure 3 This is a graph showing the changes in injection pressure and fracture width in a fracturing well according to an embodiment of the present invention.

[0042] Figure 4The following is a dynamic simulation diagram of the flowback process of a fracturing well according to an embodiment of the present invention, wherein (a) is a schematic diagram of the fracturing fluid flowback model of the fracturing well, and (b) is a diagram of the distribution state of the reservoir geostress field after fracturing with a fixed flowback volume and a simmering time.

[0043] Figure 5 This is a diagram showing the changes in the reservoir stress field during the production process according to an embodiment of the present invention.

[0044] Figure 6 This is a graph showing the test results of the long-term flow conductivity of the proppant under different sand concentrations of 20 / 40 mesh quartz sand in an embodiment of the present invention.

[0045] Figure 7 The graph shows the test results of the long-term flow conductivity of the proppant under different sand concentrations of 30 / 50 mesh ceramsite in an embodiment of the present invention. Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0047] Example 1

[0048] This invention provides a proppant selection method that considers the changes in effective closure stress throughout the entire life cycle. Based on well logging data and rock mechanics test results, this method uses a numerical simulation model to obtain the changes in reservoir stress field during the flowback and production processes. Based on this, long-term conductivity tests of the proppant are conducted, thus providing a basis for the selection of proppants for fracturing wells.

[0049] like Figure 1 As shown, the method specifically includes the following steps:

[0050] Step S1: Construct a reservoir geomechanical model for the target area;

[0051] Based on well logging data and rock mechanics test results, a reservoir geomechanical model is constructed, taking into account rock tensile failure, shear failure, and fracturing fluid loss.

[0052] The reservoir porosity, permeability, oil saturation, vertical stress, maximum horizontal principal stress, and minimum horizontal principal stress required in the reservoir geomechanical modeling process are obtained from well logging curves, while parameters such as Young's modulus, Poisson's ratio, and fracture toughness are obtained from rock mechanics tests.

[0053] Step S2: Based on the reservoir geomechanical model of the target area, construct a geomechanical-seepage coupling model based on fluid-structure interaction and porosity elasticity.

[0054] Under fluid-structure interaction, the seepage field affects the formation stress distribution by applying flow pressure and volume force to the matrix pores and natural fracture surfaces; stress, in turn, affects the seepage capacity by changing the volume strain and physical properties of the matrix and natural fracture pores.

[0055] Step S3: Dynamic propagation simulation of hydraulic fracturing;

[0056] Based on the geomechanics-seepage coupling model in step S2, the design parameters of oil well fracturing engineering are extracted to carry out dynamic numerical simulation of oil well hydraulic fracturing, and the basic parameters of reservoir geomechanics-seepage and the distribution of geostress field after fracturing are obtained.

[0057] In the geomechanics-seepage coupled model, the solid stress field (block governing equation) is solved and calculated using the dynamic relaxation method, while the flow pressure field within the fracture (continuity equation) is calculated using the finite element method. Both are solved using the Picard iteration method.

[0058] The iterative calculation formula for the Picard iterative method is as follows:

[0059]

[0060] In equation (1) above: p k+1 / 2 The fluid pressure at step k+1 / 2 is MPa; A is the overall stiffness matrix; α is an empirical coefficient; F is the crack closure pressure, MPa; p k+1 The fluid pressure at step k+1 is in MPa; w k The seam width at step k is in mm; w k+1 The seam width at step k+1, in mm; Δw k Let Δt be the dynamic seam width at step k, in mm; k Let be the dynamic time of the k-th step, s; u k+1 Let be the displacement at step k+1, in mm.

[0061] Step S4: Construct a fracturing fluid flowback model for fracturing wells and perform dynamic characterization of the geostress field during the flowback process;

[0062] Based on the geomechanics-seepage coupling model in step S2, the post-fracturing geomechanical parameters of the fractured well in step S3 are extracted as the initial boundary conditions of the fractured well flowback model. Combined with the fractured well flowback regime (i.e., determining a well-closing time and the daily flowback fluid volume), dynamic numerical simulation of fractured well flowback is carried out. The fixed stress iterative coupling method is used to carry out geomechanics-seepage coupling simulation, and the explicit coupling method is used to simulate the fracture system. The basic geomechanics-seepage parameters and geostress field distribution of the fractured well post-fracturing reservoir are obtained at a fixed flowback volume and well-closing time. The change value of the reservoir stress field after flowback is extracted to quantitatively characterize the change of effective closure stress of the fracture at a fixed flowback volume and well-closing time during the flowback process.

