Productivity simulation method and device, computing equipment, storage medium and program product

Abstract

The invention discloses a productivity simulation method and device, computing equipment, a storage medium and a program product, and the method comprises the steps: taking a geologic model as a basis, forming a geomechanical model through loading stress, adding perforation parameters, fracturing fluid, a propping agent and a pump injection program on the basis of the geomechanical model, and building a fracturing simulation model, crack forms under different construction parameters are simulated by controlling the displacement, the liquid amount, the sand ratio and other crack construction parameters, and better target crack construction parameters within a certain range are obtained; and performing unstructured processing on the fracturing simulation result to form an oil reservoir numerical model, and predicting single well productivity under different fracture construction parameters by using the oil reservoir numerical model to obtain target fracture construction parameters. The method is closely combined with geology and engineering, the algorithm is reasonably used, field use is facilitated, prediction accuracy is improved, productivity can be effectively improved, and the method is beneficial to efficient development of oil and gas fields.

Description

Technical Field

[0001] The present application relates to the field of exploration technology, and in particular to a production capacity simulation method, device, computing equipment, computer storage medium and computer program product. Background Art

[0002] In the field of oil and gas exploration and development, the current optimization design methods for fracturing production capacity are mostly inverse designs, which usually predict production capacity based on the length, height, width and conductivity of fixed fractures. Reservoir transformation often cannot obtain a single symmetrical fracture and quantifiable fracture length and height dimensions, resulting in inaccurate prediction results, and the final prediction results are far from the actual results. Summary of the invention

[0003] In view of the above problems, the present application is proposed to provide a capacity simulation method, apparatus, computing device, computer storage medium and computer program product that overcome the above problems or at least partially solve the above problems.

[0004] According to one aspect of the present application, a capacity simulation method is provided, comprising:

[0005] Apply stress to the pre-established geological model and establish a geomechanical model;

[0006] Establish a fracturing simulation model based on preset fracturing parameters and geomechanical model;

[0007] Applying multiple groups of different crack construction parameters to the fracturing simulation model to perform crack extension simulation processing, obtaining the fracturing simulation model after the crack extension simulation processing corresponding to various crack construction parameters and the crack parameters corresponding to various crack construction parameters, and selecting the target crack construction parameters according to the crack parameters;

[0008] Perform unstructured grid processing on the fracturing simulation model for each fracture extension simulation process;

[0009] A reservoir numerical simulation model is established based on the fracturing simulation models after each unstructured grid processing and the preset basic conditions, wherein the preset basic conditions include: a reference phase permeability curve;

[0010] Obtain the phase permeability curve corresponding to the well to be predicted, input the phase permeability curve and multiple sets of different fracture construction parameters into the corresponding reservoir numerical simulation model for capacity prediction, obtain the capacity prediction result, and select the target fracture construction parameters according to the capacity prediction result.

[0011] Optionally, stress loading is performed on a pre-established geological model, and establishing a geomechanical model further comprises:

[0012] Perform finite difference processing on the pre-established geological model to obtain a finite difference geological grid model;

[0013] The upper, lower and surrounding rocks are added to the finite difference geological grid model, and the added finite difference geological grid model is extruded based on the pre-defined initial conditions and boundary conditions to simulate the underground reservoir formation process and establish a geomechanical model.

[0014] Optionally, performing finite difference processing on the pre-established geological model to obtain a finite difference geological grid model includes:

[0015] Format the coordinate data of the corner grid nodes in the pre-established geological model;

[0016] The formatted node coordinate data is converted to obtain finite difference grid node coordinate data;

[0017] A finite difference geological grid model is established based on the finite difference grid node coordinate data.

[0018] Optionally, adding upper, lower and surrounding rocks to the finite difference geological grid model, extruding the added finite difference geological grid model based on predefined initial conditions and boundary conditions, simulating the underground reservoir formation process, and establishing the geomechanical model further includes:

[0019] Expanding the finite difference geological grid model by a preset multiple in the i direction and the j direction, and expanding the finite difference geological grid model by a preset number of grids above and below;

[0020] Adding elastoplastic equivalent materials to the extended finite difference geological grid model;

[0021] Stress steel plates are added around the finite difference geological grid model after equivalent material replacement to eliminate stress concentration and limit strain. The added finite difference geological grid model is extruded based on predefined initial conditions and boundary conditions to simulate the underground reservoir formation process and establish a geomechanical model.

[0022] Optionally, performing unstructured grid processing on the fracturing simulation model of each fracture extension simulation process further includes:

[0023] The fracturing simulation model for each fracture extension simulation is processed by unstructured grid according to the fracture-matrix model and the fracture-fracture model;

[0024] Among them, the crack-matrix model:

[0025]

[0026] in, is the flow rate of the fracture in the i direction and the matrix in the j direction; is the transfer coefficient between the crack in the i direction and the matrix in the j direction; is the crack pressure in the i direction; is the pressure of the matrix in the j direction; is the harmonic mean permeability of the matrix in the i direction and the fracture in the j direction; is the fluid flow capacity between the matrix in the i direction and the fracture in the j direction; is the grid intersection area of ​​the matrix in the i direction and the crack in the j direction; To characterize the average distance, m represents the matrix and f represents the fracture;

[0027] Crack-to-crack model:

