Numerical Well Testing Method and Device for Discrete Fractured Low-Porosity Sandstone Reservoirs

Abstract

The present application provides a numerical well test method and device for discrete fractured low-porosity sandstone reservoirs. The method includes: obtaining the double logarithmic curve of the measured data based on the existing production data of the reservoir, establishing a numerical model of the reservoir, and determining the double logarithmic curve of the template data corresponding to the numerical model; obtaining the data difference between the double logarithmic curves of the measured data and the template data in the same coordinate system; for the curve segments in the double logarithmic curve of the template data where the difference is greater than a preset threshold, adjusting the first model parameter of the numerical model; for the curve segments in the double logarithmic curve of the template data where the difference is less than the preset threshold, adjusting the second model parameter of the numerical model; and interpreting reservoir parameters according to the numerically modeled after two fittings. The method of the present application improves the accuracy of interpreting reservoir parameters by the numerical well test method.

Description

Technical Field

[0001] The present application relates to the technical field of gas reservoir development, and particularly to a numerical well testing method and device for discrete fractured low-porosity sandstone reservoirs. Background Art

[0002] Well testing is a key technology in the process of oil and gas reservoir development. Through well testing, the production capacity of a well can be determined, formation parameters and reservoir dynamics can be studied, and predictions can be made.

[0003] The core of modern well testing analysis methods is the chart matching interpretation method. For different oil and gas reservoir models and the corresponding actual physical models of internal and external boundary conditions, corresponding mathematical models are established, and several relationship curves are solved to obtain the interpretation charts. Then, the double logarithmic curve of the measured data is fitted with the interpretation charts. When the measured data conforms to a certain chart, the physical model corresponding to the theoretical chart is the actual physical model of the oil and gas reservoir, and thus the interpretation results of reservoir parameters are obtained.

[0004] However, in actual well testing analysis, it is often difficult to fit the interpretation charts with the measured data of the reservoir, resulting in a large difference between the interpretation model and the actual situation, low accuracy of the interpretation results, and inability to well reflect the production status of the well. Summary of the Invention

[0005] The present application provides a numerical well testing method and device for discrete fractured low-porosity sandstone reservoirs to solve the problem of a large difference between the well testing interpretation model and the actual situation.

[0006] In a first aspect, the present application provides a numerical well testing method for discrete fractured low-porosity sandstone reservoirs, including:

[0007] Obtain the existing production data of the reservoir, obtain the double logarithmic curve of the measured data according to the existing production data, establish a numerical model of the reservoir, and determine the double logarithmic curve of the template data corresponding to the numerical model, where the measured data and the template data are both pressure-related data;

[0008] Obtain the difference in pressure-related data between the double logarithmic curve of the measured data and the double logarithmic curve of the template data in the same coordinate system;

[0009] For the curve segments in the double logarithmic curve of the template data where the difference is greater than a preset threshold, adjust the first model parameter of the numerical model to fit the adjusted double logarithmic curve with the double logarithmic curve of the measured data;

[0010] For the curve segments in the double logarithmic curve of the template data where the difference is less than a preset threshold, adjust the second model parameter of the numerical model to fit the adjusted double logarithmic curve with the double logarithmic curve of the measured data;

[0011] According to the numerical model after two fittings, reservoir parameter interpretation is carried out.

[0012] In a possible design, a numerical model of the reservoir is established, including:

[0013] Establish a physical model of the reservoir, establish a corresponding mathematical model based on the physical model of the reservoir and solve it to determine the double logarithmic curve of the template data corresponding to the mathematical model;

[0014] Fit the double logarithmic curve of the template data corresponding to the mathematical model with the double logarithmic curve of the measured data to determine the basic parameters of the numerical model of the reservoir, so as to establish the numerical model of the reservoir.

[0015] In a possible design, establishing a physical model of the reservoir, establishing a corresponding mathematical model based on the physical model of the reservoir and solving it includes:

[0016] According to the heterogeneity of the reservoir, the reservoir is divided into an inner reservoir area and an outer reservoir area by using a radial composite model, and a physical model of the inner reservoir area and a physical model of the outer reservoir area are established;

[0017] According to the physical model of the inner reservoir area and the physical model of the outer reservoir area, establish a corresponding mathematical model and solve it. The mathematical model at least includes the mathematical model of the inner reservoir area, the mathematical model of the outer reservoir area, and the boundary conditions between the inner reservoir area and the outer reservoir area.

[0018] In a possible design, the physical model corresponding to the inner reservoir area is different from the physical model corresponding to the outer reservoir area.

[0019] Exemplarily, the physical model corresponding to the inner reservoir area is a discrete fracture network model; the physical model corresponding to the outer reservoir area is a dual-porosity medium model;

[0020] Correspondingly, for the curve segment with a difference greater than the preset threshold in the double logarithmic curve of the template data, the first model parameter of the numerical model is adjusted. Specifically:

[0021] Judge whether the curve segment with a difference greater than the preset threshold is in the inner reservoir area;

[0022] If so, adjust the fracture parameter of the numerical model;

[0023] If not, adjust the pore parameter of the numerical model;

[0024] For the curve segment with a difference less than the preset threshold in the double logarithmic curve of the template data, the second model parameter of the numerical model is adjusted. Specifically:

[0025] Judge whether the curve segment with a difference less than the preset threshold is in the inner reservoir area;

[0026] If so, adjust the fracture parameters of the numerical model;

[0027] If not, adjust the pore parameters of the numerical model.