[0063] In the solution process, the requirements for variable conservation and numerical stability of solving subproblems are fully considered. The finite element method is used to discretize the geomechanical equations, the finite difference method is used to discretize the fluid flow equations, and the displacement discontinuity method is used to discretize the crack propagation equations.

[0064] Step S5: Dynamic characterization of the geostress field during the production process;

[0065] Based on the geomechanics-seepage coupling model in step S2, the reservoir stress field after backflow is used as the initial boundary condition, and the goal is to maximize the cumulative oil production over three years. The oil well production process is simulated to obtain dynamic production values ​​and to obtain the changes in reservoir geostress field and effective fracture closure stress during the production process.

[0066] Step S6: Long-term proppant conductivity test;

[0067] The effective closure stress of the crack obtained during the backflow process and the production process was used as the closure stress loading value for proppant conductivity testing. The long-term conductivity of the proppant was then tested to obtain the relationship between the effective closure stress of the crack and the long-term conductivity.

[0068] Step S7: Full life cycle reservoir numerical simulation and proppant selection;

[0069] Extract the relationship between the effective closure stress of the crack and the long-term conductivity in step S6. Based on the changes in the effective closure stress of the crack during the backflow process and the production process, preliminarily screen the proppant type and sand concentration.

[0070] Based on the preliminary screening of proppant type and sand concentration, the relationship between effective fracture closure stress and long-term conductivity in step S6 is extracted. Numerical simulation of effective fracture closure stress in reservoirs is carried out under different proppant types and sand concentrations throughout the entire life cycle of "fracturing-flowback-production" with a three-year cycle. The goal is to maximize the cumulative oil production over three years to obtain the optimal production solution, thereby obtaining the preferred proppant type, mesh size and sand concentration.

[0071] Example 2

[0072] This invention constructs a high-precision reservoir geomechanical model and, based on fluid-structure interaction reservoir simulation technology, predicts the fracture conductivity at different production stages throughout the entire life cycle of "fracturing-flowback-production". It obtains the variation law of reservoir geostress field and pore pressure field throughout the entire life cycle of "fracturing-flowback-production", and then simulates and optimizes the reasonable fracture conductivity at different production stages with the goal of maximizing cumulative oil production.

[0073] This invention employs the method of Example 1 to analyze the proppant conductivity requirements of tight sandstone oil reservoirs in a certain region. It uses the method of maximizing cumulative oil production at each stage as the research objective to match formation conductivity, and combines the results of long-term proppant conductivity experiments in the laboratory to optimize proppant selection. The specific process is as follows:

[0074] Step S1: Construct a reservoir geomechanical model for the target area;

[0075] Based on well logging data and rock mechanics test results, a reservoir geomechanical model is constructed, taking into account rock tensile failure, shear failure, and fracturing fluid loss.

[0076] Step S2: Based on the reservoir geomechanical model of the target area, construct a geomechanical-seepage coupling model based on fluid-structure interaction and porosity elasticity.

[0077] Step S3: Dynamic propagation simulation of hydraulic fracturing;

[0078] Based on the geomechanics-seepage coupling model in step S2, the design parameters of oil well fracturing engineering are extracted, and dynamic numerical simulation of oil well hydraulic fracturing is carried out to obtain the basic parameters of reservoir geomechanics-seepage and the distribution state of geostress field after fracturing.

[0079] Among them, the distribution of the in-situ stress field in the fractured well is as follows: Figure 2 As shown, the changes in injection pressure and slit width are as follows: Figure 3 As shown.

[0080] Step S4: Construct a fracturing fluid flowback model for fracturing wells and perform dynamic characterization of the geostress field during the flowback process;

[0081] Based on the geomechanics-seepage coupling model in step S2, the post-fracturing geomechanics parameters of the fractured well in step S3 are extracted as the initial boundary conditions of the fractured well flowback model. Combined with the fractured well flowback regime (i.e., determining a well-clogging time and the daily flowback liquid volume), dynamic numerical simulation of fractured well flowback is carried out. The fixed stress iterative coupling method is used to carry out geomechanics-seepage coupling simulation, and the explicit coupling method is used to simulate the fracture system. The basic geomechanics-seepage parameters and geostress field distribution of the fractured well reservoir after fracturing are obtained with a fixed flowback volume and well-clogging time.

