Oil reservoir production optimization method considering complex fracture network

Abstract

The invention provides an oil reservoir production optimization method considering a complex fracture network. The oil reservoir production optimization method considering the complex fracture network comprises the steps that 1, an oil reservoir is divided into two areas according to the development scale of fractures in the oil reservoir; step 2, respectively establishing a single pipeline model and a dual-medium model for the divided areas, and solving the models by dividing grids on the pipeline; 3, performing historical fitting on the observation value and the simulation value by means of a set multi-smooth data assimilation algorithm to obtain an optimized model; and step 4, optimizing the injection-production quantity of each well by using the optimized model and taking the economic net present value of the oil reservoir as a target function. The oil reservoir production optimization method considering the complex fracture network has excellent performance in the aspect of improving the crude oil recovery efficiency, and a remarkable optimization effect is achieved. The method can effectively deal with the complex fracture network existing in the oil reservoir, improve the production efficiency and improve the economic benefit of the oil field to the maximum extent.

Description

Technical Field

[0001] The invention relates to the technical field of numerical simulation of fractured oil reservoirs, and in particular to an oil reservoir production optimization method taking complex fracture networks into consideration. Background Art

[0002] Most oil reservoirs have fractures, such as tight limestone and mudstone. In the process of reservoir development, in order to provide more flow channels, hydraulic fracturing is sometimes used to artificially create fractures in shale reservoirs. The existence of fractures will change the flow characteristics of the fluid. Therefore, when describing the flow of fluids in the reservoir, we need to fully consider the influence of fractures. And fractures exist in various forms, including large-scale fractures spanning hundreds or thousands of meters, and small-scale fractures with short extension lengths but wide distribution. How to effectively characterize the fracture system is the primary difficulty in numerical simulation of fractured reservoirs.

[0003] In the Chinese patent application with application number: CN201310138269.1, a method for numerical simulation of large-scale fractured reservoirs is involved, which belongs to the field of oil and gas field development. The method uses large-scale fractures in the reservoir as the inner boundary of the reservoir for dimensionality reduction processing, establishes a reservoir geometric model, and then uses triangular grids to geometrically divide the reservoir geometric model to form discrete units, and at the same time forms a reservoir geometric information description file, a geometric discrete information file and a well information description file, constructs a numerical calculation unit based on the discrete unit, establishes a mathematical model to characterize the flow of fluids in such reservoirs, uses an integral method to establish its numerical calculation format on each numerical calculation unit, forms a large linear algebraic equation group, and solves it, thereby realizing numerical simulation of large-scale fractured reservoirs. The invention can accurately characterize large-scale fractured reservoirs, has a clear physical meaning, and has good adaptability to numerical simulation of large-scale fractured reservoirs.

[0004] In the Chinese patent application with application number: CN202010005164.9, a method and system for numerical simulation of tight oil reservoirs based on matrix-fracture non-steady-state crossflow is involved. The hydraulic fractures in the reservoir have the largest conductivity, followed by the natural fractures, and the matrix has the lowest conductivity. The model uses discrete fractures to treat hydraulic fractures, and dual media to treat matrix and natural fractures. In the process of meshing, denser grids are divided at the hydraulic fractures, that is, hydraulic fractures are explicitly treated, while natural fractures and matrix share a set of grids. After obtaining the discrete equations, a closed system of equations can be obtained by adding well constraints, auxiliary equations and boundary conditions, and the pressure and saturation of each time step are obtained by the Newton iteration method. This invention improves the efficiency of reservoir simulation with large-scale fractures; the flow exchange between the matrix and natural fractures is processed by the continuous medium assumption, and the simulation program can accurately characterize the non-steady-state crossflow problem between the matrix and the fractures.