[0028] Or,

[0029] The physical model corresponding to the inner area of the reservoir is a dual-porosity medium model; the physical model corresponding to the outer area of the reservoir is a discrete fracture network model;

[0030] Correspondingly, for the curve segment in the double-logarithmic curve of the template data with a difference greater than the preset threshold, adjust the first model parameter of the numerical model, specifically:

[0031] Judge whether the curve segment with a difference greater than the preset threshold is in the inner area of the reservoir;

[0032] If so, adjust the pore parameters of the numerical model;

[0033] If not, adjust the fracture parameters of the numerical model;

[0034] For the curve segment in the double-logarithmic curve of the template data with a difference less than the preset threshold, adjust the second model parameter of the numerical model, specifically:

[0035] Judge whether the curve segment with a difference less than the preset threshold is in the inner area of the reservoir;

[0036] If so, adjust the pore parameters of the numerical model;

[0037] If not, adjust the fracture parameters of the numerical model.

[0038] In a possible design, the physical model corresponding to the inner area of the reservoir is the same as the physical model corresponding to the outer area of the reservoir.

[0039] Exemplarily, the physical models corresponding to both the inner area and the outer area of the reservoir are discrete fracture network models;

[0040] Correspondingly, for the curve segment in the double-logarithmic curve of the template data with a difference greater than the preset threshold, adjust the first model parameter of the numerical model, specifically: adjust the fracture parameters of the numerical model;

[0041] For the curve segment in the double-logarithmic curve of the template data with a difference less than the preset threshold, adjust the second model parameter of the numerical model, specifically: adjust the fracture parameters of the numerical model;

[0042] The fracture size characterized by the first model parameter is larger than the fracture size characterized by the second model parameter.

[0043] Or,

[0044] The physical models corresponding to the inner reservoir area and the outer reservoir area are both dual-porosity medium models;

[0045] Correspondingly, for the curve segments in the double-logarithmic curve of the template data where the difference is greater than the preset threshold, the first model parameter of the numerical model is adjusted. Specifically, the pore parameter of the numerical model is adjusted;

[0046] For the curve segments in the double-logarithmic curve of the template data where the difference is less than the preset threshold, the second model parameter of the numerical model is adjusted. Specifically, the pore parameter of the numerical model is adjusted; the pore size characterized by the first model parameter is larger than the pore size characterized by the second model parameter.

[0047] In a second aspect, the present application provides a numerical well testing device for a discrete fractured low-porosity sandstone reservoir, including:

[0048] A first acquisition module, configured to acquire the existing production data of the reservoir, obtain the double-logarithmic curve of the measured data according to the existing production data, establish the numerical model of the reservoir, and determine the double-logarithmic curve of the template data corresponding to the numerical model, where both the measured data and the template data are pressure-related data;

[0049] A second acquisition module, configured to acquire the difference in pressure-related data between the double-logarithmic curve of the measured data and the double-logarithmic curve of the template data in the same coordinate system;

[0050] A first fitting module, configured to adjust the first model parameter of the numerical model for the curve segments in the double-logarithmic curve of the template data where the difference is greater than the preset threshold, so as to fit the adjusted double-logarithmic curve with the double-logarithmic curve of the measured data;

[0051] A second fitting module, configured to adjust the second model parameter of the numerical model for the curve segments in the double-logarithmic curve of the template data where the difference is less than the preset threshold, so as to fit the adjusted double-logarithmic curve with the double-logarithmic curve of the measured data;

[0052] An interpretation module, configured to interpret the reservoir parameters according to the numerical model after two fittings.

[0053] In a third aspect, the present application provides a numerical well testing device for a discrete fractured low-porosity sandstone reservoir, including: a memory, a processor;

[0054] The memory is used to store computer programs / instructions; the processor is configured to execute the numerical well testing method for a discrete fractured low-porosity sandstone reservoir in the first aspect and any possible design of the first aspect according to the computer programs / instructions stored in the memory.

[0055] Fourthly, the present application provides a computer-readable storage medium, in which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, the numerical well testing method for discrete fractured low-porosity sandstone reservoirs in the first aspect and any possible design of the first aspect is realized.

[0056] The numerical well testing method and device for discrete fractured low-porosity sandstone reservoirs provided by the present application establish a numerical reservoir model according to the actual production data of the reservoir, and perform piecewise fitting on the double logarithmic curve of the reservoir numerical model according to the difference between the template data corresponding to the numerical model and the actual data, so that the numerical model is more consistent with the actual situation, can more accurately interpret the reservoir parameters, and reflect the production status of the reservoir. Description of the Drawings

[0057] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to these drawings.

[0058] Figure 1 It is a flowchart of a numerical well testing method for discrete fractured low-porosity sandstone reservoirs provided by an embodiment of the present application;

[0059] Figure 2 It is a well testing data graph provided by an embodiment of the present application;

[0060] Figure 3 It is a double logarithmic curve graph of measured data provided by an embodiment of the present application;

[0061] Figure 4 It is a double logarithmic curve fitting graph of template data and measured data provided by an embodiment of the present application;

[0062] Figure 5a It is a reservoir numerical model provided by an embodiment of the present application;

[0063] Figure 5b It is Figure 5a An enlarged central view of the reservoir numerical model;

[0064] Figure 6 It is another double logarithmic curve fitting graph of template data and measured data provided by an embodiment of the present application;

[0065] Figure 7a It is another reservoir numerical model provided by an embodiment of the present application;

[0066] Figure 7b It is Figure 7aCentral enlarged view of the reservoir numerical model;

[0067] Figure 8 Another double logarithmic curve fitting diagram of template data and measured data provided by an embodiment of the present application;

[0068] Figure 9 Structural schematic diagram of a numerical well testing device for a discrete fractured low-porosity sandstone reservoir provided by an embodiment of the present application. Detailed implementation manners

[0069] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be clearly and completely described below with reference to the accompanying drawings in the present application. Apparently, the described embodiments are some but not all of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0070] The terms "first", "second", "third", "fourth", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and do not have to be used to describe a specific order or sequence. It should be understood that such used data may be interchanged under appropriate circumstances. For example, without departing from the scope of this article, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information.

