Tight gas reservoir productivity prediction method and system, terminal and medium
By calculating fracture density and ground stress parameters through electrical imaging logging and array acoustic wave data, and fitting the test oil production capacity using regression equations, the problem of inaccurate production capacity prediction of tight gas reservoirs was solved, and more accurate production capacity prediction was achieved.
Patent Information
- Application Number
- CN202410314729.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-19
- Publication Date
- 2025-09-19
AI Technical Summary
In the existing technology for predicting the productivity of tight gas reservoirs, conventional logging data comparing and analyzing the production of test gas and production with the production capacity established by curve shape or curve value has poor predictability and cannot accurately predict the productivity of tight gas reservoirs.
By acquiring electrical imaging logging data and array acoustic wave data, the fracture density and ground stress parameters are calculated, and the test oil production capacity is obtained by fitting the regression equation. The production capacity prediction method combining ground stress and fracture composition takes into account the reservoir permeability characteristics and engineering factors.
It improves the accuracy of tight gas reservoir production capacity prediction and provides more accurate geological basis to support the formulation of development plans.
Smart Images
Figure CN120671948A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a method, system, terminal and medium for predicting the productivity of tight gas reservoirs. Background Art
[0002] Large, medium- and high-permeability oil and gas reservoirs in China have entered the late stages of development, and major oil and gas fields are shifting their exploration focus to unconventional resources such as tight gas and shale gas. In natural gas reservoir exploration and development, productivity is a key concern for technicians. Accurate productivity prediction is crucial for oil and gas field evaluation and significantly aids in the design of development plans. Geophysical logging is a key tool for predicting gas reservoir productivity. Domestic and international researchers have conducted research on reservoir productivity prediction methods using geophysical logging, achieving considerable success. However, each productivity prediction method has a certain scope of application. The traditional method, the morphological combination method, organically combines nine conventional logging curves, compares and analyzes the production of gas and production tests with the morphological combination of the curves, and can obtain the logging curve morphological combination pattern for qualitative analysis of the productivity of the gas reservoir in the study area. The envelope area method uses the dry layer as the baseline, and the area enclosed between the reservoir logging and the baseline is the envelope area. There is also an area that uses the compensated density and acoustic wave time difference ratio Z and the resistivity curve to overlap. It is not suitable for unconventional gas reservoirs. Accurate prediction of tight sandstone gas reservoirs still requires a set of widely applicable technical methods.
[0003] The main factors that have been identified as controlling gas reservoir productivity are categorized as geological, engineering, and production factors. Gas reservoir productivity is determined by the reservoir's own conditions, the external environment, and the characteristics of the oil and gas. For a specific gas reservoir, the porosity, permeability, and gas saturation, which reflect the oil and gas storage space, all affect the reservoir's reserves and production difficulty, but other factors are also important. Geostress is related to its seepage capacity and engineering factors. The more active the fractures, the smaller the relative value of the geostress, the smaller the engineering impact, and the easier it is to develop. Furthermore, the concentration of fractures and the angle between them and the main geostress direction will affect the engineering development results and the fracture's seepage capacity, and to a certain extent, can determine the extent and difficulty of developing the gas strata within the reservoir.
[0004] Accurate production capacity prediction for tight gas reservoirs is closely related to testing methods and development plan formulation. Previously, production capacity prediction methods based solely on comparative analysis of gas and production test outputs with curve morphology or curve values using conventional logging data worked well for conventional reservoirs. However, for tight gas reservoirs, multiple factors need to be considered, resulting in reduced accuracy in the prediction structure.
[0005] Therefore, a new productivity prediction method based on geostress and fractures is needed to better provide a geological basis for the subsequent development of tight gas reservoirs. Summary of the Invention
[0006] In order to overcome the defects of the above-mentioned prior art, the purpose of the present invention is to provide a method, system, terminal and medium for predicting the production capacity of tight gas reservoirs, so as to solve the technical problem of poor production capacity prediction in tight gas reservoirs by comparing and analyzing the production of test gas and production with the curve shape or curve value of conventional logging data.