[0005] In the Chinese patent application with application number: CN202210642759.4, a reservoir optimization method based on an approximate gradient algorithm and an embedded discrete fracture model is involved, including the following steps: S1, based on the reservoir numerical simulation software, a numerical simulation model of a fractured reservoir is established according to the embedded discrete fracture model; S2, economic parameters and algorithm parameters are set, and the well control vector is initialized; S3, a perturbation set of the well control vector is obtained, and the reservoir numerical simulation software is run to obtain the net present value corresponding to each perturbation vector; S4, the approximate gradient is calculated, and the well control vector is updated; S5, the optimal well control vector is found by the binary search method, and steps S3, S4, and S5 are repeated until the iteration termination condition is met. The invention uses an embedded discrete fracture modeling method to establish a numerical simulation model of a fractured reservoir that reasonably characterizes the seepage characteristics of fractures in the reservoir, and conducts research on the optimization of reservoir injection and production parameters of the conceptual model of one injection and one production and the oilfield mine scale model of multiple injections and multiple productions.

[0006] The above methods generally have the problems of large computational complexity and low optimization efficiency, and fail to solve the technical problems we want to solve. For this reason, we have invented a new reservoir production optimization method that takes into account complex fracture networks. Summary of the invention

[0007] The purpose of the present invention is to provide a method for optimizing oil reservoir production by taking into account complex fracture networks, which can effectively deal with complex fracture networks existing in oil reservoirs, improve production efficiency, and maximize the economic benefits of oil fields.

[0008] The object of the present invention can be achieved by the following technical measures: a method for optimizing oil reservoir production by considering a complex fracture network, the method comprising:

[0009] Step 1, the reservoir is divided into two areas according to the development scale of fractures in the reservoir;

[0010] Step 2, establishing a single pipeline model and a dual medium model for the divided areas, and solving the model by dividing the grid on the pipeline;

[0011] Step 3, using the ensemble multi-smoothing data assimilation algorithm to perform history fitting on the observed values ​​and simulated values ​​to obtain the optimized model;

[0012] Step 4: Utilize the optimized model and take the reservoir economic net present value as the objective function to optimize the injection and production volume of each well.

[0013] The purpose of the present invention can also be achieved by the following technical measures:

[0014] In step 1, the reservoir is discretized into a series of interconnected fracture nodes according to the original large-scale fractures or fractures with high conductivity generated by hydraulic fracturing in the reservoir, and the reservoir is divided into two areas according to the development scale of the fractures in the reservoir.

[0015] In step 1, the reservoir is discretized into a series of interconnected fracture nodes according to the original large-scale fractures in the reservoir or the fractures with high conductivity generated by hydraulic fracturing. The fracture description method is to arrange fracture nodes at their end points and each inflection point respectively, in order to distinguish the fracture nodes from ordinary well points, connect the fracture nodes according to the trend of the fractures, and then connect the fracture nodes with the well points.

[0016] In step 1, the reservoir is divided into two areas according to the development scale of fractures in the reservoir, areas with large-scale fracture distribution and areas with small-scale fracture distribution.

[0017] In step 1, large-scale fractures refer to fractures with a length greater than one-third of the average well spacing of the reservoir, and small-scale fractures refer to fractures with a length less than one-third of the average well spacing of the reservoir. The two areas may overlap.

[0018] In step 2, a single-pipe model and a dual-medium model are constructed for the divided area, and a grid is divided on the pipeline. According to the physical parameters of the nodes, the parameters on the grid are fully implicitly solved.

[0019] In step 2, a single pipeline model is formed, that is, the well points and fracture nodes discretized in step 1 are connected into a connected network consisting of connected pipelines, wherein the pipelines between wells and between wells and fracture nodes adopt the flow equation of the matrix system, and the pipelines between fractures adopt the flow equation of large fractures, and a grid is divided on each pipeline to form a single pipeline network model.

[0020] In step 2, the dual medium model is used, that is, each connected pipeline is regarded as two flow systems: the bedrock system and the fracture system, where the bedrock system mainly functions as storage and the fracture system mainly functions as seepage, and there is fluid exchange between the two. Therefore, in the small-scale fracture area of ​​the dual medium model, all pipelines are two-layer grids, and each grid is characterized by its own parameters.