[0071] Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0072] Furthermore, as used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms unless the context clearly dictates otherwise.

[0073] It should be further understood that the terms "comprising", "including" indicate the presence of features, steps, operations, elements, components, items, types, and / or groups, but do not exclude the presence, appearance or addition of one or more other features, steps, operations, elements, components, items, types, and / or groups.

[0074] The terms "or" and "and / or" used herein are interpreted as inclusive, or meaning any one or any combination. Thus, "A, B or C" or "A, B and / or C" means "any one of the following: A; B; C; A and B; A and C; B and C; A, B and C". An exception to this definition only occurs when the combination of elements, functions, steps or operations are mutually exclusive in some manner.

[0075] The numerical well test analysis method applies the static geological data of the reservoir in the oil and gas reservoir and the production data of reservoir development to establish a numerical model, numerically describe the reservoir, and thus obtain a pressure curve that more conforms to the actual situation of the reservoir.

[0076] In the prior art, when establishing a numerical model by the numerical well test analysis method, the curve of the template data corresponding to the numerical model is fitted with the curve of the measured data. After the fitting is completed, the numerical model corresponding to the template data curve can be used to interpret the reservoir parameters or predict the reservoir production status.

[0077] However, in actual well test analysis, the fitting result of the curve of the template data and the curve of the measured data is poor, and the reservoir parameters and production status obtained by numerical model interpretation are quite different from the actual situation of the reservoir, and the accuracy of the interpretation result is low.

[0078] In view of the above problems, the present application proposes a numerical well test method and device for a discrete fractured low-porosity sandstone reservoir, which is applied to the technical field of gas reservoir development. According to the data difference between the double logarithmic curve of the template data and the double logarithmic curve of the measured data, the parameters of the numerical model are adjusted in curve segments to fit the double logarithmic curve of the template data and the double logarithmic curve of the measured data. Compared with the prior art, the numerical model corresponding to the template data after fitting in the present application is more in line with the actual situation of the reservoir, can better interpret the reservoir parameters, and predict the reservoir production status.

[0079] The technical solution of the present application will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments.

[0080] In the present application, taking the well test equipment as the execution subject, the numerical well test method for the discrete fractured low-porosity sandstone reservoir of the following embodiments is executed. Specifically, the execution subject can be the hardware device of the well test equipment, or the software application in the well test equipment that implements the following embodiments, or the computer-readable storage medium installed with the software application that implements the following embodiments, or the code of the software application that implements the following embodiments.

[0081] Figure 1 The flowchart of a numerical well test method for a discrete fractured low-porosity sandstone reservoir provided by an embodiment of the present application is shown. As Figure 1 shown, the method of this embodiment may include the following steps:

[0082] S101: Obtain the existing production data of the reservoir, obtain the double logarithmic curve of the measured data according to the existing production data, establish a numerical model of the reservoir, and determine the double logarithmic curve of the template data corresponding to the numerical model, where the measured data and the template data are both pressure-related data.

[0083] Among them, the reservoir production data includes the production dynamic data of the reservoir, such as production time and the corresponding reservoir pressure, cumulative reservoir production, etc., which can be obtained from the reservoir historical production database.

[0084] Optionally, the production dynamic data of the reservoir and the reservoir geological parameters are imported into the well test software. After analysis by the well test software, the double logarithm curve of the measured data is obtained. Among them, the reservoir geological parameters include wellbore parameters, gas reservoir top depth, effective reservoir thickness, porosity, and gas parameters, etc. The well test software analyzes the imported data and establishes a numerical model of the reservoir, so as to determine the double logarithm curve of the template data corresponding to the numerical model.

[0085] S102: Obtain the difference in pressure-related data between the double logarithm curve of the measured data and the double logarithm curve of the template data in the same coordinate system.

[0086] The two double logarithm curves are plotted in the same coordinate system to facilitate comparing the data difference for subsequent data analysis.

[0087] S103: For the curve segment of the double logarithm curve of the template data where the difference is greater than the preset threshold, adjust the first model parameter of the numerical model to fit the double logarithm curve obtained after the adjustment with the double logarithm curve of the measured data.

[0088] S104: For the curve segment of the double logarithm curve of the template data where the difference is less than the preset threshold, adjust the second model parameter of the numerical model to fit the double logarithm curve obtained after the adjustment with the double logarithm curve of the measured data.

[0089] In steps S103 and S104, different parameters are adjusted for different curve segments of the double logarithm curve of the template data to better fit the double logarithm curve of the adjusted template pressure data with the double logarithm curve of the measured pressure data.

[0090] S105: Interpret the reservoir parameters according to the numerical model after two fittings.

[0091] The parameter adjustment is carried out twice for the curve segments to fit the double-logarithmic curve of the template pressure data with the double-logarithmic curve of the measured pressure data. The corresponding numerical model can more accurately interpret the reservoir parameters and reflect the actual production status of the reservoir. The reservoir parameters include reservoir skin factor, reservoir permeability, formation flow coefficient, formation pressure, etc. The numerical well test method for discrete fractured low-porosity sandstone reservoir provided in this embodiment draws the double-logarithmic curve according to the existing production data of the reservoir, establishes the reservoir numerical model, draws the double-logarithmic curve of the template data corresponding to the numerical model, and adjusts the parameters of the numerical model in curve segments according to the data difference between the double-logarithmic curve of the template data and the double-logarithmic curve of the measured data in the same coordinate system, so as to fit the double-logarithmic curve of the template data with the double-logarithmic curve of the measured data. By using the numerical well test analysis method of this application, the double-logarithmic curves of the template data and the measured data can be better fitted, so that the reservoir parameters can be better interpreted and the reservoir production status can be predicted.