[0028]

[0029] in, is the flow rate of crack a and crack b in direction i; is the transfer coefficient between the i direction of crack a and the j direction of crack b; is the pressure of the crack coded b in the j direction; is the pressure of the crack coded a in the i direction; is the fluid flow capacity between the i direction of crack a and the j direction of crack b; is the conductivity of crack a in the i direction; is the conductivity of crack b in the j direction; is the harmonic mean permeability of crack a in direction i; is the actual distance of crack a in direction i; is the average distance of crack a in the i direction; is the actual distance of crack b in the j direction; is the harmonic mean permeability of crack b in the j direction; is the average distance of crack b in the j direction; is the intersection length of the crack grid between crack a in the i direction and crack b in the j direction, where a and b refer to cracks. fi a represents the flow state of the crack coded a in the i direction, Represents the flow state of the crack coded b in the j direction.

[0030] Optionally, the preset fracturing parameters include: perforation depth, pore density, phase angle, fracturing fluid viscosity, proppant particle size and pumping program;

[0031] Crack construction parameters include: displacement, liquid volume, and sand ratio.

[0032] According to another aspect of the present application, there is provided a production capacity simulation device, comprising:

[0033] The first building module is suitable for performing stress loading on the pre-established geological model to establish a geomechanical model;

[0034] The second establishment module is adapted to establish a fracturing simulation model according to preset fracturing parameters and a geomechanical model;

[0035] The fracture extension simulation processing module is suitable for applying multiple groups of different fracture construction parameters to the fracturing simulation model to perform fracture extension simulation processing, obtaining the fracture simulation model after the fracture extension simulation processing corresponding to various fracture construction parameters and the fracture parameters corresponding to various fracture construction parameters, and selecting the target fracture construction parameters according to the fracture parameters;

[0036] An unstructured grid processing module, suitable for performing unstructured grid processing on the fracturing simulation model for each fracture extension simulation process;

[0037] The third establishment module is adapted to establish a reservoir numerical simulation model based on the fracturing simulation models processed by each unstructured grid and preset basic conditions, wherein the preset basic conditions include: a reference phase permeability curve;

[0038] The capacity simulation module is suitable for obtaining the phase permeability curve corresponding to the well to be predicted, inputting the phase permeability curve and multiple sets of different fracture construction parameters into the corresponding reservoir numerical simulation model for capacity prediction, obtaining the capacity prediction result, and selecting the target fracture construction parameters according to the capacity prediction result.

[0039] According to another aspect of the present application, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0040] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned production capacity simulation method.

[0041] According to another aspect of the present application, a computer storage medium is provided, in which at least one executable instruction is stored, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned capacity simulation method.

[0042] According to another aspect of the present application, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to perform operations corresponding to the above-mentioned capacity simulation method.

[0043] The production capacity simulation method, device, computing equipment, computer storage medium and computer program product provided in the embodiments of the present application are closely integrated with geology and engineering, and the algorithm is reasonably used to facilitate on-site use, thereby improving the prediction accuracy, thereby effectively improving the production capacity and being beneficial to the efficient development of oil and gas fields.

[0044] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0046] Figure 1 A schematic diagram of a process flow of a capacity simulation method according to an embodiment of the present application is shown;

[0047] Figure 2 is a schematic diagram of the finite difference grid;

[0048] Figure 3 Schematic diagram of the comparison between the geological model (left) and the finite difference geological grid model (right);

[0049] Figure 4 a- Figure 4 h is a schematic diagram of generating the geomechanical model;

[0050] Figure 5 It is a schematic diagram of crack expansion under different displacement (2m3 / min~8m3 / min);

[0051] Figure 6 Schematic diagram of crack parameters under different displacement rates;

[0052] Figure 7 This is a schematic diagram after unstructured grid processing;

[0053] Figure 8 It is a schematic diagram of the numerical model of the reservoir;

[0054] Fig. 9 It is a schematic diagram of the numerical model of the reservoir under different displacement rates (2m3 / min~8m3 / min);

[0055] Fig.10It is a schematic diagram of the changes in daily oil production and cumulative oil production under different displacements (2m3 / min~8m3 / min);

[0056] Fig.11 A schematic diagram of the structure of a production capacity simulation device according to an embodiment of the present application is shown;

[0057] Fig.12 A schematic diagram of the structure of a computing device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0058] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0059] Figure 1 FIG. 1 is a flow chart of a method for simulating production capacity according to an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0060] Step S101, stress loading is performed on a pre-established geological model to establish a geomechanical model.

[0061] The present application is carried out on the basis of a pre-established geological model. Specifically, stress loading is performed on the pre-established geological model, and the rock diagenesis process is simulated by loading stress, so as to obtain the current ground stress field and form a geomechanical model.

[0062] In an optional implementation, the geomechanical model can be established by the following method:

[0063] Perform finite difference processing on the pre-established geological model to obtain a finite difference geological grid model;

[0064] The upper, lower and surrounding rocks are added to the finite difference geological grid model, and the added finite difference geological grid model is extruded based on the pre-defined initial conditions and boundary conditions to simulate the underground reservoir formation process and establish a geomechanical model.