[0021] In step 2, a grid is divided on the pipeline. The grids on the same connection unit have equal seepage cross-sectional area and permeability. When the connection unit is a connection between well points, the permeability is the average value of the two well points. When the connection unit is a connection between a well point and a fracture node, the permeability is the permeability within the well point control volume.

[0022] In step 2, the physical property parameters are the permeability, porosity, thickness, and mesh area of ​​the grid, and the grid parameters that need to be solved are physical quantities such as pressure and saturation.

[0023] In step 3, in order to reduce the difference between the constructed model and the reservoir, the observed values ​​and the simulated values ​​are historically fitted with the help of the ensemble multi-smoothing data assimilation algorithm to obtain the optimized model.

[0024] In step 3, the parameters to be fitted include the well-well connection volume V in the single pipeline model. ij,M and the volume V between the cracks ij,F and the connected volume V of the matrix and fracture in the dual medium model ij,m 、V ij,f .

[0025] In step 4, the reservoir economic net present value is used as the performance indicator to establish the reservoir injection and production optimization objective function, and the reservoir production optimization problem is transformed into a mathematical control model solution problem in combination with relevant constraints, so as to optimize the injection and production volume of each well.

[0026] In step 4, after the model is historically matched and simulated for a long enough time, the production optimization is performed using the model updated by historical matching as the forward model, and the production optimization time is divided into Nc continuous time intervals of equal length, each of which is called a control step.

[0027] In step 4, the control step, the operating conditions of each well need to be specified. In a specific control step, each well is operated at a constant operating condition.

[0028] In step 4, the operating condition, i.e., the control variable, is the total rate or phase rate of each well. Here, the total rate is used for production optimization.

[0029] The method for optimizing oil reservoir production considering complex fracture networks in the present invention has a moderate amount of calculation and high accuracy. By discretizing the wells in the oil reservoir system into one-dimensional grids and constructing a one-dimensional dual medium model, numerical simulation and optimization of fractured oil reservoirs are achieved. The method for optimizing oil reservoir production considering complex fracture networks proposed in the present invention performs well in improving crude oil recovery and has achieved significant optimization effects. The method can effectively deal with the complex fracture networks existing in the oil reservoir, improve production efficiency, and maximize the economic benefits of the oil field. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flowchart of a specific embodiment of the method for optimizing oil reservoir production considering complex fracture networks of the present invention;

[0031] Figure 2 A crack distribution diagram in a specific embodiment of the present invention;

[0032] Figure 3A single pipeline connection diagram of a single pipeline model in a specific embodiment of the present invention;

[0033] Figure 4 A single pipeline connection diagram of a dual medium model in a specific embodiment of the present invention;

[0034] Figure 5 A distribution diagram of oil reservoir well points and fractures in a specific embodiment of the present invention;

[0035] Figure 6 A pipeline connection diagram of two models in a specific embodiment of the present invention;

[0036] Figure 7 A comparison diagram of historical matching results in a specific embodiment of the present invention;

[0037] Figure 8 This is a production optimization result diagram with an optimization time step of 180 days in a specific embodiment of the present invention;

[0038] Fig. 9 This is a production optimization result diagram with an optimization time step of 90 days in a specific embodiment of the present invention;

[0039] Fig.10 This is a production optimization result diagram with an optimization time step of 60 days in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0040] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0041] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations and / or combinations thereof.

[0042] like Figure 1 As shown, Figure 1The flowchart of the method for optimizing oil reservoir production considering complex fracture networks of the present invention. The method for optimizing oil reservoir production considering complex fracture networks of the present invention comprises the following steps: the oil reservoir is discretized into a series of interconnected fracture nodes according to the original large-scale fractures in the oil reservoir or the fractures with high conductivity generated by hydraulic fracturing, and the fluid flow between wells is divided into two categories: simple flow in the area without fracture development and complex flow in the area with original fracture development, and a single pipeline model and a dual medium model are established respectively, and the model is solved by dividing the grid on the pipeline. Then, historical fitting is performed with the help of the ensemble multi-smoothing data assimilation algorithm, and the fitted model is used to establish production optimization with the target of the economic net present value of the oil reservoir.

