A method for optimizing the design of flowback production after three-dimensional fracturing well pattern in shale oil

Abstract

The present invention discloses a shale oil three-dimensional fracturing well network post-pressure backflow production optimization design method, which belongs to the field of oil and gas field development technology, and includes the following steps: step 1, sorting out the basic parameters of shale oil reservoirs; step 2, establishing a mathematical model of the backflow production process of the fracturing horizontal wells in the shale oil reservoir; step 3, solving the mathematical model of the backflow production process of the fracturing horizontal wells in the shale oil reservoir based on the Newton-Raphson method; step 4, analyzing the dynamic nodes of the reservoir-wellbore-nozzle flow system; step 5, optimizing the nozzle working system based on the genetic algorithm; step 6, designing a three-dimensional fracturing well network backflow production plan for the shale oil reservoir. The present invention can realize the simulation of the backflow production process and the optimization of the production plan of the three-dimensional fracturing well network in the shale oil reservoir, and provide theoretical guidance and technical support for the three-dimensional development of shale oil reservoirs.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas field development, and specifically relates to a method for optimizing the design of post-fracturing flowback production of a shale oil three-dimensional fracturing well network. Background Art

[0002] Most of my country's shale oil reservoirs are low-permeability or ultra-low-permeability reservoirs with low formation energy and strong reservoir heterogeneity, requiring large-scale hydraulic fracturing to transform the formation. "Horizontal well + multi-stage hydraulic fracturing" is a common method for developing shale oil at home and abroad. The complex fractures formed after horizontal well fracturing can increase shale oil production capacity, but it is still facing the problem of single well fracturing effect is not obvious and the reservoir utilization is poor. Three-dimensional development can greatly reduce development costs, expand the effective fracturing network from the local scale of a single well to the global scale of multiple wells and even the entire oil and gas field development, and greatly increase the ultimate recoverable reserves (EUR) of a single well assessment. It has become the current research direction in the field of shale oil in China.

[0003] The optimization design of shale oil three-dimensional fracturing well network development plan needs to clarify the flow law of reservoir fluid during the process of well shut-off, backflow and production after horizontal well fracturing. However, the current traditional analytical / semi-analytical seepage model basically only targets a single stage, and it is difficult to simulate the above flow characteristics in the whole cycle. Although the numerical simulation method can consider the above process at the same time, it has the disadvantages of poor solution convergence and long calculation time. In response to this problem, the present invention considers the fracturing fluid filtration and imbibition displacement effect, establishes a flow simulation method that can simultaneously characterize the whole process of backflow and production, and combines the dynamic node analysis method proposed in the patent with publication number CN116050298A to construct a shale oil three-dimensional fracturing well network post-fracturing backflow production optimization design method based on genetic algorithm, which can realize the reservoir fluid flow law in the backflow production process of shale oil reservoirs and the optimization design of three-dimensional fracturing well network development plan. Summary of the invention

[0004] In order to solve the above problems, the present invention proposes a method for optimizing the design of post-fracturing flowback production of shale oil three-dimensional fracturing well network. Based on the basic parameter data of shale oil reservoirs, a flowback production process model and a genetic optimization algorithm for fracturing horizontal wells in shale oil reservoirs are established to form a method for accurately simulating the dynamic flow laws of the flowback production process and optimizing the design of production plans, thereby realizing the simulation of the flowback production process and the optimization of the production plan of the three-dimensional fracturing well network in shale oil reservoirs, and providing theoretical guidance and technical support for the three-dimensional development of shale oil reservoirs.

[0005] The technical solution of the present invention is as follows:

[0006] A method for optimizing the design of flowback production after shale oil three-dimensional fracturing well pattern fracturing, comprising the following steps:

[0007] Step 1: Arrange the basic parameters of shale oil reservoirs;

[0008] Step 2, establishing a mathematical model of the flowback production process of fractured horizontal wells in shale oil reservoirs;

[0009] Step 3, solving the mathematical model of the flowback production process of fractured horizontal wells in shale oil reservoirs based on the Newton-Raphson method;

[0010] Step 4, analyzing the dynamic nodes of the reservoir-wellbore-nozzle flow system;

[0011] Step 5: Optimize the nozzle working system based on genetic algorithm;

[0012] Step 6: Design a flowback production plan for the three-dimensional fracturing well network in the rock oil reservoir.

[0013] Furthermore, in step 1, the basic parameters include reservoir pressure, reservoir thickness, porosity, permeability, saturation, fluid relative permeability, fluid viscosity and volume coefficient, artificial fracture half-length, width, number of fractures, fracture spacing, fracture area length, horizontal well length, well inclination, and tubing size.

[0014] Furthermore, in step 2, material balance equations for different regions of the reservoir matrix, fracture network, and artificial fractures are established respectively;

[0015] The material balance equation in the reservoir matrix region is:

[0016] (1);

[0017] (2);

[0018] In the formula, , They are the cumulative oil production and cumulative water production in the matrix; , They are the oil and water production in the matrix area; is the time step; is the imbibition rate per unit time; is the number of artificial cracks; is the distance the pressure wave propagates within the matrix region; is the geometric area of ​​the matrix dynamic discharge area; is the initial porosity in the matrix region; , are the volume coefficients of the oil phase and the water phase under the original conditions, respectively; , are the volume coefficients of the oil phase and the water phase, respectively; , are the initial oil saturation and water saturation in the matrix area, respectively; is the porosity within the matrix region; is the average pressure in the dynamic discharge area of ​​the matrix; , They are the oil and water saturations in the matrix dynamic discharge area;

[0019] The material balance equation in the fracture network region is:

[0020] (3);

[0021] (4);

[0022] In the formula, , are the cumulative oil production and cumulative water production in the fracture network area, respectively; , They are oil production and water production within the fracture network area; For crack half length; is the distance reached by the pressure wave in the fracture network area; is the reservoir thickness; is the average porosity within the dynamic drainage area of ​​the fracture network at the original formation pressure; , They are the initial oil saturation and water saturation in the fracture network area; is the average porosity within the dynamic drainage area of ​​the fracture network at average pressure; is the average pressure within the dynamic discharge area of ​​the fracture network; , They are the oil saturation and water saturation in the dynamic drainage area of ​​the fracture network;