[0065] Specifically, taking the T well area as an example, the strata encountered in the T well area are, from top to bottom, the Quaternary and Tertiary of the Cenozoic; the Cretaceous and Triassic of the Mesozoic; the Permian and Carboniferous of the Upper Paleozoic and the Silurian and Ordovician of the Lower Paleozoic. The Jurassic and Devonian are missing. The main oil layer is the upper 3 sub-members of the Silurian Kepingtag Formation. From the distribution characteristics of the oil layer, the structural high part gradually thins to the low part, and the main area is generally 15-20m. The upper 3 sub-members of the Silurian Kepingtag Formation are stably developed in the whole well area and can be divided into 3 sand layer groups, 10 small layers, 29 single layers, and 1 sand layer group with developed sand bodies. Multiple sets of sand bodies are developed vertically. The main oil-bearing sand body section has a stratum thickness of 17-21m, a single sand body thickness of 3-4m, and an interlayer thickness of 3-6m. The sand body has strong vertical and horizontal heterogeneity and is mainly composed of quartz sandstone.

[0066] According to the wellbore collapse data, the maximum principal stress directions of the six wells T1, T2, T3, T5, T9, and T10 are determined to be NE67°, NE65°, NE68°, NE71°, NE65°, and NE60°, respectively. In general, the maximum horizontal principal stress direction of the current tectonic stress field in the T well area is about NE70°. According to the results of the in-situ stress interpretation, the bedrock horizontal minimum principal stress equivalent density is 1.63-1.82SG, and the fracture pressure is 1.76-2.08SG, which has high stress characteristics. The horizontal stress difference coefficient is between 0.47-0.5. According to rock mechanics experiments, the reservoir rock is soft and has the characteristics of low Young's modulus and high Poisson's ratio.

[0067] Based on the geological model of the T well area, the geological model uses a corner point grid model, which has reached the level of tens of millions of grids. In order to effectively improve the efficiency of production capacity simulation, the finite difference method can be used to process the geological model. The finite difference method approximates the motion equations, equilibrium equations and boundary conditions in the differential equations with differential forms, that is, algebraic expressions are used to represent the continuous functions f=f(x,y) (such as stress, temperature, displacement, etc.) at the spatial nodes, thereby transforming the solution of differential equations into the solution of algebraic equations. Taking the stress and strain solution problem of elastic bodies as the target body, the finite difference method uses two sets of parallel lines with equal spacing h and parallel to the coordinate axis to grid the model ( Figure 2 ).

[0068] exist Figure 2 In the figure, at nodes 1 and 3 near node 0, the x coordinates are equal to x+x0 and x-x0, respectively, that is, x-x0 is equal to -h and h, respectively. The differential formulas of the x-axis and y-axis can be obtained:

[0069]

[0070] More specifically, the operation process is to use the self-written grid conversion program to format the data and convert the data type of the corner grid node coordinate data in the pre-established geological model, so as to obtain the finite difference grid node coordinate data, and then establish the finite difference geological grid model of the complex reservoir based on the finite difference grid node coordinate data, such as Figure 3 shown.

[0071] For example, the coordinate data of the corner grid nodes in the pre-established geological model can be formatted first. For example, the grid data of the geological model needs to be exported as the (x, y, z) coordinate value format of the continuous unit, and the coordinate values ​​of the 8 nodes of a hexahedral unit need to be arranged continuously, which is to correspond to the node arrangement rule of the finite difference grid. The specific steps are as follows: ① Export the node coordinate data of the corner grid in layers, and number the files in the order of the layers; ② Define the Filecount() function - count the number of statistical data files, which is convenient for the correspondence of nodes between different layers; ③ Define the fNNArr() function - create an array matrix to store the data of each file; ④ Define the UnitArr() function - obtain the coordinate data of the 8 nodes of the hexahedral unit and convert it into three columns of xyz data; ⑤ Save the formatted data in a newly created table; Then, convert the formatted node coordinate data to obtain the finite difference grid node coordinate data, that is, after completing the formatting of the node coordinates, write a grid conversion program to convert the coordinate data according to the node arrangement method of the finite difference grid to generate the coordinate file of the finite difference grid unit. The specific implementation steps are as follows: ①Define the Fileread() function to read the formatted data file; ②Define the Rowscount() function to calculate the number of valid data rows and define the number of loops; ③Define the For() function to reorder the coordinate data according to the node arrangement order of the valid differential grid and add keywords; ④Save the converted data in a new table; Finally, establish a finite difference geological grid model based on the finite difference grid node coordinate data. At the same time, the Young's modulus, shear modulus and Poisson's ratio attribute bodies contained in the geological model are placed in the finite difference grid.

[0072] After the finite difference geological grid model is established, the real diagenetic environment of the deep reservoir can be simulated based on the finite difference geological grid model. For example, the upper, lower and surrounding rocks are added to the finite difference geological grid model, and the added finite difference geological grid model is extruded based on the pre-defined initial conditions and boundary conditions to simulate the underground reservoir formation process and establish a geomechanical model, wherein the initial conditions and boundary conditions refer to the initial formation stress state and the surrounding stress state.