[0043] The method for optimizing oil reservoir production considering complex fracture networks of the present invention comprises the following steps:

[0044] Step 1: The reservoir is discretized into a series of interconnected fracture nodes based on the original large-scale fractures or fractures with high conductivity generated by hydraulic fracturing, and the reservoir is divided into two areas according to the development scale of the fractures in the reservoir.

[0045] Step 2: Construct a single-pipe model and a dual-medium model for the divided area, divide the grid on the pipeline, and perform a fully implicit solution for the parameters on the grid based on the physical properties of the nodes.

[0046] Step 3: In order to reduce the difference between the constructed model and the reservoir, the observed values ​​and the simulated values ​​are historically fitted with the help of the ensemble multi-smoothing data assimilation algorithm to obtain the optimized model.

[0047] Step 4: Establish the reservoir injection and production optimization objective function with the reservoir economic net present value as the performance indicator, and transform the reservoir production optimization problem into the problem of solving the mathematical control model in combination with relevant constraints, so as to optimize the injection and production volume of each well.

[0048] In the step 1, the reservoir is discretized into a series of interconnected fracture nodes according to the original large-scale fractures in the reservoir or the fractures with high conductivity generated by hydraulic fracturing. The fracture description method is to arrange fracture nodes at their end points and each inflection point respectively, in order to distinguish the fracture nodes from ordinary well points, connect the fracture nodes according to the trend of the fractures, and then connect the fracture nodes with the well points.

[0049] In step 1, the reservoir is divided into two regions according to the development scale of the fractures in the reservoir, namely, the region with large-scale fracture distribution and the region with small-scale fracture distribution, such as Figure 2 shown.

[0050] The large-scale fractures refer to fractures whose length is greater than one third of the average well spacing in the reservoir.

[0051] The small-scale fractures refer to fractures whose length is less than one-third of the average well spacing in the reservoir.

[0052] The two areas may overlap.

[0053] The single pipeline model in step 2 is to connect the well points and fracture nodes discretized in step 1 into a connected network composed of connected pipelines, wherein the pipelines between wells and between wells and fracture nodes adopt the flow equation of the matrix system, and the pipelines between fractures adopt the flow equation of large fractures, and a grid is divided on each pipeline, such as Figure 3 As shown, a single pipeline network model is formed.

[0054] In the dual medium model in step 2, each connected pipeline is regarded as two flow systems: bedrock system and fracture system, where the bedrock system is mainly used for storage and the fracture system is mainly used for seepage, and there is fluid exchange between the two. Therefore, in the small-scale fracture area of ​​the dual medium model, all pipelines are two-layer grids, and each grid is characterized by its own parameters, such as Figure 4 shown.

[0055] In step 2, a grid is divided on the pipeline, and the grids on the same connection unit have equal seepage cross-sectional area and permeability. When the connection unit is a connection between well points, the permeability is the average value of the two well points. When the connection unit is a connection between a well point and a fracture node, the permeability is the permeability within the well point control volume.

[0056] The physical property parameters in step 2 are the permeability, porosity, thickness, mesh area, etc. of the mesh.

[0057] The grid parameters that need to be solved are physical quantities such as pressure and saturation.

[0058] The parameters that need to be fitted in step 3 include the interconnected volume V between wells in the single pipeline model. ij,M and the volume V between the cracks ij,F and the connected volume V of the matrix and fracture in the dual medium model ij,m 、V ij,f .

[0059] In step 4, production optimization should be carried out after the historical matching and simulation of the model we built for a long enough time. The model updated by historical matching is used as the forward model, and the production optimization time is divided into Nc continuous time intervals of equal length. Each time interval is called a control step.

[0060] The control step requires specifying the operating conditions of each well. In a specific control step, each well is operated under constant operating conditions.

[0061] The operating conditions (controlled variables) are the total rate or phase rate of each well. Here, we use the total rate for production optimization.