[0023] According to the different pressure conditions in the artificial fracture area, the material balance equations in different areas of the artificial fracture are established;

[0024] The material balance equation when the artificial fracture pressure is greater than the fracture network pressure is:

[0025] (5);

[0026] (6);

[0027] The material balance equation when the artificial fracture pressure is less than the fracture network pressure is:

[0028] (7);

[0029] (8);

[0030] In the formula, , They are the cumulative oil production and cumulative water production in the artificial fracture area; , They are oil phase yield and water phase yield respectively; is the cumulative filtration loss from the artificial fracture area to the fracture network; is the filtration rate; is the crack width of the artificial crack; is the distance reached by the pressure wave in the artificial fracture area; is the height of the artificial crack; is the initial porosity within the artificial fracture area; , They are the initial oil and water saturations in the artificial fracture area; is the porosity at the average pressure in the artificial fracture area; is the average pressure in the dynamic discharge area of ​​the artificial fracture; , They are the oil and water saturations in the dynamic discharge area of ​​artificial fractures.

[0031] Furthermore, in step 3, the material balance equation of the oil-water phase in the artificial fracture, fracture network and matrix is ​​abstracted into the following form:

[0032] (9);

[0033] In the formula, Represents the average pressure in the dynamic discharge area of ​​artificial fractures; Represents the average water saturation in the dynamic discharge area of ​​artificial fractures; Represents the average pressure in the dynamic discharge area of ​​the seam network; Represents the average water saturation within the dynamic discharge area of ​​the fracture network; Represents the average pressure within the dynamic discharge area of ​​the matrix; Represents the average water saturation within the dynamic discharge area of ​​the matrix; to The equation symbol for the nonlinear equation to be solved;

[0034] The material balance equation is solved using the Newton-Raphson iteration method. The solution is shown in formula (10):

[0035] (10);

[0036] (11);

[0037] In the formula, , They are sequence Pressure and saturation data after iterations; is the nonlinear material balance equations to be solved; The system of equations is The partial derivative of .

[0038] Furthermore, in step 3, the Newton-Raphson method is used to solve the material balance equations of the reservoir matrix, fracture network, and artificial fracture area, and the pressure and saturation data in different areas at any time are obtained, and finally the cross-flow rate of the reservoir fluid between the matrix-fracture network, the fracture network-artificial fractures, and the daily production and cumulative production data of the fractured horizontal wells are obtained; the solution process of the material balance equation is as follows:

[0039] Step 3.1. Input the basic physical parameters of artificial fractures, fracture networks and matrix areas, including pressure, permeability, porosity, fracture half-length, fracture width, relative permeability, and set the calculation time step. With the total number of time steps ; Initialize the simulation time step index , duration ;

[0040] Step 3.2: Determine the size of the dynamic leakage area and the relative permeability value according to the corresponding pressure and saturation data at each time, calculate the oil-water two-phase production and the flow rate between each area based on the capacity calculation formula, and calculate and size;

[0041] Step 3.3: Update the pressure and saturation data for the next time step based on formula (10) , determine whether the pressure and saturation data calculated in step 3.2 meet the allowable error, that is, determine whether If the convergence condition is not met, the updated Substitute into step 3.2 and recalculate until convergence;

[0042] Step 3.4, based on the converged pressure and saturation data, calculate the oil and water production rates at the next time step, and calculate the cumulative oil and water production according to the rates, and simultaneously update the pressure and saturation formation physical property parameters;

[0043] Step 3.5: Determine whether it is the last time step, that is, determine Is it equal to ; If it is not the last time step, then the time step index is incremented by 1, the duration is updated, and then step 3.2 is restarted to simulate the reservoir production dynamics of the next time step; if the last time step has been simulated, the simulation is ended and the calculation results are output.

[0044] Furthermore, the specific process of step 4 is as follows:

[0045] Step 4.1, based on the mathematical model of the flowback production process of the fractured horizontal well in the shale oil reservoir, simulate the production law under different bottom hole flow pressures to obtain the multiphase velocity, average pressure and saturation in the dynamic drainage area, and water content corresponding to the production conditions;

[0046] Step 4.2, according to the calculation results of step 4.1, draw the inflow dynamic curve, the horizontal axis is the oil production, and the vertical axis is the bottom hole flow pressure;

[0047] Step 4.3, given the initial value of water cut, calculate the pressure drop loss under different oil production rates based on the wellbore gas-liquid two-phase flow model and the nozzle critical flow model, generate the outflow dynamic curve, and the intersection of the outflow curve and the inflow curve is the coordination point;

[0048] The calculation formula of the wellbore gas-liquid two-phase flow model is:

[0049] (12);

[0050] In the formula, is the average pressure in the horizontal well pipeline; is the distance of axial flow; is the liquid holdup, which indicates the volume fraction of the liquid phase in the gas-liquid mixture during the flow process; is the liquid density; is the gas phase density; is the acceleration due to gravity; is the angle between the pipe and the horizontal; is the flow resistance coefficient; is the mass flow rate of the mixed liquid; is the average flow rate of the mixture; is the inner diameter of the oil pipe; is the flow cross-sectional area of ​​the oil pipe; is the gas phase superficial velocity;

[0051] The calculation formula of the nozzle critical flow model is:

[0052] (13);

[0053] In the formula, is the liquid production; is the nozzle diameter; is the production gas-oil ratio; is the wellhead tubing pressure; is the moisture content;

[0054] Step 4.4, based on the converged coordination point parameters, the average pressure and average saturation in the dynamic discharge area are obtained, and then used as the initial parameters for the next time step simulation;

[0055] Step 4.5: Repeat steps 4.1 to 4.4 until the last time step is simulated and all calculations are completed; based on the calculation results of each time step, the change patterns of reservoir pressure, saturation and production capacity under different nozzle sizes are obtained.

[0056] Furthermore, in step 5, based on a genetic algorithm, with cumulative oil production as the optimization target and nozzle size as the optimization parameter, a nozzle working system optimization method for the flowback production process of a horizontal well in a shale oil reservoir is established;

[0057] The optimization objective function equation and constraints established are as follows:

[0058] (14);

[0059] In the formula, is the cumulative oil production; is the nozzle size; It is The nozzle size simulated in each time step; is the total duration of the simulation; is the time step; It is The nozzle size simulated in each time step; is the remainder function.