[0092] In one example, in step S101, establishing the numerical model of the reservoir includes:

[0093] Step 1: Establish the physical model of the reservoir, establish the corresponding mathematical model according to the physical model of the reservoir and solve it to determine the double-logarithmic curve of the template data corresponding to the mathematical model.

[0094] The mathematical model refers to the seepage mathematical model, including the establishment of the comprehensive seepage differential equation and the proposal of boundary conditions and initial conditions, which will be introduced in detail in subsequent examples.

[0095] Step 2: Fit the double-logarithmic curve of the template data corresponding to the mathematical model with the double-logarithmic curve of the measured data to determine the basic parameters of the numerical model of the reservoir, so as to establish the numerical model of the reservoir.

[0096] By fitting the double-logarithmic curve of the template data corresponding to the mathematical model with the double-logarithmic curve of the measured data, the basic parameters of the reservoir numerical model are inversely obtained, and the reservoir numerical model can be initially established.

[0097] The numerical model of the reservoir established in this example can be used to objectively describe the heterogeneous reservoir, reduce the difference between the well test analysis results and the actual reservoir, and make it more reliable.

[0098] In one example, in the above step 1, establishing the physical model of the reservoir, establishing the corresponding mathematical model according to the physical model of the reservoir and solving it includes:

[0099] Step 11: According to the heterogeneity of the reservoir, the reservoir is divided into an inner reservoir area and an outer reservoir area by using a radial composite model, and the physical model of the inner reservoir area and the physical model of the outer reservoir area are established.

[0100] Step 12: Establish and solve corresponding mathematical models based on the physical models of the inner reservoir area and the outer reservoir area. The mathematical models shall at least include the mathematical model of the inner reservoir area, the mathematical model of the outer reservoir area, and the boundary conditions between the inner reservoir area and the outer reservoir area.

[0101] The mathematical models corresponding to the physical models of the inner reservoir area and the outer reservoir area include the seepage mathematical models, boundary conditions, and initial conditions of the inner reservoir area and the outer reservoir area.

[0102] Solving means numerically solving the reservoir seepage mathematical model, from which the mass conservation expression at any point in the reservoir can be obtained, thereby determining the double logarithm curve of the template data corresponding to the mathematical model.

[0103] Among them, the physical model corresponding to the inner reservoir area and the physical model corresponding to the outer reservoir area can be different.

[0104] For example, the physical model corresponding to the inner reservoir area is a discrete fracture network model; the physical model corresponding to the outer reservoir area is a dual-porosity medium model.

[0105] Correspondingly, for the curve segments in the double logarithm curve of the template data with differences greater than the preset threshold, the first model parameter of the numerical model is adjusted. Specifically:

[0106] Judge whether the curve segment with a difference greater than the preset threshold is in the inner reservoir area;

[0107] If so, adjust the fracture parameter of the numerical model;

[0108] If not, adjust the pore parameter of the numerical model.

[0109] For the curve segments in the double logarithm curve of the template data with differences less than the preset threshold, the second model parameter of the numerical model is adjusted. Specifically:

[0110] Judge whether the curve segment with a difference less than the preset threshold is in the inner reservoir area;

[0111] If so, adjust the fracture parameter of the numerical model;

[0112] If not, adjust the pore parameter of the numerical model.

[0113] Taking a vertical well in a fractured gas reservoir as an example, the solution of this example is introduced in detail.

[0114] (1) Obtain reservoir geological parameter data and production data. The detailed data are shown in Parameter 1 in Table 1 and Table 2 respectively. The geological parameter data include gas reservoir, gas, and wellbore parameters, well test data, such as Figure 2 shown, and the production data include production dynamic data.

[0115] Table 1 Reservoir Geological Parameters

[0116]

[0117]

[0118] Table 2 Reservoir Production Data

[0119] Production time (h) Pressure (MPa) <![CDATA[Cumulative gas production (10 4 m 3 )]]> Deviation factor (dimensionless) 0 87.0 0.00 1.5315 24.21 84.6 32.14 1.5314 48.01 84.6 64.22 1.5343 51.79 84.7 69.29 1.5413 71.80 86.3 69.29 1.5317 95.82 86.4 69.29 1.5320 119.85 86.4 69.29 1.5308 143.81 86.5 69.29 1.5316 177.82 86.5 69.30 1.5321

[0120] (2) Import the reservoir geological parameters and production data into the well test software, and the double logarithm curve of the measured data is as Figure 3 shown.

[0121] (3) The well test software analyzes the imported data, conducts unstable pressure analysis under the dual constraints of production data and geological data, uses the method of analytical well testing to invert reservoir parameters, and according to the shape of the pressure test curve, there is a horizontal section, that is, the radial flow stage, and the curve rises in the later stage, indicating that the physical properties of the outer reservoir become worse, as Figure 3 shown by the curve in the second half.