[0062] The present invention aims to effectively characterize large-scale fractures and small-scale fractures in oil reservoirs. Conventional grid-based reservoir numerical simulation methods usually require the division of a large number of grids to finely characterize and describe the fractures, which often takes a considerable amount of time. The present invention only considers the flow between wells, wells and fractures, and fractures and fractures, simplifies the oil reservoir, and greatly improves the calculation efficiency while retaining a certain calculation accuracy.

[0063] In a specific embodiment 1 of the present invention, the reservoir production optimization method considering complex fracture networks includes the following steps:

[0064] Firstly, the reservoir is divided into areas with large-scale fracture distribution and areas with small-scale fracture distribution according to the development scale of fractures in the reservoir. Then, a single pipeline network model is established in the area with large-scale fracture distribution, and a double pipeline network model is established in the area with small-scale fracture distribution. Then, in the area with large-scale fracture distribution, the reservoir is discretized into a series of interconnected fracture nodes according to the structural characteristics of the fractures. The fracture description method is to arrange fracture nodes at their end points and each inflection point, connect the fracture nodes according to the trend of the fracture, and then connect the fracture nodes with the well points to form a connection network.

[0065] The steps for constructing a single pipeline network model in a large-scale fracture distribution area are as follows:

[0066] The well nodes are connected to form a connected network. The connected volume of each connected pipe, taking the i and j nodes as an example, is calculated as follows:

[0067]

[0068]

[0069] Among them, i, j are nodes; V ij,M and V ij,F Respectively represent the connected volumes between wells or between fractures, m 3 , S ij is the subdivision area between two points, m 2 ; is the average thickness between two points, m; and is the average porosity between wells or between fractures, 1;

[0070] The formula for calculating the seepage cross-sectional area is as follows:

[0071]

[0072] The material balance equations between well points or between well points and fracture nodes in the single pipeline network model, as well as the material balance equations between fracture nodes are as follows:

[0073]

[0074]

[0075] Where β = o, w, o, w represent the oil phase and water phase respectively; M represents bedrock, F represents fractures, and in areas other than those with small-scale fractures, only bedrock flow is considered between wells; ρ M,β and ρ F,β are the densities of a phase fluid in bedrock and in fractures, kg / m 3 ; φ M and φ F are the porosity of bedrock and fractures, 1; S M,β and S F,β are the saturation of phase fluid in bedrock and fracture, 1; t represents time, s; v M,β and v F,β Respectively represent the seepage velocity of a phase fluid in bedrock and fracture, m 3 / s;q M,β is the source-sink term (q>0 is the source term, i.e. injection is positive), which indicates the amount of a certain phase fluid added per unit volume per unit time, m 3 / s.

[0076] The equation of motion is expressed as follows:

[0077]

[0078] Where β = o, w, o, w represent the oil phase and water phase respectively; v is the seepage velocity, m 3 / s; K is permeability, mD; μ is fluid viscosity, mPa·s; is the flow pressure difference, MPa.

[0079] The steps to construct a dual medium model in the area of ​​small-scale fracture distribution are as follows:

[0080] The calculation formula of the connected volume of the connected unit is as follows:

[0081]

[0082]

[0083] Among them, i, j are well nodes; V ij,m and V ij,f represents the connected volume of matrix and fracture between node i and node j, respectively, m3 , S ij is the area of ​​the mesh between nodes i and j, m 2 ; is the average thickness between nodes i and j, m; and is the average porosity of matrix and fractures between nodes i and j, 1;

[0084] The crossflow between bedrock and fractures mainly depends on the viscosity of the fluid, the pressure difference between bedrock and fractures, and certain characteristic quantities of rock (length, area, volume, etc.). The crossflow between fractures and bedrock can be expressed as:

[0085]

[0086] Where Q is the crossflow between the crack and the bedrock, m 3 / s; α is the shape factor, 1 / m 2 ρ m,β is the density of a phase fluid in bedrock, kg / m 3 ; K m is the permeability of a phase fluid in the bedrock, mD; μ β is the viscosity of a phase fluid, mPa·s; P m and P f are the matrix and fracture pressures, MPa, respectively.