[0060] Furthermore, the specific process of step 5 is as follows:

[0061] Step 5.1, Population initialization: First initialize the evolutionary generation , Maximum evolutionary generations , the number of individuals in each generation , randomly generate individual values ​​to form the number of individuals in the initial group ; is the initial evolutionary generation; among them, the number of individuals Corresponding adjustment times, number of individuals The set of nozzle sizes corresponding to each adjustment, and the individual corresponding nozzle sizes;

[0062] Step 5.2: Individual fitness evaluation: Calculate the group fitness based on the optimization objective function in formula (14) The individual fitness in the group is used to evaluate the quality of the current group; the individual fitness corresponds to the cumulative oil production, and the higher the cumulative oil production, the better;

[0063] Step 5.3: Perform selection, crossover and mutation operations to adjust and sort the individuals in the group so that the group Generate the next generation of population, that is, generate different nozzle size sequences to obtain the optimal cumulative oil production;

[0064] Among them, the selection operation is based on the individual fitness evaluation in step 5.2. The selection operator is applied to the group to select individuals with high fitness for crossover and mutation;

[0065] The crossover operation generates new individuals by replacing and reorganizing part of the structure of two parent individuals, and performs multi-point crossover and uniform crossover at the crossover point;

[0066] The mutation operation is to make random adjustments to the structure of some individuals in the population;

[0067] Step 5.4, termination condition judgment: If the evolutionary generation reaches the maximum evolutionary generation, the individual with the maximum fitness in the evolutionary process is output as the optimal result and the calculation is terminated; otherwise, repeat steps 5.2-5.3 until the maximum evolutionary generation is reached.

[0068] Furthermore, the specific process of step 6 is: according to the genetic algorithm optimization result of step 5, an optimized choke working system sequence diagram is obtained, and the choke sizes corresponding to the horizontal wells in the three-dimensional well network at different production times are determined according to the sequence diagram, so as to design the choke working system sequence in the flowback production process of the three-dimensional fracturing well network in the shale oil reservoir, and formulate a development plan to obtain the optimal mining effect.

[0069] The beneficial technical effects brought about by the present invention are as follows.

[0070] Finely characterize the fluid flow state in each area during the flowback production stage: Currently, the simulation research on the flowback and production stages is isolated. The traditional analytical / semi-analytical seepage model basically only targets a single stage, and it is difficult to simulate the above flow characteristics throughout the entire cycle. Although the numerical simulation method can consider the above processes at the same time, it has the disadvantages of poor solution convergence and long calculation time. The present invention considers the fracturing fluid loss and imbibition displacement effects, constructs material balance equations for artificial fractures, fracture networks and matrix areas, and establishes a flow simulation method that can simultaneously characterize the entire process of flowback and production. The solution process is simple and there is no need to solve large matrices.

[0071] Design of three-dimensional fracturing well network production plan based on genetic algorithm: When formulating the production and development strategy of shale oil three-dimensional fracturing well network, the selection of the nozzle size of the fracturing horizontal well is crucial. The larger the better. The reservoir's fracture network heterogeneity, compression coefficient and other parameters will affect the optimal nozzle size. The present invention establishes a nozzle working system optimization method for the shale oil fracturing horizontal well backflow production process based on genetic algorithm. It can optimize the replacement time and size of the nozzle according to the reservoir physical parameters, and predict the production capacity of the fracturing horizontal well backflow production stage, providing theoretical and technical support for the development of shale oil three-dimensional fracturing well network and the formulation of production strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] FIG1 is a flow chart of a method for optimizing design of post-fracturing flowback production of a three-dimensional fracturing well pattern in a shale oil reservoir according to the present invention.

[0073] Figure 2 It is a schematic diagram of the development of a three-dimensional fracturing well network for a shale oil reservoir in an embodiment of the present invention.

[0074] Figure 3 Schematic diagram of crude oil volume coefficient and viscosity data in an embodiment of the present invention.

[0075] Figure 4 Schematic diagram of oil-water relative permeability data in an embodiment of the present invention.

[0076] Figure 5 It is a calculation flow chart of the material balance equation of the present invention.

[0077] Figure 6 This is a diagram showing the calculation results of the flow rate during the flowback stage of a horizontal well in a shale oil reservoir fracture in an embodiment of the present invention.

[0078] Figure 7 This is a diagram showing the calculation results of the oil and water production during the production phase of a fractured horizontal well in a shale oil reservoir in an embodiment of the present invention.

[0079] Figure 8 It is the optimization flow chart of the genetic algorithm of the present invention.

[0080] Fig. 9 This is a diagram of the nozzle optimization results of horizontal well 1 based on genetic algorithm in an embodiment of the present invention.

[0081] Fig.10 This is a diagram of the nozzle optimization results of horizontal well 2 based on genetic algorithm in an embodiment of the present invention. DETAILED DESCRIPTION

[0082] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0083] The present invention proposes a method for optimizing the design of post-fracturing flowback production of shale oil three-dimensional fracturing well network, which is mainly used for the development of three-dimensional fracturing well network in shale oil reservoirs. This method can simulate the fluid flow process and design the development plan in the process of three-dimensional fracturing well network development of shale oil by establishing a mathematical model for solving the flowback production process of fracturing horizontal wells in shale oil reservoirs and optimizing the genetic algorithm according to the actual reservoir reservoir physical properties, fluid physical properties and basic parameters such as fracturing horizontal wells. Figure 1 As shown, the following steps are included:

[0084] Step 1: Arrange the basic parameters of shale reservoirs as input data for calculating the fluid dynamics of the fracturing horizontal well flowback production process in the subsequent step 3 and the dynamic node analysis of the reservoir-wellbore-nozzle flow system in step 4.