[0122] Therefore, the radial composite model is used to divide the reservoir into the inner reservoir area and the outer reservoir area, establish a physical model of the reservoir, the discrete fracture network model is used for the inner reservoir area, and the dual porosity medium model is used for the outer reservoir area. The corresponding seepage mathematical model is established as follows:

[0123] Discrete fracture seepage mathematical model for the inner reservoir area:

[0124]

[0125] Dual porosity medium seepage mathematical model for the outer reservoir area:

[0126]

[0127] Among them, r is the radius of the inner area, m; p1 is the pressure in the inner area, MPa; φ1 is the porosity of the inner area, dimensionless; μ is the fluid viscosity, mPa·s; C t is the comprehensive compressibility, MPa -1 ; k1 is the permeability of the inner area, D; p 2f is the fracture pressure in the outer area, MPa; p 2m is the matrix pressure in the outer area, MPa; λ2 is the inter-zone flow coefficient in the outer area, dimensionless; ω2 is the storage ratio in the outer area, dimensionless.

[0128] Artificial fracture control equation:

[0129]

[0130] Among them, k Fis the permeability of the artificial fracture, D; p F is the artificial fracture pressure, MPa; B is the volume coefficient, dimensionless; q F is the fracture flow rate, m 3 / d; W F is the artificial fracture width, m; h F is the artificial fracture height, m; φ is the matrix porosity, dimensionless.

[0131] Initial conditions:

[0132] p| t＝0 = p i

[0133] Inner boundary conditions:

[0134]

[0135] Outer boundary conditions:

[0136]

[0137] Connection surface conditions:

[0138]

[0139] where p i is the initial pressure, MPa; y w is the wellbore radius, m; Γ represents the outer boundary position, m; p1, p2 are the pressures in the inner and outer regions, MPa.

[0140] The numerical solution of the seepage mathematical model is carried out. The principle is to use the difference equations of space and time discretization to replace the partial differential equations. The flow of single-phase incompressible fluid is expressed by Darcy's law and the law of conservation of mass:

[0141]

[0142]

[0143] where ρ is the reservoir density, g / cm 3 ; is the porosity, dimensionless; is the volume change, m 3 .

[0144] The finite volume fully implicit discretization method is adopted and grid generation is considered. The mass conservation equation is integrated into the element volume to obtain:

[0145]

[0146] Converting the volume integral on the element boundary to the surface integral, we get:

[0147]

[0148] Among them, is the change in surface area, m 2 .

[0149] Considering the set of cells connected to the cell, the well, and the reservoir boundary, discretize the time between t n and t n+1 to obtain the mass conservation expression for any point in the reservoir:

[0150]

[0151] Among them, j represents the grid cell, j i is the set of grid cells, W represents the well, and B represents the boundary.

[0152] According to the mass conservation expression for any point in the reservoir, determine the double logarithmic curve of the template data corresponding to the seepage mathematical model, and fit it with the double logarithmic curve of the measured data. The fitting diagram is as Figure 4 shown, determine the basic parameters of the numerical model of the reservoir to establish the numerical model of the reservoir, as Figure 5a , Figure 5b shown.

[0153] (4) Refer to Figure 4 , compare the double logarithmic curve of the template data with the double logarithmic curve of the measured data. The curve segment with a difference greater than the preset threshold is in the inner area of the reservoir. The double logarithmic curve of the measured data has a concave segment, that is, the characteristics of fracture linear flow. Accordingly, adjust the fracture parameters of the reservoir numerical model. The corresponding double logarithmic curve comparison diagram is as Figure 6 shown; the curve segment with a difference less than the preset threshold is in the inner area of the reservoir. Use MATLAB numerical software to program and randomly generate discrete fractures, and import them into the numerical model through the discrete fracture model, that is, adjust the fracture parameters of the numerical model, as Figure 7a , Figure 7b shown, so as to fit the adjusted double logarithmic curve with the double logarithmic curve of the measured data, as Figure 8 shown.

[0154] (5) According to the numerical model after adjusting the parameters twice, conduct reservoir parameter interpretation, and the results are shown as Parameter 1 in Table 3.

[0155] Table 3 Well test analysis of reservoir parameters

[0156]

[0157] Again, the physical model corresponding to the inner area of the reservoir is the dual porosity medium model; the physical model corresponding to the outer area of the reservoir is the discrete fracture network model;

[0158] Correspondingly, for the curve segment in the double logarithmic curve of the template data where the difference is greater than the preset threshold, the first model parameter of the numerical model is adjusted, specifically as follows:

[0159] Judge whether the curve segment with a difference greater than the preset threshold is in the inner area of the reservoir;

[0160] If so, adjust the pore parameters of the numerical model;

[0161] If not, adjust the fracture parameters of the numerical model;

[0162] For the curve segment in the double logarithmic curve of the template data where the difference is less than the preset threshold, the second model parameter of the numerical model is adjusted, specifically as follows:

[0163] Judge whether the curve segment with a difference less than the preset threshold is in the inner area of the reservoir;

[0164] If so, adjust the pore parameters of the numerical model;

[0165] If not, adjust the fracture parameters of the numerical model.

[0166] Taking a vertical well in a fractured gas reservoir as an example, the solution of this example is introduced in detail.

[0167] The solution of this example is similar to the solution of the previous example, the difference is that:

[0168] (1) The reservoir geological parameter data and production data are shown in Parameter Two in Table 1 and Table 4 respectively.

[0169] Table 4 Reservoir Production Data

[0170] Production time (h) Pressure (MPa) <![CDATA[Cumulative gas production (10 4 m 3 )]]> Deviation factor (dimensionless) 0 87.4 0.00 1.5315 24.00 87.84 77.50 1.5314 48.01 87.84 113.40 1.5343 96.13 89.95 183.61 1.5413 144.34 87.84 255.43 1.5317 216.73 86.25 331.31 1.5320 264.15 85.72 393.46 1.5408 413.53 93.66 393.46 1.5716 503.13 94.19 451.73 1.5821

[0171] (2) The dual-porosity medium seepage mathematical model in the inner area of the reservoir:

[0172]

[0173] The discrete fracture seepage mathematical model in the outer area of the reservoir:

[0174]

[0175] Among them, l is the fracture length, m; ω1 is the storage ratio in the inner area, dimensionless; p 1f is the fracture pressure in the inner area, MPa; p 1m is the matrix pressure in the inner area, MPa; λ1 is the inter-zone channeling coefficient, dimensionless; p2 is the pressure in the outer area, φ2 is the porosity in the outer area; k2 is the permeability in the outer area.