[0087] The material balance equation between matrix and crack is as follows:

[0088]

[0089]

[0090] Among them, ρ m,β and ρ f,β are the densities of a phase fluid in bedrock and in fractures, kg / m 3 ; φ m and φ f are the porosity of bedrock and fractures, 1; S m,β and S f,β are the saturation of phase fluid in bedrock and fracture, 1; t represents time, s; v m,β and v f,β represents the seepage velocity of a phase fluid in the bedrock and fracture respectively. 3 / s;q m and q f are the source and sink terms of the matrix and fracture respectively (q>0 is the source term, that is, the injection is positive, indicating the amount of material added per unit volume per unit time), m 3 / s.

[0091] Then, the well-to-well communication volume V in the single pipeline model is ij,M and the volume V between the cracks ij,F and the connected volume V of the matrix and fracture in the dual medium model ij,m 、V ij,f Perform the fitting.

[0092] The historical fitting objective function established to reduce the error between the constructed model and the actual model is as follows:

[0093]

[0094] Where m is the characteristic parameter of each grid that needs to be historically matched, O(m) is the established minimization objective function, g(m) is the production dynamic index obtained by simulating the established pipeline model, and d obs is the observed production dynamic index, C D is the principal diagonal matrix of the covariance of the observation error.

[0095] The ensemble multi-smoothing data assimilation algorithm updates the reservoir model parameters using the update equations after each data assimilation step, but the update equations do not ensure that each parameter satisfies the boundary constraints or linear equality constraints, so we add constraints to enforce the parameters.

[0096] The constraints are as follows:

[0097]

[0098] Among them, V tot,m and V tot,f denote the total volume of matrix and fracture respectively, N w is the number of wells, V ij,M 、V ij,F and V ij,m 、V ij,f are the volumes of matrix and fractures in the connected unit between nodes i and j in the single pipeline network model and dual medium model, respectively. The purpose of the above constraints is to make the sum of the volume of the matrix in the reservoir equal to the total volume of the matrix, and the sum of the volume of the fractures equal to the total volume of the fractures.

[0099] Finally, the reservoir injection and production optimization objective function established with the economic net present value (NPV) as the indicator is as follows:

[0100]

[0101] Among them, r o is the unit price of oil, CNY / m 3 ; r w The unit price of produced water treatment, CNY / m 3 ; r wi is the unit price of injected water, CNY / m 3; b is the annual discount rate; N t is the total number of simulation time steps; t n is the nth time step; Δt n is the time step length of the nth time step; P and I correspond to the number of production wells and injection wells, respectively; and is the oil and water production of the jth production well at the nth time step, m 3 / day; is the amount of liquid injected into the j-th injection well at the n-th time step, m 3 / day;

[0102] The control variables are expressed as follows:

[0103]

[0104] Where N w represents the number of wells, u i Represents the phase rate of the i-th well, and each time step will have different control variables, so the dimension of the total control variables is: N u =N c ·N w , where N u is the total control variable dimension, N c is the number of time steps.

[0105] According to the actual situation of the oil field, the control variables are constrained as follows:

[0106]

[0107] in, and They represent the lower and upper boundary conditions of the well-to-well control condition at the j-th time step.

[0108] Concept example 1:

[0109] The reservoir model constructed is as follows Figure 5 As shown, the reservoir is divided into two areas, with two large-scale fractures in the left area and irregular small fractures in the right area. The grid size of the reservoir model is 42*42*1; grid size is 10m*10m*5m; there are fourteen wells in the reservoir, including four water injection wells and ten production wells. ECLIPSE is used to simulate the production of the reservoir block for 4920 days, and the data of the previous 3120 days are used as real data for historical fitting of the constructed model.