[0085] The basic parameters of shale oil reservoir reservoir and fluid properties, three-dimensional fracturing well pattern fracturing horizontal wells, etc. are sorted out. The schematic diagram of the three-dimensional fracturing well pattern development of the shale oil reservoir studied in this embodiment is as follows Figure 2 As shown in Figure 1, the three-dimensional well pattern consists of two multi-stage fractured horizontal wells (horizontal well 1 and horizontal well 2) connected to the horizontal well platform. The basic parameters mainly include reservoir pressure, reservoir thickness, porosity, permeability, saturation, fluid relative permeability, fluid viscosity and volume coefficient, artificial fracture half-length, width, number of fractures, fracture spacing, fracture area length, horizontal well length, well inclination, tubing size, etc. The detailed input parameters are shown in Table 1 and Figure 3 , Figure 4 The reservoir fluid physical property data include crude oil viscosity, volume coefficient and relative permeability data. Figure 3 The fluid viscosity and volume coefficient data in the reservoir fluid physical property data are displayed. Figure 4 The oil-water relative permeability data among the reservoir fluid physical property data are displayed.

[0086] Table 1 Basic input parameters of the model

[0087] .

[0088] Step 2, establishing a mathematical model of the flowback production process of fractured horizontal wells in shale oil reservoirs;

[0089] Since there are complex multi-scale seepage between artificial fractures, fracture networks and matrix in horizontal wells after fracturing, the material balance equations of reservoir matrix, fracture network and different regions of artificial fractures are established by considering the loss effect of fracturing fluid in the early stage of well shut-in and flowback, respectively, to provide a mathematical model for step 3. After the implementation of hydraulic fracturing production enhancement measures, the oil reservoir of shale oil reservoir generally has stages such as well shut-in, fracturing fluid flowback and post-fracturing production. In this process, there are complex multi-scale seepage between artificial fractures, fracture networks and matrix, and the flow characteristics between different media are significantly different in each stage. Therefore, considering the loss effect of fracturing fluid in the early stage of well shut-in and flowback, the material balance equations of reservoir matrix, fracture network and different regions of artificial fractures are established.

[0090] Reservoir matrix area:

[0091] (1);

[0092] (2);

[0093] In the formula, , are the cumulative oil production and water production in the matrix, m 3 ; , are respectively the oil production and water production in the matrix area, m 3 ·day-1 ; is the time step; is the imbibition rate per unit time, m 3 ·day -1 ; is the number of artificial cracks, an integer; is the distance the pressure wave propagates in the matrix region, m; is the geometric area of ​​the matrix dynamic discharge area, m 2 ; is the initial porosity in the matrix region, decimal; , are the volume coefficients of the oil phase and the water phase under the original conditions, m 3 ·m -3 ; , are the volume coefficients of the oil phase and the water phase, respectively, m 3 ·m -3 ; , are the initial oil saturation and water saturation in the matrix area, respectively, in decimals; is the porosity within the matrix region, a decimal; is the average pressure in the matrix dynamic discharge area, MPa; , are the oil and water saturations in the matrix dynamic discharge area, respectively, in decimals.

[0094] Crack Network Area:

[0095] (3);

[0096] (4);

[0097] In the formula, , are the cumulative oil production and water production in the fracture network area, m 3 ; , are respectively the oil production and water production in the fracture network area, m 3 ·day -1 ; is the crack half length, m; is the distance reached by the pressure wave in the fracture network area; is the reservoir thickness, m; is the average porosity within the dynamic drainage area of ​​the fracture network at the original formation pressure, a decimal; , are the initial oil saturation and water saturation in the fracture network area, decimal; is the average porosity within the dynamic drainage area of ​​the fracture network at the average pressure, a decimal; is the average pressure within the dynamic discharge area of ​​the fracture network, MPa; , are the oil saturation and water saturation in the dynamic discharge area of ​​the fracture network, respectively, in decimals.

[0098] Artificial crack area:

[0099] Since the pressure difference between artificial fractures and fracture networks will lead to opposite flow directions, when the pressure of artificial fractures is greater than that of fracture network areas, the flow direction is from artificial fractures to fracture networks, and when the pressure of artificial fractures is less than that of fracture network areas, the flow direction is from fracture networks to artificial fractures. In order to ensure that the production data is meaningful and does not have negative values, different pressure conditions are distinguished and material balance equations for different areas of artificial fractures are established.

[0100] Material balance equation when the artificial fracture pressure is greater than the fracture network pressure:

[0101] (5);

[0102] (6);

[0103] Material balance equation when the artificial fracture pressure is less than the fracture network pressure:

[0104] (7);

[0105] (8);

[0106] In the formula, , are the cumulative oil production and water production in the artificial fracture area, m 3 ; , are respectively the oil phase yield and the water phase yield, m 3 ·day -1 ; is the cumulative filtration loss from the artificial fracture area to the fracture network, m 3 ; is the filtration velocity, m 3 ·day -1 ; is the crack width of the artificial crack, m; is the distance reached by the pressure wave in the artificial fracture area, m; is the height of the artificial crack, m; is the initial porosity in the artificial fracture area, a decimal; , are the initial oil and water saturations in the artificial fracture area, respectively, in decimals; is the porosity at the average pressure in the artificial fracture area, a decimal; is the average pressure in the dynamic discharge area of ​​the artificial fracture, MPa; , are the oil and water saturations in the dynamic discharge area of ​​artificial fractures, respectively, in decimals.

[0107] Step 3: Solve the mathematical model of the flowback production process of fractured horizontal wells in shale oil reservoirs based on the Newton-Raphson method to obtain the crossflow rate of reservoir fluid between matrix-fracture network, fracture network-artificial fractures, and production data such as daily production and cumulative production of fractured horizontal wells;

[0108] The mathematical model of the flowback production process of fractured horizontal wells in shale oil reservoirs is solved to simulate the flowback production process of fractured horizontal wells. According to the mathematical model established in step 2, there are six unknown variables in the material balance equation, namely the pressure and water saturation of three regions: artificial fractures, fracture networks, and matrix. The Newton-Raphson method is used to implicitly process all dependent variables in the equation and transform the nonlinear problem into a fixed point approximate solution to solve the equation. This method can effectively overcome the shortcomings of instability and non-convergence in material balance calculations.