[0007] The present invention is achieved through the following technical solutions:
[0008] In a first aspect, the present invention provides a method for predicting the productivity of a tight gas reservoir, comprising:
[0009] Acquiring electrical imaging logging data and a number of fracture numbers, and calculating fracture density based on the electrical imaging logging data and the number of fractures;
[0010] Acquiring array acoustic wave data, and calculating ground stress parameters based on the array acoustic wave data;
[0011] Calculating the shear stress and effective stress of each crack using the in-situ stress parameters, and obtaining the ratio of the shear stress to the effective stress of each crack;
[0012] According to the fracture density and the ratio of the shear stress to the effective stress of each fracture, the production capacity of the oil test is obtained by fitting the regression equation, thereby completing the production capacity prediction of the tight gas reservoir.
[0013] Preferably, the electrical imaging logging data is used to calculate the fracture density based on the number of fractures, wherein the fracture density calculation formula is as follows:
[0014] ρ=N / H
[0015] Where ρ is the fracture density, a decimal; N is the number of fractures, an integer; and H is the effective thickness of the reservoir, m.
[0016] Preferably, the array acoustic wave data is used to calculate the ground stress parameter, wherein the calculation formula of the ground stress parameter is as follows:
[0017] l=sinαsinβ
[0018] m=cosαsinβ
[0019] n=cosβ
[0020] Among them, l, m, and n are the component coefficients of the three-axis directions of the ground stress; α is the angle between the crack azimuth and the maximum principal stress direction; β is the crack inclination.
[0021] Preferably, the shear stress through each crack is calculated as follows:
[0022]
[0023] The calculation formula for the effective stress of each crack is as follows:
[0024] σe=(l 2 *SHmax+m 2 *SHmin+n 2 *SV)-pprs
[0025] The ratio of shear stress to effective stress in each crack is calculated as follows:
[0026]
[0027] Wherein, α is the angle between the fracture azimuth and the maximum principal stress direction, β is the fracture inclination, pprs is the pore pressure, SHmax is the maximum horizontal principal stress, SHmin is the minimum horizontal principal stress, SV is the vertical stress, l, m, and n are the component coefficients of the three axial directions of the in-situ stress, T is the shear stress, σe is the effective stress, and TS is the ratio of the shear stress to the effective stress.
[0028] Preferably, the specific process of fitting to obtain the test oil production capacity is as follows:
[0029] The average values of the crack density and the ratio of the shear stress to the effective stress of each crack were set as independent variables;
[0030] The average production of the oil test data divided by the effective reservoir thickness in the oil test interval is set as the dependent variable;
[0031] The least squares fitting method is used to establish a nonlinear regression model based on the relationship between independent variables and dependent variables. The nonlinear regression model is evaluated and optimized using evaluation indicators to obtain an optimized nonlinear regression model, which is then used to predict production capacity and obtain the production of tight gas formations.
[0032] Furthermore, the process of capacity forecasting is as follows:
[0033] The fitted nonlinear regression model was applied to other untested well sections. The average value of the ratio of fracture shear stress to effective stress calculated in the untested well sections was used as the independent variable. The fitted nonlinear model was used to calculate the predicted value of the well test productivity of the well section.
[0034] In a second aspect, the present invention provides a tight gas reservoir productivity prediction system, comprising:
[0035] A first acquisition module is used to acquire electrical imaging logging data and a number of fracture numbers, and calculate the fracture density based on the electrical imaging logging data and the number of fractures;
[0036] A second acquisition module is used to acquire array acoustic wave data and calculate the ground stress parameters according to the array acoustic wave data;
[0037] a processing module, configured to calculate the shear stress and effective stress of each fracture using the in-situ stress parameters, and obtain a ratio of the shear stress to the effective stress of each fracture;
[0038] The prediction module is used to obtain the test production capacity by using a regression equation according to the fracture density and the ratio of the shear stress to the effective stress of each fracture, thereby completing the tight gas reservoir production capacity prediction.