[0110] First, complete the connection diagram of the single-pipe model and the dual-medium model, such as Figure 6As shown, the circles represent well points and fracture nodes, the black pipes represent large-scale fractures, the white pipes represent fracture systems in the dual-medium model, the gray pipes represent matrix systems, the left area is the single-channel network model area, and the right area is the dual-medium model area. The forward simulation using the method described in this paper takes 15.2 seconds, which is significantly reduced compared to the conventional reservoir numerical simulator, which takes 53.4 seconds. Figure 7 This is the result of the block cumulative oil production fitting after historical fitting of the single well oil production rate in the first 3120 days. The black asterisk is the historical data, and the black solid line is the simulation result of the cumulative oil production of the present invention. It can be seen that the fitting effect is good. This embodiment uses 180 days as the time step, optimizes each well for 10 time steps, and optimizes production for a total of five years. The cumulative oil production obtained by continuing production with the original production system has been greatly improved. The comparison results of the cumulative oil production before and after optimization are shown in the figure. Figure 8 As shown in the figure, the black solid line is the cumulative oil production after optimization, the black dotted line is the cumulative oil production of the system before optimization, and the vertical dotted line represents the dividing line between history fitting and production optimization.

[0111] Conceptual example 2:

[0112] This embodiment uses the same reservoir parameters and injection-production system as the conceptual example 1, and uses the same history matching result for production optimization. The difference from the conceptual example 1 is that this embodiment uses 90 days as a single optimization step, optimizes each well for 20 time steps, and the total optimization time is also five years. The comparison results of the cumulative oil production before and after the optimization are as follows: Fig. 9 As shown in the figure, the black solid line is the cumulative oil production after optimization, the black dotted line is the cumulative oil production of the system before optimization, and the vertical dotted line represents the dividing line between history fitting and production optimization.

[0113] Concept example 3:

[0114] This embodiment uses the same reservoir parameters and injection-production system as the conceptual example 1, and uses the same history matching result for production optimization. The difference from the conceptual example 1 is that this embodiment uses 60 days as a single optimization step, optimizes each well for 30 time steps, and the total optimization time is also five years. The comparison results of the cumulative oil production before and after the optimization are as follows: Fig.10 As shown in the figure, the black solid line is the cumulative oil production after optimization, the black dotted line is the cumulative oil production of the system before optimization, and the vertical dotted line represents the dividing line between history fitting and production optimization.

[0115] The above three conceptual examples are used to test the impact of different optimization strategies on the optimization results. The results show that under the same optimization cycle, an optimization system with fewer optimization steps and more optimization frequencies will produce better optimization results.

[0116] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions recorded in the aforementioned embodiments or replace some of the technical features therein with equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0117] Except for the technical features described in the specification, all other technical features are known technologies to those skilled in the art.

Claims

1. A method for optimizing reservoir production considering complex fracture networks, characterized in that: The reservoir production optimization method considering complex fracture networks includes: Step 1, the reservoir is divided into two areas according to the development scale of fractures in the reservoir; Step 2, establishing a single pipeline model and a dual medium model for the divided areas, and solving the model by dividing the grid on the pipeline; Step 3, using the ensemble multi-smoothing data assimilation algorithm to perform history fitting on the observed values ​​and simulated values ​​to obtain the optimized model; Step 4: Utilize the optimized model and take the reservoir economic net present value as the objective function to optimize the injection and production volume of each well.

2. The method for optimizing oil reservoir production considering complex fracture networks according to claim 1, characterized in that: In step 1, the reservoir is discretized into a series of interconnected fracture nodes according to the original large-scale fractures or fractures with high conductivity generated by hydraulic fracturing in the reservoir, and the reservoir is divided into two areas according to the development scale of the fractures in the reservoir.

3. The method for optimizing oil reservoir production considering complex fracture networks according to claim 2, characterized in that: In step 1, the reservoir is discretized into a series of interconnected fracture nodes according to the original large-scale fractures in the reservoir or the fractures with high conductivity generated by hydraulic fracturing. The fracture description method is to arrange fracture nodes at their end points and each inflection point respectively, in order to distinguish the fracture nodes from ordinary well points, connect the fracture nodes according to the trend of the fractures, and then connect the fracture nodes with the well points.