[0109] The material balance equation of oil-water phases in artificial fractures, fracture networks and matrix is ​​abstracted into the following form:

[0110] (9);

[0111] In the formula, Represents the average pressure in the dynamic discharge area of ​​artificial fractures, MPa; Represents the average water saturation in the dynamic discharge area of ​​the artificial fracture, decimal; Represents the average pressure in the dynamic discharge area of ​​the seam network, MPa; Represents the average water saturation in the dynamic discharge area of ​​the fracture network, decimal; Represents the average pressure in the dynamic discharge area of ​​the matrix, MPa; Represents the average water saturation in the dynamic discharge area of ​​the matrix, decimal; to is the equation symbol for the nonlinear equation to be solved.

[0112] The material balance equation is solved using the Newton-Raphson iteration method. The solution is shown in formula (10):

[0113] (10);

[0114] (11);

[0115] In the formula, is the number of iterations, when hour is the initial value of the calculation input; , They are sequence Pressure and saturation data after iterations; is the nonlinear material balance equations to be solved; The system of equations is The partial derivative of .

[0116] The Newton-Raphson method solves the material balance equations of the reservoir matrix, fracture network, and artificial fracture area, and obtains the pressure and saturation data of different areas at any time, and finally obtains the cross-flow rate of reservoir fluid between the matrix-fracture network, fracture network-artificial fracture, and the daily production and cumulative production of fractured horizontal wells. The specific calculation process of the material balance equation is as follows: Figure 5 As shown, the following steps are included:

[0117] Step 3.1. Input the basic physical parameters of artificial fractures, fracture networks and matrix areas, including pressure, permeability, porosity, fracture half-length, fracture width, relative permeability, and set the calculation time step. With the total number of time steps ; Initialize the simulation time step index , duration ; Duration is the total time of the simulation;

[0118] Step 3.2: Determine the size of the dynamic leakage area and the relative permeability value based on the corresponding pressure and saturation data at each time length, calculate the oil-water two-phase production and the flow between each area based on the capacity calculation formula, and calculate the pseudo-pressure, production, and material balance equation and its derivatives size;

[0119] Step 3.3: Update the pressure and saturation data for the next time step based on formula (10) , judge whether the pressure and saturation data calculated in step 3.2 (if it is the second time step, compare it with the set initial pressure and saturation data) reaches the allowable error, that is, judge whether it satisfies If the convergence condition is not met, the updated Substitute into step 3.2 and recalculate until convergence; the time step refers to the time interval between each iteration, which can be set manually, such as 1 day; the number of iterations is the number of update operations required to achieve the set convergence condition, which is calculated by the algorithm based on factors such as the complexity of the equation; 1 time step contains multiple iterations;

[0120] Step 3.4, based on the converged pressure and saturation data, calculate the oil and water production rates at the next time step, and calculate the cumulative oil and water production according to the rates, and simultaneously update the pressure and saturation formation physical property parameters;

[0121] Step 3.5: Determine whether it is the last time step, that is, determine Is it equal to ; If the last time step has not been reached, execute ,according to Update the duration, and then restart step 3.2 to simulate the reservoir production dynamics of the next time step; if the last time step has been simulated, end the simulation and output the calculation results.

[0122] In this embodiment, the calculation time step is 1 day, the flowback time is 20 days, the production time is 2980 days, and the total time is 3000 days. The calculation results of the solution data of the flowback production model of the shale oil reservoir fracturing horizontal well are as follows: Figure 6 and Figure 7 As shown, the corresponding flowback volume, cumulative oil production and cumulative water production data in the flowback production stage are obtained. Figure 6 The cumulative flowback volume data during the flowback phase is displayed. Figure 7 The daily oil and water production data during the production phase are shown. Figure 6 and Figure 7 It can be seen that the relationship between the flowback volume in the flowback stage and the daily oil and water production in the production stage changes with time.

[0123] Step 4: Analyze the dynamic nodes of the reservoir-wellbore-nozzle flow system; mainly use the dynamic node analysis method of the reservoir-wellbore-nozzle flow system, draw the inflow dynamic curve based on the calculation results of step 3, generate the outflow dynamic curve based on the pressure drop loss of the wellbore and nozzle system, calculate the coordination points, and obtain the change law of reservoir pressure, saturation and production capacity under different nozzle sizes.

[0124] The present invention adopts the dynamic node analysis method proposed in the patent "A method for optimizing the design of the working system of multi-phase production nozzles in multi-stage fracturing horizontal wells" (publication number CN116050298A) for analysis; the dynamic node analysis method takes into account the changes in reservoir production capacity and the law of multiphase flow, and is used to solve the changes in the production capacity of the nozzle-wellbore-reservoir system. The mathematical model of the flowback production process of the fracturing horizontal well in the shale oil reservoir established and solved by steps 2 and 3 is combined with the wellbore gas-liquid two-phase flow model and the nozzle critical flow model for analysis. According to the calculation results of step 3, the inflow dynamic (IPR) curve is drawn, with the horizontal axis being the oil production and the vertical axis being the bottom hole flow pressure; based on the wellbore gas-liquid two-phase flow model and the nozzle critical flow model, the pressure drop loss of the wellbore and the nozzle system is calculated to generate the outflow dynamic (OPR) curve, and the coordination point is calculated to obtain the change law of reservoir pressure, saturation and production capacity under different nozzle sizes.

[0125] The wellbore gas-liquid two-phase flow model adopts the Beggs-Brill model, which can be used for horizontal and vertical gas-liquid two-phase flow calculation methods. In the present invention, it is a horizontal well, that is, horizontal gas-liquid two-phase flow. The simplified calculation formula is shown in formula (12):

[0126] (12);

[0127] In the formula, is the average pressure in the horizontal well pipeline, MPa; is the distance of axial flow, m; is the liquid holdup, a decimal number representing the volume fraction of the liquid phase in the gas-liquid mixture during the flow process; is the liquid density, kg·m -3 ; is the gas phase density, kg·m -3 ; is the acceleration due to gravity, m·s -2 ; is the angle between the pipeline and the horizontal, degrees; is the flow resistance coefficient, dimensionless; is the mass flow rate of the mixed liquid, kg·s -1 ; is the average flow velocity of the mixture, m·s -1 ; is the inner diameter of the oil pipe, m; is the flow cross-sectional area of ​​the oil pipe, m 2 ; is the gas phase apparent (converted) velocity, m·s -1 .