[0073] Among them, simulating the real diagenetic environment of deep reservoirs is divided into the following three steps:

[0074] ① Expand the target area grid of the finite difference geological grid model. Specifically, expand the grid of the finite difference geological grid model by a preset multiple in the i and j directions, and expand the preset number of grids above and below the finite difference geological grid model. For example, expand the finite difference geological grid model outward by 5 times in i and j respectively, and expand 10 grids above and below the finite difference geological grid model to complete the expansion of the model grid.

[0075] ② Replace the finite difference geological grid model with equivalent materials. For example, add elastoplastic equivalent materials to the extended finite difference geological grid model, give the extended grid lithology, give it basic rock mechanics properties, and complete the material modeling of the entire model.

[0076] ③ The finite difference geological grid model after equivalent material replacement is constrained by stress steel plates and loaded with boundary loads. Stress steel plates are added around the finite difference geological grid model after equivalent material replacement to eliminate stress concentration and limit strain. The added finite difference geological grid model is squeezed based on pre-defined initial conditions and boundary conditions to simulate the formation process of underground reservoirs. Through multiple stress evolutions, a geomechanical model is established. The established geomechanical model is a dynamic geomechanical model.

[0077] Figure 4 a- Figure 4 h is a schematic diagram of generating a geomechanical model, which describes the process of grid expansion of the finite difference geological grid model to generate a geomechanical model:

[0078] (1) The top and bottom surfaces of the reservoir were expanded by 600 m respectively to simulate the squeezing and supporting effects of the overlying rock and underlying basement on the reservoir ( Figure 4 a~ Figure 4 c). The overlying rock layer and the lower basement are divided into five layers vertically, and the same division spacing is used as the target layer in the plane. A total of 243,040 grid cells are established ( Figure 4 d~ Figure 4 e).

[0079] (2) On the plane, surrounding rock units are created around the reservoir, expanding to 9 times the original reservoir size, thereby "wrapping" the entire reservoir and simulating the real burial environment of deep reservoirs underground. At the same time, in order to avoid the problem of boundary stress concentration and excessive model deformation when loading boundary stress conditions, high-strength rock mechanics parameters are assigned to the boundary units, that is, a layer of rigid border is created for restriction ( Figure 4 f~ Figure 4 g). A total of 2000 grid cells were established for the surrounding rock and border, and the final overall model created had a total of 488080 grid cells.

[0080] After stress loading, equilibrium is finally reached to form a geomechanical model ( Figure 4 h).

[0081] Step S102: establishing a fracturing simulation model according to preset fracturing parameters and a geomechanical model.

[0082] On the basis of the geomechanical model, the preset fracturing parameters are defined, wherein the preset fracturing parameters include: perforation depth, pore density, phase angle, fracturing fluid viscosity, proppant particle size and pumping program. Specifically, the completion section is first defined, i.e., perforation depth, pore density and phase angle, and then the fracturing fluid with the same viscosity as the field and the proppant with the same compressive strength as the field are defined, and then the actual pumping program is input to establish a fracturing simulation model. Among them, the perforation depth can be set to 3000m, the pore density to 20 holes / meter, and the phase angle to 60 degrees. Then, the fracturing fluid viscosity is defined to be 150mPa·s and the proppant particle size to be 40 / 70 mesh.

[0083] Step S103, applying multiple groups of different crack construction parameters to the fracturing simulation model to perform crack extension simulation processing, obtaining the fracturing simulation model after the crack extension simulation processing corresponding to various crack construction parameters and the crack parameters corresponding to various crack construction parameters, and selecting the target crack construction parameters according to the crack parameters.

[0084] Specifically, the fracture construction parameters include displacement, liquid volume, and sand ratio. The ranges of displacement, liquid volume, and sand ratio are changed respectively, and the fracture extension simulation processing is performed on the fracturing simulation model to simulate the fracture extension and obtain the fracture parameters under different fracture construction parameters. Each set of fracture construction parameters corresponds to a set of fracture parameters and a corresponding fracturing simulation model after the fracture extension simulation processing. Therefore, the target fracture construction parameters can be reversely selected according to the obtained fracture parameters, and the better fracture construction parameters are selected as the target fracture construction parameters.

[0085] In the pumping program, the displacement range is first changed to obtain the fracture parameters such as fracture length, fracture height, transformation volume and support area under different displacements. The displacement is used as the horizontal coordinate and the fracture parameter is used as the vertical coordinate to obtain the interval with higher transformation volume and support area within a certain displacement range. Similarly, the range of liquid volume and sand ratio is changed to obtain a better fracture parameter range. Table 1 schematically lists the simulation range of fracture construction parameters in Block T.

[0086] Table 1 Simulation range of crack construction parameters in Block T

[0087]

[0088] Taking displacement optimization as an example, the influence of displacement is simulated based on the established fracturing model, and the displacement is set to 2, 3, 4, 5, 6, 7, and 8 m 3 / min, simulate the crack morphology under different displacements, and analyze the crack length, height, width, support area and support volume.