4. The method for optimizing oil reservoir production considering complex fracture networks according to claim 2, characterized in that: In step 1, the reservoir is divided into two areas according to the development scale of fractures in the reservoir, areas with large-scale fracture distribution and areas with small-scale fracture distribution.

5. The method for optimizing oil reservoir production considering complex fracture networks according to claim 4, characterized in that: In step 1, large-scale fractures refer to fractures with a length greater than one-third of the average well spacing of the reservoir, and small-scale fractures refer to fractures with a length less than one-third of the average well spacing of the reservoir. The two areas may overlap.

6. The method for optimizing oil reservoir production considering complex fracture networks according to claim 1, characterized in that: In step 2, a single-pipe model and a dual-medium model are constructed for the divided area, and a grid is divided on the pipeline. According to the physical parameters of the nodes, the parameters on the grid are fully implicitly solved.

7. The method for optimizing oil reservoir production considering complex fracture networks according to claim 6, characterized in that: In step 2, a single pipeline model is formed, that is, the well points and fracture nodes discretized in step 1 are connected into a connected network consisting of connected pipelines, wherein the pipelines between wells and between wells and fracture nodes adopt the flow equation of the matrix system, and the pipelines between fractures adopt the flow equation of large fractures, and a grid is divided on each pipeline to form a single pipeline network model.

8. The method for optimizing oil reservoir production considering complex fracture networks according to claim 6, characterized in that: In step 2, the dual medium model is used, that is, each connected pipeline is regarded as two flow systems: the bedrock system and the fracture system, where the bedrock system mainly functions as storage and the fracture system mainly functions as seepage, and there is fluid exchange between the two. Therefore, in the small-scale fracture area of ​​the dual medium model, all pipelines are two-layer grids, and each grid is characterized by its own parameters.

9. The method for optimizing oil reservoir production considering complex fracture networks according to claim 6, characterized in that: In step 2, a grid is divided on the pipeline. The grids on the same connection unit have equal seepage cross-sectional area and permeability. When the connection unit is a connection between well points, the permeability is the average value of the two well points. When the connection unit is a connection between a well point and a fracture node, the permeability is the permeability within the well point control volume.

10. The method for optimizing oil reservoir production considering complex fracture networks according to claim 6, characterized in that: In step 2, the physical property parameters are the permeability, porosity, thickness, and mesh area of ​​the grid, and the grid parameters that need to be solved are physical quantities such as pressure and saturation.

11. The method for optimizing oil reservoir production considering complex fracture networks according to claim 1, characterized in that: In step 3, in order to reduce the difference between the constructed model and the reservoir, the observed values ​​and the simulated values ​​are historically fitted with the help of the ensemble multi-smoothing data assimilation algorithm to obtain the optimized model.

12. The method for optimizing oil reservoir production considering complex fracture networks according to claim 11, characterized in that: In step 3, the parameters that need to be fitted include the well-well communication volume V in the single pipeline model. ij,M and the volume V between the cracks ij,F and the connected volume V of the matrix and fracture in the dual medium model ij,m 、V ij,f .

13. The method for optimizing oil reservoir production considering complex fracture networks according to claim 1, characterized in that: In step 4, the reservoir economic net present value is used as the performance indicator to establish the reservoir injection and production optimization objective function, and the reservoir production optimization problem is transformed into a mathematical control model solution problem in combination with relevant constraints, so as to optimize the injection and production volume of each well.

14. The method for optimizing oil reservoir production considering complex fracture networks according to claim 13, characterized in that: In step 4, after the model is historically matched and simulated for a long enough time, the production optimization is performed using the model updated by historical matching as the forward model, and the production optimization time is divided into Nc continuous time intervals of equal length, each of which is called a control step.

15. The method for optimizing oil reservoir production considering complex fracture networks according to claim 14, characterized in that: In step 4, the control step, the operating conditions of each well need to be specified. In a specific control step, each well is operated at a constant operating condition.

16. The method for optimizing oil reservoir production considering complex fracture networks according to claim 15, characterized in that: In step 4, the operating condition, i.e., the control variable, is the total rate or phase rate of each well. Here, the total rate is used for production optimization.

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