[0082] Among them, the dynamic simulation results of the flowback process in fractured wells are as follows: Figure 4 As shown, (a) is a schematic diagram of the fracturing fluid flowback model of a fracturing well, and (b) is a diagram of the reservoir stress field distribution after fracturing with a fixed flowback volume and simmering time.

[0083] Extract the reservoir stress field change value after flowback and quantitatively characterize the effective closure stress change of fractures during the flowback process with a fixed flowback volume and well shut-in time.

[0084] Step S5: Dynamic characterization of the geostress field during the production process;

[0085] Based on the geomechanics-seepage coupling model in step S2, the reservoir stress field after backflow is used as the initial boundary condition to simulate the oil well production process. With the goal of maximizing the cumulative oil production over three years, a dynamic numerical simulation of production is conducted to obtain the changes in the reservoir geostress field and the effective closure stress of fractures during the production process.

[0086] Among them, the changes in the reservoir stress field during the production process are as follows: Figure 5 As shown.

[0087] Step S6: Long-term proppant conductivity test;

[0088] The effective closure stress of the crack obtained during the backflow process and the production process was used as the closure stress loading value for proppant conductivity testing. The long-term conductivity of the proppant was then tested to obtain the relationship between the effective closure stress of the crack and the long-term conductivity.

[0089] Step S7: Full life cycle reservoir numerical simulation and proppant selection;

[0090] Extracting the relationship between effective crack closure stress and long-term conductivity in step S6, and based on the changes in effective crack closure stress during the backflow and production processes, preliminary screening of proppant type and sand concentration is conducted; for example... Figure 6 The results show the long-term conductivity test results of proppant under different sand concentrations of 20 / 40 mesh quartz sand. Figure 7 The results show the long-term conductivity test results of proppant under different sand concentrations for 30 / 50 mesh ceramsite. Comparison reveals that under a closure stress of 30-40 MPa, 20 / 40 mesh quartz sand (sand concentration of 5 / 7 / 9 kg / m³) is recommended. 2 ).

[0091] Based on the preliminary screening of proppant type and sand concentration, the relationship between effective fracture closure stress and long-term conductivity in step S6 is extracted. Numerical simulation of effective fracture closure stress in reservoirs is carried out under different proppant types and sand concentrations throughout the entire life cycle of "fracturing-flowback-production" with a 3-year cycle. With the goal of maximizing the cumulative oil production over three years, the optimal solution for production is obtained, thus yielding the preferred proppant type, mesh size, and sand concentration.

[0092] Simulation results show that for 20 / 40 mesh quartz sand, when its sand concentration is 7 kg / m³ 2 At this time, the proppant exhibits optimal conductivity. Therefore, based on the conductivity requirements and test results, 20 / 40 mesh quartz sand was ultimately selected, with a proppant concentration of 7 kg / m³. 2 .

[0093] The above description is merely an embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the scope of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for selecting a proppant that considers the effective closure stress variation throughout its entire life cycle, characterized in that, The method includes the following steps: Step S1: Construct a reservoir geomechanical model for the target area; Step S2: Based on the reservoir geomechanical model of the target area, construct a geomechanical-seepage coupling model based on fluid-structure interaction and porosity elasticity. Step S3: Dynamic propagation simulation of hydraulic fracturing; Based on the geomechanics-seepage coupling model, the design parameters of oil well fracturing engineering are extracted to carry out dynamic numerical simulation of oil well hydraulic fracturing, and the basic geomechanics-seepage parameters and geostress field distribution of the reservoir after fracturing are obtained. Step S4: Construct a fracturing fluid flowback model for fracturing wells and perform dynamic characterization of the geostress field during the flowback process; Based on the geomechanics-seepage coupling model in step S2, the basic parameters of post-fracturing reservoir geomechanics-seepage and the distribution state of geostress field are extracted in step S3 as the initial conditions of the fracturing well flowback model. Combined with the fracturing well flowback regime, dynamic numerical simulation of fracturing well flowback is carried out to quantitatively characterize the changes in effective closure stress of fractures during the flowback process with fixed flowback volume and well simmering time. Step S5: Dynamic characterization of the geostress field during the production process; Based on the geomechanics-seepage coupling model, the reservoir stress field after backflow is used as the initial boundary condition to simulate the changes in reservoir geostress field and effective fracture closure stress during the production process. Step S6: Long-term proppant conductivity test; The effective closure stress of the cracks obtained during the backflow process and the production process was used as the closure stress loading value for proppant conductivity testing. The long-term conductivity of the proppant was then tested to obtain the relationship between the effective closure stress of the cracks and the long-term conductivity. Step S7: Full life cycle reservoir numerical simulation and proppant selection; The relationship between effective fracture closure stress and long-term conductivity in step S6 is extracted. Based on the changes in effective fracture closure stress during the flowback and production processes, the proppant type and sand concentration are initially screened. With the goal of maximizing cumulative oil production, reservoir numerical simulations of effective fracture closure stress throughout the entire life cycle of "fracturing-flowback-production" are conducted under different proppant types and sand concentrations to obtain the preferred proppant type, mesh size, and sand concentration.