[0089] From the displacement influencing factors simulation diagram ( Figure 5 , where the top is a 3D diagram and the bottom is a 2D diagram) It can be seen that under the existing reservoir geological and mechanical characteristics, the displacement of 2 to 4 m3 / min is difficult to cope with the effective support of the multi-fracture system. The fracture length and height are limited due to the divergence of the fracture network, which is far from the single fracture design system. After the transformation, the overall fracture size and conductivity are average, there are many invalid supports (purple part), and even local signs of sand blockage. At the same time, the plane distribution ability of the fracture network is poor, and the final fracture length is insufficient. This situation gradually improves after the displacement reaches 5 m3 / min. From the fracture parameters under different displacements ( Figure 6 ) It can also be seen that with the increase of displacement, the length, height, support area and transformation volume of the fractures increase effectively, but when the displacement reaches 6m3 / min, the increase slows down significantly. The optimal displacement parameter design is 5-6m3 / min, which needs to be further simulated and demonstrated according to the actual reservoir conditions of the wells. The fracture length, height, width, support area and support volume of different displacements are analyzed and studied. The artificial fracture network mainly considers the fracture length and support area, so it is believed that the optimal displacement is 5-6m3 / min. 3 / min.

[0090] Step S104, performing unstructured grid processing on the fracturing simulation models of each fracture extension simulation process.

[0091] In step S103, multiple groups of fracturing simulation models for fracture extension simulation processing under different fracture construction parameters will be obtained. Here, it is necessary to perform unstructured grid processing on each fracturing simulation model for fracture extension simulation processing.

[0092] At present, there are two main regional connection relationships: fracture-matrix regional connection relationship and fracture-fracture regional connection relationship. Due to the interlaced relationship between grids, a fracture grid may be embedded in multiple matrix or fracture grids, and the change of the physical quantity of the grid will inevitably propagate to the multiple grids in which it is embedded. The regional connection relationship can be used to construct the propagation relationship of the physical field between different grids in numerical solution.

[0093] Therefore, the fracturing simulation model for each fracture extension simulation can be processed by unstructured grid according to the fracture-matrix model and the fracture-fracture model;

[0094] Among them, the crack-matrix model:

[0095]

[0096] in, is the flow rate of the fracture in the i direction and the matrix in the j direction; is the transfer coefficient between the crack in the i direction and the matrix in the j direction; is the crack pressure in the i direction; is the pressure of the matrix in the j direction; is the harmonic mean permeability of the matrix in the i direction and the fracture in the j direction; is the fluid flow capacity between the matrix in the i direction and the fracture in the j direction; is the grid intersection area of ​​the matrix in the i direction and the crack in the j direction; To characterize the average distance, m represents the matrix and f represents the fracture;

[0097] Crack-to-crack model:

[0098]

[0099]

[0100] in, is the flow rate of crack a and crack b in direction i; is the transfer coefficient between the i direction of crack a and the j direction of crack b; is the pressure of the crack coded b in the j direction; is the pressure of the crack coded a in the i direction; is the fluid flow capacity between the i direction of crack a and the j direction of crack b; is the conductivity of crack a in the i direction; is the conductivity of crack b in the j direction; is the harmonic mean permeability of crack a in direction i; is the actual distance of crack a in direction i; is the average distance of crack a in the i direction; is the actual distance of crack b in the j direction; is the harmonic mean permeability of crack b in the j direction; is the average distance of crack b in the j direction; is the intersection length of the crack grid between crack a in the i direction and crack b in the j direction, where a and b refer to cracks. fi a represents the flow state of the crack coded a in the i direction, Represents the flow state of the crack coded b in the j direction.

[0101] Step S105, establishing a reservoir numerical simulation model based on the fracturing simulation models processed by the unstructured grids and preset basic conditions, wherein the preset basic conditions include: a reference phase permeability curve.

[0102] After performing unstructured grid processing according to step S104, a fracturing simulation model after unstructured grid processing can be obtained. The number of fracturing simulation models after unstructured grid processing corresponds to the number of groups of fracture construction parameters. Figure 7 The left side is a finite difference grid, and the right side is an unstructured grid). By adding preset basic conditions, such as reference phase permeability curve (as shown in Table 2, this is just an example, and the reference phase permeability curves of different wells are different), rock compression coefficient and other basic conditions, a reservoir numerical model can be established. Different rocks have different rock compression coefficients. For example, the rock compression coefficient of a certain rock is set to 0.5×10 -3 , thus completing the establishment of the reservoir numerical model (such as Figure 8 As shown in the figure, different fracture construction parameters correspond to different reservoir numerical simulation models, and the number of reservoir numerical simulation models corresponds to the number of groups of fracture construction parameters. Here, the reservoir numerical models under different fracture construction parameters are finally obtained.

[0103] Table 2 Reference phase permeability curve input table

[0104]

[0105] Taking displacement optimization as an example, according to the fracturing simulation model after each unstructured grid processing (such as Fig. 9 The output changes under different displacements are analyzed.

[0106] Combining the artificial fracture simulation diagrams with different displacements and the production capacity comparison analysis curves, it can be seen that when the displacement is increased from 2m 3 / min increased to 3~4m 3 / min, the cumulative oil production increased significantly, which showed a good positive correlation with the increase in the effective fracture network formed. This increase trend was observed when the displacement reached 5-6m 3 / min, and then it slowed down significantly. After that, the displacement continued to increase, and the cumulative oil production did not increase much. Although the corresponding fracture transformation volume was still increasing, the reason was that the increase in the connected seepage area was limited. Comparing the production predictions after fracturing at different displacements, it was found that when the displacement reached 6m 3 / min, the output will increase slightly if the displacement is further increased, so the displacement is set at 5-6m 3 / min is the best (such as Fig.10 shown).