[0128] Referring to Zhang Qi’s book “Principles and Design of Oil Production Engineering”, the flow of gas-liquid mixture in the nozzle is similar to the flow of single-phase gas. The nozzle critical flow model proposed by him is adopted and some empirical parameters are corrected. The calculation formula is shown in formula (13):

[0129] (13);

[0130] In the formula, is the liquid production, t·day -1 ; is the nozzle diameter, mm; is the production gas-oil ratio, m 3 ·t -1 ; is the wellhead tubing pressure, MPa; is the moisture content, a decimal.

[0131] Step 4 The specific calculation steps are as follows:

[0132] Step 4.1, based on the mathematical model of the flowback production process of fractured horizontal wells in shale oil reservoirs, simulate the production rules under different bottom hole flow pressures to obtain the multiphase velocity, average pressure and saturation in the dynamic drainage area, water content and other parameters corresponding to the production conditions;

[0133] Step 4.2, based on the calculation results of step 4.1, draw an inflow performance (IPR) curve, where the abscissa is the oil production and the ordinate is the bottom hole flowing pressure;

[0134] Step 4.3, given the initial value of water cut, calculate the pressure drop loss under different oil production rates based on the wellbore gas-liquid two-phase flow model and the nozzle critical flow model, and generate the outflow performance (OPR) curve. The intersection of the outflow curve and the inflow curve is the coordination point;

[0135] Step 4.4: Based on the converged coordination point parameters, the average pressure and average saturation in the dynamic discharge area can also be obtained, which can be used as the initial parameters for the next time step simulation;

[0136] Step 4.5: Repeat steps 4.1 to 4.4 until the last time step is simulated and all calculations are completed. Based on the calculation results of each time step, the change patterns of reservoir pressure, saturation and production capacity under different nozzle sizes are obtained.

[0137] Step 5: Optimize the nozzle working system based on genetic algorithm;

[0138] Based on genetic algorithm, combined with the mathematical model of the flowback production process of shale oil reservoir fracturing horizontal wells established in steps 1 to 4 and the results of dynamic node analysis of the reservoir-wellbore-nozzle flow system, the cumulative oil production is taken as the optimization target and the nozzle size is taken as the optimization parameter. An optimization method for the nozzle working system of the flowback production flow process of shale oil reservoir fracturing horizontal wells is established. The replacement timing and size of the nozzle can be optimized according to the reservoir physical properties, providing a reference for formulating production strategies for the development of shale oil three-dimensional fracturing well networks.

[0139] The optimization objective function equation and constraints established are as follows:

[0140] (14);

[0141] In the formula, is the cumulative oil production, m 3 ; is the nozzle size, mm; It is The nozzle size simulated in each time step; is the total duration of the simulation, day; is the time step, day; It is The nozzle size simulated in each time step; is the remainder function.

[0142] In this embodiment, the total simulation duration is 1800 days, and the time step is 30 days, that is, the nozzle size is updated every 30 days, and a total of 60 adjustments are made. The nozzle size is between 1-10 mm, which is an integer.

[0143] The process of genetic algorithm is as follows Figure 8 As shown, the basic operation process is as follows:

[0144] Step 5.1, Population initialization: First initialize the evolutionary generation , Maximum evolutionary generations , the number of individuals in each generation (In this embodiment, the number of individuals is the number of adjustments, set to 60), randomly generate individual values ​​to form the number of individuals in the initial group (In this embodiment, the nozzle size corresponding to each adjustment is a set of 60 nozzle sizes), the individual is the nozzle size; is the initial evolutionary generation;

[0145] Step 5.2: Individual fitness evaluation: Based on the optimization objective function in formula (14): Calculation Group The individual fitness (in this embodiment, the cumulative oil production) in the group is used to evaluate the quality of the current group (the higher the cumulative oil production, the better);

[0146] Step 5.3: After performing selection, crossover and mutation operations, adjust and sort the individuals in the group so that the group Generate the next generation of population, that is, generate different nozzle size sequences to obtain the optimal cumulative oil production;

[0147] Among them, selection operation: based on the individual fitness evaluation in step 5.2, the selection operator is applied to the group to select elite individuals, that is, individuals with higher fitness, and make them have a higher probability of being selected for crossover and mutation, so as to retain and pass on excellent gene combinations;

[0148] Crossover operation: Generate new individuals by replacing and reorganizing part of the structure of two parent individuals. Multi-point crossover, uniform crossover, etc. can be performed at the intersection. The crossover operation allows the new individual to retain some characteristics of the parent individuals or generate a completely new combination, thereby increasing the diversity of the group.

[0149] Mutation operation: Randomly adjust the structure of some individuals in the group. By increasing the randomness of exploring the solution space, it helps to escape from the local optimal solution and enables the algorithm to search the solution space more widely.

[0150] Step 5.4, termination condition judgment: If the convergence condition is met, that is, the evolutionary generation reaches the maximum evolutionary generation, the individual with the maximum fitness in the evolutionary process is output as the optimal result and the calculation is terminated; if the convergence condition is not met, that is, the evolutionary generation has not reached the maximum evolutionary generation, then repeat steps 5.2-5.3 until the maximum evolutionary generation is reached.

[0151] Step 6: Design a flowback production plan for a three-dimensional fracturing well pattern in a rock oil reservoir;

[0152] According to the genetic algorithm optimization result of step 5, the optimized nozzle working system sequence diagram is obtained. According to the sequence diagram, the nozzle size corresponding to the horizontal well of the three-dimensional well network at different production times can be determined, so as to design the nozzle working system sequence in the three-dimensional fracturing well network backflow production process of shale oil reservoirs, and formulate a development plan to obtain the best mining effect. The present invention provides a reference for the formulation of production strategies for the development of three-dimensional fracturing well networks for shale oil. The present invention can not only realize the flow simulation of the whole stage of backflow production of fracturing horizontal wells in shale oil reservoirs, but also provide theoretical and technical support for the formulation of production strategies for the development of three-dimensional fracturing well networks for shale oil.