[0176] (3) The reservoir parameters obtained by well test analysis are shown in Parameter Two in Table 3.

[0177] The processing methods of the remaining steps and the data graphs are similar to those in the previous example, and will not be elaborated in this example.

[0178] In addition, the physical models corresponding to the inner area and the outer area of the reservoir can also be the same.

[0179] For example, the physical models corresponding to the inner area and the outer area of the reservoir are both discrete fracture network models;

[0180] Correspondingly, for the curve segments in the double logarithmic curve of the template data where the difference is greater than the preset threshold, the first model parameter of the numerical model is adjusted, specifically: the fracture parameters of the numerical model are adjusted;

[0181] For the curve segments in the double logarithmic curve of the template data where the difference is less than the preset threshold, the second model parameter of the numerical model is adjusted, specifically: the fracture parameters of the numerical model are adjusted;

[0182] The fracture size characterized by the first model parameter is larger than the fracture size characterized by the second model parameter.

[0183] Taking a vertical well in a fractured gas reservoir as an example, the solution of this example is introduced in detail.

[0184] The solution of this example is similar to the solution of the previous example, with the differences being:

[0185] (1) The reservoir geological parameter data and production data are shown in Parameter Three in Table 1 and Table 5 respectively.

[0186] Table 5 Reservoir Production Data

[0187] Production time (h) Pressure (MPa) <![CDATA[Cumulative gas production (10 4 m 3 )]]> Deviation factor (dimensionless) 0 41.9 0.00 0.96354 48.21 41.5 45.46 0.95951 408.12 46.8 61.56 0.95427 468.12 43.6 136.42 0.95044 540.32 46.6 191.34 0.94547 588.26 49.3 223.14 0.954323 534.31 45.4 263.24 0.947651 582.34 45.6 300.06 0.948483 700.43 68.1 309.46 0.936510

[0188] (2) Discrete fracture seepage mathematical model for the inner area of the reservoir:

[0189]

[0190] Discrete fracture seepage mathematical model for the outer area of the reservoir:

[0191]

[0192] (3) The reservoir parameters obtained from well test analysis are shown in Parameter Three in Table 3.

[0193] The processing methods of the remaining steps and the data graphs are similar to those in the previous example, and will not be elaborated in this example.

[0194] Again, for example, the physical models corresponding to the inner area and the outer area of the reservoir are both dual porosity medium models;

[0195] Correspondingly, for the curve segments in the double logarithmic curve of the template data where the difference is greater than the preset threshold, the first model parameter of the numerical model is adjusted. Specifically, the pore parameter of the numerical model is adjusted;

[0196] For the curve segments in the double logarithmic curve of the template data where the difference is less than the preset threshold, the second model parameter of the numerical model is adjusted. Specifically, the pore parameter of the numerical model is adjusted; the pore size characterized by the first model parameter is larger than the pore size characterized by the second model parameter.

[0197] Taking a vertical well in a fractured gas reservoir as an example, the solution of this example is introduced in detail.

[0198] The solution of this example is similar to the solution of the previous example, except that:

[0199] (1) The reservoir geological parameter data and production data are shown in Parameter Four in Table 1 and Table 6 respectively.

[0200] Table 6 Reservoir Production Data

[0201] Production time (h) Pressure (MPa) <![CDATA[Cumulative gas production (10 4 m 3 )]]> Deviation factor (dimensionless) 0 115.8 0.00 1.8316 141.2 116.0 292.3 1.8311 285.2 116.1 582.1 1.8343 405.2 115.9 871.3 1.8310 528.7 116.0 1160.3 1.8314 717.3 115.6 1494.1 1.8289 861.2 115.7 1691.1 1.8291 1005.2 115.4 1881.5 1.8283 1413.7 115.6 2451.3 1.8284

[0202] (2) The seepage mathematical model of the dual-porosity medium in the inner area of the reservoir:

[0203]

[0204] The seepage mathematical model of the dual-porosity medium in the outer area of the reservoir:

[0205]

[0206] (3) The reservoir parameters obtained by well test analysis are shown in Parameter Four in Table 3.

[0207] The processing methods and data graphs of the remaining steps are similar to those of the previous example, and will not be elaborated in this example.

[0208] In the above four examples, the numerical well test method for discrete fractured low-porosity sandstone reservoirs provided by this application is used for well test analysis. According to the reservoir geological parameters and production data, a physical model and the corresponding seepage mathematical model are established and solved, the double logarithmic curve of the template data is plotted, and according to the difference between it and the double logarithmic curve of the measured data, it is fitted in sub-curve segments. The numerical model obtained by fitting can more accurately interpret the reservoir parameters and reflect the reservoir production situation.