[0107] Step S106, obtaining the phase permeability curve corresponding to the well to be predicted, inputting the phase permeability curve and multiple sets of different fracture construction parameters into the corresponding reservoir numerical simulation model to perform capacity prediction, obtaining the capacity prediction result, and selecting the target fracture construction parameters according to the capacity prediction result.

[0108] After establishing the reservoir numerical model under different fracture construction parameters, the phase permeability curve corresponding to the well to be predicted can be obtained, which mainly refers to the oil production. Each set of fracture construction parameters corresponds to a reservoir numerical simulation model. Here, the phase permeability curve and a set of fracture construction parameters are input into the corresponding reservoir numerical simulation model for capacity prediction. Different groups of fracture construction parameters will be input into the reservoir numerical simulation model corresponding to the group of fracture construction parameters for capacity prediction, thereby obtaining multiple capacity prediction results. The target fracture construction parameters are selected according to the capacity prediction results, so that construction can be carried out according to the target fracture construction parameters.

[0109] The productivity model method provided in the present application is proposed based on the idea of ​​geological engineering integration. On the basis of the geological model, the corner point grid in the geological model is converted into a finite difference grid through a self-programming language, and the Young's modulus, shear modulus and Poisson's ratio attribute bodies contained in the geological model are placed in the finite difference grid to form a finite difference geological model; based on the finite difference geological model, by adding overlying rock layers, underlying basement and surrounding rocks, and using peripheral steel plates for constraints, by loading triaxial stress, the rock diagenesis process is simulated, the current ground stress field is obtained, and a geomechanical model is formed; on the basis of the geomechanical model, perforation parameters, fracturing fluid, proppant and pumping program are added to establish a fracturing simulation model, and by controlling fracture construction parameters such as displacement, liquid volume, sand ratio, etc., the fracture morphology under different construction parameters is simulated to obtain the optimal target fracture construction parameters within a certain range; the fracturing simulation results are unstructured to form a reservoir numerical model, and the reservoir numerical model is used to predict the single well productivity under different fracture construction parameters to obtain the target fracture construction parameters. Its integrated geology-mechanics-complex artificial fractures-integrated reservoir numerical simulation model is used to predict production capacity, which is of great significance for improving the efficiency of fracturing operations, reducing development costs, and realizing the efficient development of oil and gas reservoirs.

[0110] In summary, according to the method provided in this embodiment, geology and engineering are closely combined, the algorithm is reasonably used, and it is convenient for on-site use, which improves the prediction accuracy, thereby effectively improving production capacity and being beneficial to the efficient development of oil and gas fields.

[0111] Fig.11 FIG. 1 shows a schematic diagram of the structure of a production capacity simulation device according to an embodiment of the present application. Fig.11 As shown, the device comprises:

[0112] The first building module 1101 is adapted to perform stress loading on a pre-established geological model to establish a geomechanical model;

[0113] The second establishment module 1102 is adapted to establish a fracturing simulation model according to preset fracturing parameters and a geomechanical model;

[0114] The fracture extension simulation processing module 1103 is adapted to apply multiple groups of different fracture construction parameters to the fracturing simulation model to perform fracture extension simulation processing, obtain the fracture simulation model after the fracture extension simulation processing corresponding to various fracture construction parameters and the fracture parameters corresponding to various fracture construction parameters, and select the target fracture construction parameters according to the fracture parameters;

[0115] An unstructured grid processing module 1104, adapted to perform unstructured grid processing on the fracturing simulation model of each fracture extension simulation process;

[0116] The third establishing module 1105 is adapted to establish a reservoir numerical simulation model based on the fracturing simulation models processed by each unstructured grid and preset basic conditions, wherein the preset basic conditions include: a reference phase permeability curve;

[0117] The capacity simulation module 1106 is suitable for obtaining the phase permeability curve corresponding to the well to be predicted, inputting the phase permeability curve and multiple sets of different fracture construction parameters into the corresponding reservoir numerical simulation model for capacity prediction, obtaining the capacity prediction result, and selecting the target fracture construction parameters according to the capacity prediction result.

[0118] Optionally, the first establishing module is further adapted to: perform finite difference processing on the pre-established geological model to obtain a finite difference geological grid model;

[0119] The upper, lower and surrounding rocks are added to the finite difference geological grid model, and the added finite difference geological grid model is extruded based on the pre-defined initial conditions and boundary conditions to simulate the underground reservoir formation process and establish a geomechanical model.

[0120] Optionally, the first establishing module is further adapted to: format the coordinate data of the corner grid nodes in the pre-established geological model;

[0121] The formatted node coordinate data is converted to obtain finite difference grid node coordinate data;

[0122] A finite difference geological grid model is established based on the finite difference grid node coordinate data.

[0123] Optionally, the first establishing module is further adapted to: expand the finite difference geological grid model by a preset multiple in the i direction and the j direction, and expand the finite difference geological grid model by a preset number of grids above and below;

[0124] Adding elastoplastic equivalent materials to the extended finite difference geological grid model;

[0125] Stress steel plates are added around the finite difference geological grid model after equivalent material replacement to eliminate stress concentration and limit strain. The added finite difference geological grid model is extruded based on predefined initial conditions and boundary conditions to simulate the underground reservoir formation process and establish a geomechanical model.