[0153] like Fig. 9 and Fig.10 As shown, according to the nozzle working system sequence diagram obtained by genetic algorithm optimization, the nozzle sizes corresponding to the two horizontal wells Horizontal Well 1 and Horizontal Well 2 in the three-dimensional well pattern in this embodiment at different production times can be determined. Fig. 9From the data, we can get the sequence of nozzle operation in horizontal well 1. In the early stage, 5mm nozzle was mainly used, and 4mm and 3mm nozzles were replaced in due time. After 8-10 days, 4mm nozzle was mainly used, and 3mm nozzles were replaced in due time. Fig.10 It can be seen that the 6mm oil nozzle was used in the first 90 days, 5mm was used after 90 days, and then the diameter was adjusted to 5mm, 4mm, and 3mm.

[0154] Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by technicians in this technical field within the essential scope of the present invention should also fall within the protection scope of the present invention.

Claims

1. A method for optimizing the design of flowback production after shale oil three-dimensional fracturing well pattern, characterized in that: The steps include: Step 1: Arrange the basic parameters of shale oil reservoirs; Step 2, establishing a mathematical model of the flowback production process of fractured horizontal wells in shale oil reservoirs; In the step 2, material balance equations for different regions of the reservoir matrix, fracture network, and artificial fractures are established respectively; The material balance equation in the reservoir matrix region is: Where N s,m , W s,m are the cumulative oil production and water production in the matrix; q o,mf ,q w,mf are the oil and water production in the matrix area, respectively; Δt is the time step; q o,imb is the imbibition rate per unit time; n f is the number of artificial cracks; y inv,m is the distance the pressure wave propagates in the matrix region; A matrix,DDA is the geometric area of ​​the matrix dynamic discharge area; φ im is the initial porosity in the matrix region; B oi , B wi are the volume coefficients of the oil phase and the water phase under the original conditions; B o , B w are the volume coefficients of the oil phase and the water phase, respectively; S oi,m , S wi,m are the initial oil saturation and water saturation in the matrix area; φ m is the porosity within the matrix region; is the average pressure in the dynamic discharge area of ​​the matrix; They are the oil and water saturations in the matrix dynamic discharge area; The material balance equation in the fracture network region is: Where N s,f , W s,f are the cumulative oil production and water production in the fracture network area respectively; q o,fF ,q w,fF are the oil production and water production in the fracture network area; x f is the half length of the crack; inv is the distance reached by the pressure wave in the fracture network area; h is the reservoir thickness; is the average porosity within the dynamic drainage area of ​​the fracture network under the original formation pressure; S oi,f , S wi,f They are the initial oil saturation and water saturation in the fracture network area; is the average porosity within the dynamic drainage area of ​​the fracture network at average pressure; is the average pressure within the dynamic discharge area of ​​the fracture network; They are the oil saturation and water saturation in the dynamic drainage area of ​​the fracture network; According to the different pressure conditions in the artificial fracture area, the material balance equations in different areas of the artificial fracture are established; The material balance equation when the artificial fracture pressure is greater than the fracture network pressure is: The material balance equation when the artificial fracture pressure is less than the fracture network pressure is: Where N P , W P are the cumulative oil production and water production in the artificial fracture area respectively; q o ,q w are oil phase production and water phase production respectively; W leak,w is the cumulative filtration loss from the artificial fracture area to the fracture network; leak is the filtration velocity; w f is the crack width of the artificial crack; x inv is the distance reached by the pressure wave in the artificial fracture area; h f is the height of the artificial crack; φ iF is the initial porosity in the artificial fracture area; S oi,F , S wi,F are the initial oil and water saturations in the artificial fracture area; φ F is the porosity at the average pressure in the artificial fracture area; is the average pressure in the dynamic discharge area of ​​the artificial fracture; They are the oil and water saturations in the dynamic drainage area of ​​artificial fractures; Step 3, solving the mathematical model of the flowback production process of fractured horizontal wells in shale oil reservoirs based on the Newton-Raphson method; In step 3, the material balance equation of the oil-water two phases in the artificial fractures, fracture networks and matrix is ​​abstracted into the following form: In the formula, x1 represents the average pressure in the dynamic discharge area of ​​artificial fractures; x2 represents the average water saturation in the dynamic discharge area of ​​artificial fractures; x3 represents the average pressure in the dynamic discharge area of ​​fracture network; x4 represents the average water saturation in the dynamic discharge area of ​​fracture network; x5 represents the average pressure in the dynamic discharge area of ​​matrix; x6 represents the average water saturation in the dynamic discharge area of ​​matrix; F1 to F6 are the equation symbols of the nonlinear equations to be solved; The material balance equation is solved using the Newton-Raphson iteration method. The solution is shown in formula (10): In the formula, X (K+1) , K (K) are the pressure and saturation data after the K+1th and Kth iterations respectively; F(X (K) ) is the nonlinear material balance equations to be solved; F′(X (K) ) is the equation system for X (K) The partial derivative of Step 4: Analyze the dynamic nodes of the reservoir-wellbore-nozzle flow system; the specific process is as follows: Step 4.1, based on the mathematical model of the flowback production process of the fractured horizontal well in the shale oil reservoir, simulate the production law under different bottom hole flow pressures to obtain the multiphase velocity, average pressure and saturation in the dynamic drainage area, and water content corresponding to the production conditions; Step 4.2, according to the calculation results of step 4.1, draw the inflow dynamic curve, the horizontal axis is the oil production, and the vertical axis is the bottom hole flow pressure; Step 4.3, given the initial value of water cut, calculate the pressure drop loss under different oil production rates based on the wellbore gas-liquid two-phase flow model and the nozzle critical flow model, generate the outflow dynamic curve, and the intersection of the outflow curve and the inflow curve is the coordination point; The calculation formula of the wellbore gas-liquid two-phase flow model is: Where p is the average pressure in the horizontal well pipeline; Z is the axial flow distance; H l is the liquid holdup, which indicates the volume fraction of the liquid phase in the gas-liquid mixture during the flow process; ρ l is the liquid density; ρ g is the gas phase density; g is the gravitational acceleration; θ is the angle between the pipe and the horizontal; λ is the flow resistance coefficient; G is the mass flow rate of the mixed liquid; v is the average flow velocity of the mixture; D is the inner diameter of the oil pipe; A is the flow cross-sectional area of ​​the oil pipe; v sg is the gas phase superficial velocity; The calculation formula of the nozzle critical flow model is: In the formula, q t is the liquid production; d is the nozzle diameter; R s is the production gas-oil ratio; P t is the wellhead tubing pressure; f w is the moisture content; Step 4.4, based on the converged coordination point parameters, the average pressure and average saturation in the dynamic discharge area are obtained, and then used as the initial parameters for the next time step simulation; Step 4.5, repeat step 4.1 to step 4.4 until the last time step is simulated and all calculations are completed; based on the calculation results of each time step, the change law of reservoir pressure, saturation and production capacity under different nozzle sizes is obtained; Step 5: Optimize the nozzle working system based on genetic algorithm; Step 6: Design a flowback production plan for the three-dimensional fracturing well network in the rock oil reservoir.