2. The method according to claim 1, characterized in that, Step S1 specifically includes: Based on well logging data and rock mechanics test results, a reservoir geomechanical model is constructed, taking into account rock tensile failure, shear failure, and fracturing fluid loss.

3. The method according to claim 2, characterized in that, The reservoir porosity, permeability, oil saturation, vertical stress, maximum horizontal principal stress, and minimum horizontal principal stress required in the reservoir geomechanical modeling process are obtained from well logging curves, while Young's modulus, Poisson's ratio, and fracture toughness parameters are obtained from rock mechanics tests.

4. The method according to claim 1, characterized in that, In step S3, during the dynamic propagation simulation of hydraulic fracturing, the solid stress field in the geomechanics-seepage coupling model is solved using the dynamic relaxation method, and the flow pressure field within the fracture is calculated using the finite element method. Both are solved using the Picard iteration method.

5. The method according to claim 4, characterized in that, The iterative calculation formula for Picard's iterative method is as follows: In the above formula: p k+1 / 2 The fluid pressure at step k+1 / 2 is MPa; A is the overall stiffness matrix; α is an empirical coefficient; F is the crack closure pressure, MPa; p k+1 The fluid pressure at step k+1 is in MPa; w k The seam width at step k is in mm; w k+1 The seam width at step k+1, in mm; Δw k Let Δt be the dynamic seam width at step k, in mm; k Let be the dynamic time of the k-th step, s; u k+1 Let be the displacement at step k+1, in mm.

6. The method according to claim 1, characterized in that, In step S4, the backflow system includes: determining a well-closing time and determining the daily backflow liquid volume.

7. The method according to claim 1, characterized in that, In step S4, when performing dynamic numerical simulation of flowback in fractured wells, a fixed stress iterative coupling method is used to carry out geomechanics-seepage coupling simulation, and an explicit coupling method is used to simulate the fracture system to obtain the basic geomechanics-seepage parameters and geostress field distribution of the reservoir after fractured wells with a fixed flowback volume and well shut-in time. Extract the reservoir stress field change value after flowback and quantitatively characterize the effective closure stress change of fractures during the flowback process with a fixed flowback volume and well shut-in time.

8. The method according to claim 7, characterized in that, In step S4, the solution process employs the finite element method to discretize the geomechanical equations, the finite difference method to discretize the fluid flow equations, and the displacement discontinuity method to discretize the crack propagation equations.

9. The method according to claim 1, characterized in that, Step S5 specifically includes: Based on the geomechanics-seepage coupling model in step S2, the reservoir stress field after backflow is used as the initial boundary condition, and the goal is to maximize the cumulative oil production over three years. The oil well production process is simulated to obtain dynamic production values ​​and to obtain the changes in reservoir geostress field and effective fracture closure stress during the production process.

10. The method according to claim 1, characterized in that, Step S7 specifically includes: Extract the relationship between the effective closure stress of the crack and the long-term conductivity in step S6. Based on the changes in the effective closure stress of the crack during the backflow process and the production process, preliminarily screen the proppant type and sand concentration. Based on the preliminary screening of proppant types and sand concentrations, the relationship between effective fracture closure stress and long-term conductivity in step S6 was extracted. Using a three-year cycle, the entire lifecycle of "fracturing-flowback-production" was analyzed under different proppant types and sand concentrations. The numerical simulation of fractured reservoirs with effective closure stress, with the objective of maximizing cumulative oil production over three years, yielded the optimal production solution. This allows for the determination of the optimal proppant type, mesh size, and sand concentration.

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