[0126] Optionally, the unstructured grid processing module is further adapted to: perform unstructured grid processing on the fracturing simulation model of each fracture extension simulation process according to the fracture-matrix model and the fracture-fracture model;

[0127] Among them, the crack-matrix model:

[0128]

[0129] in, is the flow rate of the fracture in the i direction and the matrix in the j direction; is the transfer coefficient between the crack in the i direction and the matrix in the j direction; is the crack pressure in the i direction; is the pressure of the matrix in the j direction; is the harmonic mean permeability of the matrix in the i direction and the fracture in the j direction; is the fluid flow capacity between the matrix in the i direction and the fracture in the j direction; is the grid intersection area of ​​the matrix in the i direction and the crack in the j direction; To characterize the average distance, m represents the matrix and f represents the fracture;

[0130] Crack-to-crack model:

[0131]

[0132] in, is the flow rate of crack a and crack b in direction i; is the transfer coefficient between the i direction of crack a and the j direction of crack b; is the pressure of the crack coded b in the j direction; is the pressure of the crack coded a in the i direction; is the fluid flow capacity between the i direction of crack a and the j direction of crack b; is the conductivity of crack a in the i direction; is the conductivity of crack b in the j direction; is the harmonic mean permeability of crack a in direction i; is the actual distance of crack a in direction i; is the average distance of crack a in the i direction; is the actual distance of crack b in the j direction; is the harmonic mean permeability of crack b in the j direction; is the average distance of crack b in the j direction; is the intersection length of the crack grid between crack a in the i direction and crack b in the j direction, where a and b refer to cracks. fi a represents the flow state of the crack coded a in the i direction, Represents the flow state of the crack coded b in the j direction.

[0133] Optionally, the preset fracturing parameters include: perforation depth, pore density, phase angle, fracturing fluid viscosity, proppant particle size and pumping program;

[0134] Crack construction parameters include: displacement, liquid volume, and sand ratio.

[0135] The description of each module above refers to the corresponding description in the method embodiment and will not be repeated here.

[0136] In summary, the device provided in this embodiment closely combines geology and engineering, rationally applies algorithms, facilitates on-site use, improves prediction accuracy, and can effectively increase production capacity, which is beneficial to the efficient development of oil and gas fields.

[0137] An embodiment of the present application provides a non-volatile computer storage medium, which stores at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to perform operations corresponding to the production capacity simulation method in any of the above method embodiments.

[0138] An embodiment of the present application provides a computer program product, which includes at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to perform operations corresponding to the production capacity simulation method in any of the above method embodiments.

[0139] Fig.12 A schematic diagram of the structure of an embodiment of a computing device of the present application is shown. The specific embodiment of the present application does not limit the specific implementation of the computing device.

[0140] like Fig.12 As shown, the computing device may include: a processor (processor) 1202 , a communication interface (Communications Interface) 1204 , a memory (memory) 1206 , and a communication bus 1208 .

[0141] The processor 1202, the communication interface 1204, and the memory 1206 communicate with each other via a communication bus 1208. The communication interface 1204 is used to communicate with other devices such as a client or other server network elements. The processor 1202 is used to execute a program 1210, which can specifically execute the relevant steps in the above-mentioned embodiment of the capacity simulation method for computing devices.

[0142] Specifically, the program 1210 may include program codes, which include computer operation instructions.

[0143] The processor 1202 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0144] The memory 1206 is used to store the program 1210. The memory 1206 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0145] The program 1210 can be specifically used to enable the processor 1202 to execute the capacity simulation method in any of the above-mentioned method embodiments. The specific implementation of each step in the program 1210 can refer to the corresponding descriptions in the corresponding steps and units in the above-mentioned capacity simulation embodiments, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the above-mentioned method embodiments, which will not be repeated here.

[0146] The algorithm or display provided here are not inherently related to any specific computer, virtual system or other equipment. Various general systems can also be used together with the teaching based on this. According to the above description, it is obvious to construct the structure required for this type of system. In addition, the present application embodiment is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present application described here, and the above description of specific languages ​​is to disclose the best mode of implementation of the present application.

[0147] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0148] Similarly, it should be understood that in order to streamline the present application and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: the claimed application requires more features than the features clearly stated in each claim. More specifically, as reflected in the claims below, the inventive aspects are less than all the features of the single embodiment disclosed above. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present application.

[0149] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0150] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0151] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components according to the embodiments of the present application. The application can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing a part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0152] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be constructed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A capacity simulation method, comprising: Apply stress to the pre-established geological model and establish a geomechanical model; Establishing a fracturing simulation model according to preset fracturing parameters and the geomechanical model; Applying multiple groups of different crack construction parameters to the fracturing simulation model to perform crack extension simulation processing, obtaining the fracturing simulation model after the crack extension simulation processing corresponding to various crack construction parameters and the crack parameters corresponding to various crack construction parameters, and selecting the target crack construction parameters according to the crack parameters; Perform unstructured grid processing on the fracturing simulation model for each fracture extension simulation process; A reservoir numerical simulation model is established based on the fracturing simulation models processed by each unstructured grid and preset basic conditions, wherein the preset basic conditions include: a reference phase permeability curve; Obtain the phase permeability curve corresponding to the well to be predicted, input the phase permeability curve and multiple groups of different fracture construction parameters into the corresponding reservoir numerical simulation model to perform capacity prediction, obtain the capacity prediction result, and select the target fracture construction parameters according to the capacity prediction result.