2. The method for optimizing the design of flowback production after shale oil three-dimensional fracturing well pattern according to claim 1 is characterized in that: In step 1, the basic parameters include reservoir pressure, reservoir thickness, porosity, permeability, saturation, fluid relative permeability, fluid viscosity and volume coefficient, artificial fracture half-length, width, number of fractures, fracture spacing, fracture area length, horizontal well length, well inclination, and tubing size.

3. The method for optimizing the design of flowback production after shale oil three-dimensional fracturing well pattern according to claim 1 is characterized in that: In step 3, the Newton-Raphson method is used to solve the material balance equations of the reservoir matrix, fracture network, and artificial fracture area to obtain the pressure and saturation data in different areas at any time, and finally obtain the cross-flow rate of reservoir fluid between the matrix-fracture network, the fracture network-artificial fracture, and the daily production and cumulative production data of the fractured horizontal well; the solution process of the material balance equation is as follows: Step 3.1: Input the basic physical parameters of artificial fractures, fracture networks and matrix areas, including pressure, permeability, porosity, fracture half-length, fracture width, relative permeability, and set the calculation time step t step The total number of time steps N; the time step index i = 1 for the initial simulation, the duration t = t step ×i; Step 3.2: Determine the size of the dynamic drainage area and the relative permeability value according to the corresponding pressure and saturation data at each time length, calculate the oil-water two-phase production and the flow between each area based on the capacity calculation formula, and calculate F(X (K) ) and F′(X (K) ) size; Step 3.3: Update the pressure and saturation data X for the next time step based on formula (10): (K+1) , judge whether the pressure and saturation data calculated in step 3.2 reach the allowable error, that is, judge whether |X (K+1) -X (K) |≤10 -6 If the convergence condition is not met, the updated X (K+1) Substitute into step 3.2 and recalculate until convergence; Step 3.4, based on the converged pressure and saturation data, calculate the oil and water production rates at the next time step, and calculate the cumulative oil and water production according to the rates, and simultaneously update the pressure and saturation formation physical property parameters; Step 3.5, determine whether it is the last time step, that is, determine whether i is equal to N; if it is not the last time step, execute the time step index plus 1, update the duration, and then restart step 3.2 to simulate the reservoir production dynamics of the next time step; if the last time step has been simulated, end the simulation and output the calculation results.

4. The method for optimizing the design of flowback production after shale oil three-dimensional fracturing well pattern according to claim 3 is characterized in that: In step 5, based on a genetic algorithm, with cumulative oil production as the optimization target and nozzle size as the optimization parameter, a nozzle working system optimization method for the flowback production process of a horizontal well in a shale oil reservoir is established; The optimization objective function equation and constraints established are as follows: In the formula, Q N is the cumulative oil production; c is the nozzle size; c N is the nozzle size simulated at the Nth time step; T product is the total simulation time; t step is the time step; c i is the nozzle size simulated at the i-th time step; MOD(·) is the remainder function.

5. The method for optimizing the design of flowback production after shale oil three-dimensional fracturing well pattern according to claim 4 is characterized in that: The specific process of step 5 is as follows: Step 5.1, population initialization: First initialize the evolutionary generation T and the maximum evolutionary generation T max , the number of individuals in each generation group M, randomly generate individual values ​​to form the number of individuals in the initial group P (T0); T0 is the initial evolutionary generation; where the number of individuals M corresponds to the number of adjustments, the number of individuals P (T0) is the nozzle size set corresponding to each adjustment, and the individual corresponds to the nozzle size; Step 5.2, individual fitness evaluation: Based on the optimization objective function in formula (14), the individual fitness in the group P(T) is calculated to evaluate the quality of the current group; the individual fitness corresponds to the cumulative oil production, and the higher the cumulative oil production, the better; Step 5.3, perform selection, crossover and mutation operations to adjust and sort the individuals in the group, so that the group P(T) produces the next generation of groups, that is, correspondingly produces different nozzle size sequences to obtain the optimal cumulative oil production; Among them, the selection operation is based on the individual fitness evaluation in step 5.

2. The selection operator is applied to the group to select individuals with high fitness for crossover and mutation; The crossover operation generates new individuals by replacing and reorganizing part of the structure of two parent individuals, and performs multi-point crossover and uniform crossover at the crossover point; The mutation operation is to make random adjustments to the structure of some individuals in the population; Step 5.4, termination condition judgment: If the evolutionary generation reaches the maximum evolutionary generation, the individual with the maximum fitness in the evolutionary process is output as the optimal result and the calculation is terminated; otherwise, repeat steps 5.2-5.3 until the maximum evolutionary generation is reached.

6. The method for optimizing the design of flowback production after shale oil three-dimensional fracturing well pattern according to claim 5 is characterized in that: The specific process of step 6 is: according to the genetic algorithm optimization result of step 5, an optimized choke working system sequence diagram is obtained, and the choke sizes corresponding to the horizontal wells in the three-dimensional well network at different production times are determined according to the sequence diagram, so as to design the choke working system sequence in the flowback production process of the three-dimensional fracturing well network in the shale oil reservoir, and formulate a development plan to obtain the optimal mining effect.

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