[0209] This application also provides a numerical well test device 90 for discrete fractured low-porosity sandstone reservoirs, which is used to implement the numerical well test method for discrete fractured low-porosity sandstone reservoirs in each of the above method embodiments or examples, as Figure 9 shown. The device includes:

[0210] The first acquisition module 901 is configured to acquire the existing production data of the reservoir, obtain the double logarithmic curve of the measured data based on the existing production data, establish a numerical model of the reservoir, and determine the double logarithmic curve of the template data corresponding to the numerical model, where both the measured data and the template data are pressure-related data;

[0211] The second acquisition module 902 is configured to acquire the difference in pressure-related data between the double logarithmic curve of the measured data and the double logarithmic curve of the template data in the same coordinate system;

[0212] The first fitting module 903 is configured to, for the curve segment of the double logarithmic curve of the template data where the difference is greater than the preset threshold, adjust the first model parameter of the numerical model so that the double logarithmic curve obtained after the adjustment fits the double logarithmic curve of the measured data;

[0213] The second fitting module 904 is configured to, for the curve segment of the double logarithmic curve of the template data where the difference is less than the preset threshold, adjust the second model parameter of the numerical model so that the double logarithmic curve obtained after the adjustment fits the double logarithmic curve of the measured data;

[0214] The interpretation module 905 is configured to perform reservoir parameter interpretation based on the numerical model after two fittings.

[0215] The discrete fractured low-porosity sandstone reservoir numerical well testing device provided by the embodiments of the present application can execute the above method embodiments or examples. For the specific implementation principle and technical effects, reference can be made to the above method embodiments, which will not be elaborated here.

[0216] The present application also provides a discrete fractured low-porosity sandstone reservoir numerical well testing device for implementing the discrete fractured low-porosity sandstone reservoir numerical well testing method in the above method embodiments or examples. The device includes: a memory, a processor;

[0217] The memory is used to store computer programs / instructions; the processor is configured to execute the discrete fractured low-porosity sandstone reservoir numerical well testing method in any of the above embodiments or examples according to the computer programs / instructions stored in the memory.

[0218] The memory may include a high-speed random access memory (Random Access Memory, RAM), and may also include non-volatile memory (Non-Volatile Memory, NVM), such as at least one disk memory, and may also be a USB flash drive, a mobile hard disk, a read-only memory, a disk, or an optical disc, etc.

[0219] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The steps of the method disclosed in combination with the invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0220] Optionally, the memory can be either independent or integrated with the processor.

[0221] When the memory is a device independent of the processor, the device may further include a bus. The bus is used to connect the memory and the processor. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0222] The discrete fractured low-porosity sandstone reservoir numerical well testing equipment provided in this embodiment can be used to execute the above method embodiments or examples, and its implementation manner and technical effects are similar, so details are not described herein again.

[0223] This application also provides a computer-readable storage medium. The computer-readable storage medium stores computer programs / instructions. When the computer programs / instructions are executed by a processor, the numerical well testing method for discrete fractured low-porosity sandstone reservoirs in the above method embodiments or examples is implemented.

[0224] Among them, the computer-readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transfer of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, the computer-readable storage medium is coupled to the processor so that the processor can read information from the computer-readable storage medium and write information to the computer-readable storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can be located in an Application Specific Integrated Circuits (ASIC). In addition, the ASIC can be located in the user equipment. Of course, the processor and the computer-readable storage medium can also exist as discrete components in the communication device.

[0225] Specifically, the computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically-Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable read-only memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0226] In several embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or module can be in electrical, mechanical, or other forms.

[0227] Among them, each module can be physically separated, for example, installed at different positions of a device, or installed on different devices, or distributed to multiple network units, or distributed to multiple processors. Each module can also be integrated together, for example, installed in the same device, or integrated in a set of code. Each module can exist in the form of hardware, or can also exist in the form of software, or can also be implemented in the form of software plus hardware. This application can select some or all of the modules according to actual needs to achieve the purpose of the solution of this embodiment.

[0228] When the integrated module is implemented in the form of a software functional module, it can be stored in a computer-readable storage medium. The above-mentioned software functional module stored in a storage medium includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute some steps of the methods of various embodiments of this application.

[0229] It should be understood that although the steps in the flowcharts in the above embodiments are shown in sequence according to the arrows, these steps do not necessarily need to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and they can be executed in other orders. Moreover, at least a part of the steps in the figure may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily need to be executed at the same time, but can be executed at different times. Their execution order does not necessarily need to be sequential, but can be executed alternately or alternately with at least a part of other steps or sub-steps or stages of other steps.

[0230] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and not to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features. And these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of various embodiments of this application.

Claims

1. A numerical well test method for discrete fractured low-porosity sandstone reservoirs, characterized in that, The method includes: Obtaining the existing production data of the reservoir, obtaining the double-logarithmic curve of the measured data according to the existing production data, establishing a numerical model of the reservoir, and determining the double-logarithmic curve of the template data corresponding to the numerical model, where the measured data and the template data are both pressure-related data; Obtaining the difference in pressure-related data between the double-logarithmic curve of the measured data and the double-logarithmic curve of the template data in the same coordinate system; For the curve segment of the double-logarithmic curve of the template data where the difference is greater than the preset threshold, adjusting the first model parameter of the numerical model to fit the adjusted double-logarithmic curve with the double-logarithmic curve of the measured data; For the curve segment of the double-logarithmic curve of the template data where the difference is less than the preset threshold, adjusting the second model parameter of the numerical model to fit the adjusted double-logarithmic curve with the double-logarithmic curve of the measured data; Interpret the reservoir parameters according to the numerical model after two fittings.

2. The method according to claim 1, characterized in that The establishing of the numerical model of the reservoir includes: Establishing a physical model of the reservoir, establishing a corresponding mathematical model according to the physical model of the reservoir and solving it to determine the double-logarithmic curve of the template data corresponding to the mathematical model; Fitting the double-logarithmic curve of the template data corresponding to the mathematical model with the double-logarithmic curve of the measured data, determining the basic parameters of the numerical model of the reservoir, and establishing the numerical model of the reservoir.