2. The method according to claim 1, wherein: The step of performing stress loading on the pre-established geological model to establish a geomechanical model further comprises: Perform finite difference processing on the pre-established geological model to obtain a finite difference geological grid model; The upper, lower and surrounding rocks are added to the finite difference geological grid model, and the added finite difference geological grid model is extruded based on predefined initial conditions and boundary conditions to simulate the underground reservoir formation process and establish a geomechanical model.

3. The method according to claim 2, wherein: The step of performing finite difference processing on the pre-established geological model to obtain a finite difference geological grid model comprises: Format the coordinate data of the corner grid nodes in the pre-established geological model; The formatted node coordinate data is converted to obtain finite difference grid node coordinate data; A finite difference geological grid model is established according to the finite difference grid node coordinate data.

4. The method according to claim 2 or 3, wherein: The step of adding upper, lower and surrounding rocks to the finite difference geological grid model, extruding the added finite difference geological grid model based on predefined initial conditions and boundary conditions, simulating the underground reservoir formation process, and establishing the geomechanical model further includes: Expanding the finite difference geological grid model by a preset multiple in the i direction and the j direction, and expanding the upper and lower parts of the finite difference geological grid model by a preset number of grids; Adding elastoplastic equivalent materials to the extended finite difference geological grid model; Stress steel plates are added around the finite difference geological grid model after equivalent material replacement to eliminate stress concentration and limit strain. The added finite difference geological grid model is extruded based on predefined initial conditions and boundary conditions to simulate the underground reservoir formation process and establish a geomechanical model.

5. The method according to any one of claims 1 to 3, wherein: The unstructured grid processing of the fracturing simulation model for each fracture extension simulation process further includes: The fracturing simulation model for each fracture extension simulation is processed by unstructured grid according to the fracture-matrix model and the fracture-fracture model; Among them, the crack-matrix model: in, is the flow rate of the fracture in the i direction and the matrix in the j direction; is the transfer coefficient between the crack in the i direction and the matrix in the j direction; is the crack pressure in the i direction; is the pressure of the matrix in the j direction; is the harmonic mean permeability of the matrix in the i direction and the fracture in the j direction; is the fluid flow capacity between the matrix in the i direction and the fracture in the j direction; is the grid intersection area of ​​the matrix in the i direction and the crack in the j direction; To characterize the average distance, m represents the matrix and f represents the fracture; Crack-to-crack model: in, is the flow rate of crack a and crack b in direction i; is the transfer coefficient between the i direction of crack a and the j direction of crack b; is the pressure of the crack coded b in the j direction; is the pressure of the crack coded a in the i direction; is the fluid flow capacity between the i direction of crack a and the j direction of crack b; is the conductivity of crack a in the i direction; is the conductivity of crack b in the j direction; is the harmonic mean permeability of crack a in direction i; is the actual distance of crack a in direction i; is the average distance of crack a in the i direction; is the actual distance of crack b in the j direction; is the harmonic mean permeability of crack b in the j direction; is the average distance of crack b in the j direction; is the intersection length of the crack grid between crack a in the i direction and crack b in the j direction, where a and b refer to cracks. fi a represents the flow state of the crack coded a in the i direction, Represents the flow state of the crack coded b in the j direction.

6. The method according to any one of claims 1 to 3, wherein: The preset fracturing parameters include: perforation depth, pore density, phase angle, fracturing fluid viscosity, proppant particle size and pumping program; The crack construction parameters include: displacement, liquid volume, and sand ratio.

7. A production capacity simulation device, comprising: The first building module is suitable for performing stress loading on the pre-established geological model to establish a geomechanical model; A second establishment module is adapted to establish a fracturing simulation model according to preset fracturing parameters and the geomechanical model; A crack extension simulation processing module is adapted to apply multiple groups of different crack construction parameters to the fracturing simulation model to perform crack extension simulation processing, obtain the fracturing simulation model after the crack extension simulation processing corresponding to various crack construction parameters and the crack parameters corresponding to various crack construction parameters, and select the target crack construction parameters according to the crack parameters; An unstructured grid processing module, suitable for performing unstructured grid processing on the fracturing simulation model for each fracture extension simulation process; The third establishment module is adapted to establish a reservoir numerical simulation model based on the fracturing simulation models processed by each unstructured grid and preset basic conditions, wherein the preset basic conditions include: a reference phase permeability curve; The capacity simulation module is suitable for obtaining the phase permeability curve corresponding to the well to be predicted, inputting the phase permeability curve and multiple sets of different fracture construction parameters into the corresponding reservoir numerical simulation model for capacity prediction, obtaining the capacity prediction result, and selecting the target fracture construction parameters according to the capacity prediction result.

8. A computing device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the production capacity simulation method according to any one of claims 1-6.

9. A computer storage medium, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to perform operations corresponding to the capacity simulation method according to any one of claims 1 to 6.

10. A computer program product, comprising at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the capacity simulation method according to any one of claims 1 to 6.

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