3. The method according to claim 2, characterized in that, The establishing of the physical model of the reservoir, establishing a corresponding mathematical model according to the physical model of the reservoir and solving it includes: According to the heterogeneity of the reservoir, using a radial composite model to divide the reservoir into an inner reservoir area and an outer reservoir area, and establishing a physical model of the inner reservoir area and a physical model of the outer reservoir area; According to the physical models of the inner reservoir area and the outer reservoir area, establishing corresponding mathematical models and solving them. The mathematical models at least include the mathematical model of the inner reservoir area, the mathematical model of the outer reservoir area, and the boundary conditions between the inner reservoir area and the outer reservoir area.

4. The method according to claim 3, characterized in that The physical model corresponding to the inner reservoir area is different from the physical model corresponding to the outer reservoir area.

5. The method according to claim 4, wherein The physical model corresponding to the inner reservoir area is a discrete fracture network model; the physical model corresponding to the outer reservoir area is a dual-porosity medium model; Correspondingly, for the curve segment of the double-logarithmic curve of the template data where the difference is greater than the preset threshold, adjusting the first model parameter of the numerical model specifically as: Judging whether the curve segment where the difference is greater than the preset threshold is in the inner reservoir area; If so, adjusting the fracture parameters of the numerical model; If not, adjusting the pore parameters of the numerical model; For the curve segment of the double-logarithmic curve of the template data where the difference is less than the preset threshold, adjusting the second model parameter of the numerical model specifically as: Judging whether the curve segment where the difference is less than the preset threshold is in the inner reservoir area; If so, adjusting the fracture parameters of the numerical model; If not, adjusting the pore parameters of the numerical model; Or, The physical model corresponding to the inner reservoir area is a dual-porosity medium model; the physical model corresponding to the outer reservoir area is a discrete fracture network model; Correspondingly, for the curve segment of the double-logarithmic curve of the template data where the difference is greater than a preset threshold, the first model parameter of the numerical model is adjusted. Specifically: Determine whether the curve segment where the difference is greater than the preset threshold is in the inner reservoir area; If so, adjust the pore parameters of the numerical model; If not, adjust the fracture parameters of the numerical model; For the curve segment of the double-logarithmic curve of the template data where the difference is less than the preset threshold, the second model parameter of the numerical model is adjusted. Specifically: Determine whether the curve segment where the difference is less than the preset threshold is in the inner reservoir area; If so, adjust the pore parameters of the numerical model; If not, adjust the fracture parameters of the numerical model.

6. The method according to claim 3, wherein The physical model corresponding to the inner reservoir area is the same as the physical model corresponding to the outer reservoir area.

7. The method according to claim 6, wherein The physical models corresponding to both the inner reservoir area and the outer reservoir area are discrete fracture network models; Correspondingly, for the curve segment of the double-logarithmic curve of the template data where the difference is greater than the preset threshold, the first model parameter of the numerical model is adjusted. Specifically: adjust the fracture parameters of the numerical model; For the curve segment of the double-logarithmic curve of the template data where the difference is less than the preset threshold, the second model parameter of the numerical model is adjusted. Specifically: adjust the fracture parameters of the numerical model; The fracture size characterized by the first model parameter is greater than the fracture size characterized by the second model parameter; Or, The physical models corresponding to both the inner reservoir area and the outer reservoir area are dual-porosity medium models; Correspondingly, for the curve segment of the double-logarithmic curve of the template data where the difference is greater than the preset threshold, the first model parameter of the numerical model is adjusted. Specifically: adjust the pore parameters of the numerical model; For the curve segment of the double-logarithmic curve of the template data where the difference is less than the preset threshold, the second model parameter of the numerical model is adjusted. Specifically: adjust the pore parameters of the numerical model; the pore size characterized by the first model parameter is greater than the pore size characterized by the second model parameter.

8. A numerical well test device for a discrete fractured low-porosity sandstone reservoir, characterized in that, The device includes: A first acquisition module, configured to acquire the existing production data of the reservoir, obtain the double-logarithmic curve of the measured data according to the existing production data, establish the numerical model of the reservoir, and determine the double-logarithmic curve of the template data corresponding to the numerical model, where the measured data and the template data are both pressure-related data; A second acquisition module, configured to acquire the difference in pressure-related data between the double-logarithmic curve of the measured data and the double-logarithmic curve of the template data in the same coordinate system; The first fitting module is configured to adjust the first model parameter of the numerical model for a curve segment in the double-logarithmic curve of the template data where the difference is greater than a preset threshold, so as to fit the double-logarithmic curve obtained after the adjustment with the double-logarithmic curve of the measured data; The second fitting module is configured to adjust the second model parameter of the numerical model for a curve segment in the double-logarithmic curve of the template data where the difference is less than a preset threshold, so as to fit the double-logarithmic curve obtained after the adjustment with the double-logarithmic curve of the measured data; The interpretation module is configured to interpret reservoir parameters according to the numerical model after the two fittings.

9. A numerical well test device for a discrete fractured low-porosity sandstone reservoir, characterized in that, The device includes: a memory and a processor; The memory is used to store computer programs / instructions; The processor is configured to execute the discrete fractured low-porosity sandstone reservoir numerical well testing method according to any one of claims 1 to 7 based on the computer programs / instructions stored in the memory.

10. A computer-readable storage medium, characterized in that, Computer programs / instructions are stored in the computer-readable storage medium, and when the computer programs / instructions are executed by the processor, the discrete fractured low-porosity sandstone reservoir numerical well testing method according to any one of claims 1 to 7 is implemented.

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