[0039] Preferably, the prediction module is provided with a first setting module, a second setting module and a fitting module;
[0040] A first setting module is used to set the average value of the crack density and the ratio of the shear stress to the effective stress of each crack as independent variables;
[0041] The second setting module is used to set the average production of oil test data divided by the effective reservoir thickness in the oil test layer as a dependent variable;
[0042] The fitting module is used to use the least squares fitting method to establish a nonlinear regression model based on the relationship between independent variables and dependent variables; the nonlinear regression model is evaluated and optimized using evaluation indicators to obtain the optimized nonlinear regression model, which is then used for production capacity prediction to obtain the production of tight gas layers.
[0043] In a third aspect, a mobile terminal comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for predicting the production capacity of tight gas reservoirs when executing the computer program.
[0044] In a fourth aspect, a computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the method for predicting the production capacity of tight gas reservoirs as described above.
[0045] Compared with the prior art, the present invention has the following beneficial technical effects:
[0046] The present invention provides a method, system, terminal and medium for predicting the productivity of tight gas reservoirs. During specific operation, acoustic logging and electrical imaging logging are combined to respectively obtain electrical imaging logging data and the number of fractures, and calculate the fracture density based on the number of fractures using the electrical imaging logging data; and array acoustic wave data are obtained, and geostress parameters are calculated based on the array acoustic wave data. The shear stress and effective stress of each fracture are calculated using the geostress parameters, and the ratio of the shear stress to the effective stress of each fracture is obtained; finally, the test oil productivity is obtained based on the fracture density and the ratio of the shear stress to the effective stress of each fracture using a regression equation, thereby completing the tight gas reservoir productivity prediction, realizing the evaluation of geostress and fracture effectiveness, thereby realizing the logging productivity prediction considering the reservoir permeability characteristics and engineering factors, and improving the accuracy of the prediction structure. BRIEF DESCRIPTION OF THE DRAWINGS
[0047] Figure 1 Flow chart of a method for predicting the productivity of a tight gas reservoir according to an embodiment of the present invention;
[0048] Figure 2 Flowchart of a method for fitting and obtaining oil test productivity in an embodiment of the present invention;
[0049] Figure 3 Schematic diagram of the principle structure of a tight gas reservoir productivity prediction system in an embodiment of the present invention;
[0050] Figure 4 This is a schematic diagram of the principle structure of the prediction module in an embodiment of the present invention;
[0051] Figure 5 Schematic diagram of pre-processing and crack picking in an embodiment of the present invention;
[0052] Figure 6 Schematic diagram of the ratio TS of each fracture coefficient of a certain well in an embodiment of the present invention;
[0053] Figure 7 This is a schematic diagram of the correlation between the least squares fitting result and the actual result in an embodiment of the present invention;
[0054] In the figure: 1-first acquisition module; 2-second acquisition module; 3-processing module; 4-prediction module; 41-first setting module; 42-second setting module; 43-fitting module. DETAILED DESCRIPTION
[0055] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.
[0056] The present invention is described in further detail below with reference to the accompanying drawings:
[0057] The purpose of the present invention is to provide a method, system, terminal and medium for predicting the productivity of tight gas reservoirs, so as to solve the technical problem of poor productivity prediction in tight gas reservoirs by comparing and analyzing the production of test gas and production with the curve shape or curve value of conventional logging data.
[0058] Example 1
[0059] See also Figure 1 In one embodiment of the present invention, a method for predicting the productivity of a tight gas reservoir is provided, comprising the following steps:
[0060] Step 1: obtaining electrical imaging logging data and a number of fracture numbers, and calculating fracture density based on the electrical imaging logging data and the number of fractures;
[0061] Specifically, in the embodiments of this specification, Figure 5 As shown, the imaging logging data is acquired by logging instruments. The measured reservoirs can be conventional sandstone and tight sandstone. The measurement signals of different reservoirs are affected to varying degrees by the mud type. The methods for acquiring logging data from the measured reservoirs are not specifically limited in the embodiments of this specification and can be selected based on actual working conditions. After acquiring the data, data processing is performed, including data loading, well deviation correction, velocity correction, electrical alignment, and image generation. Static image curves are generated using 256 color levels, referred to as image curves, and their data type is matrix.
[0062] The electrical imaging logging data is used to calculate the fracture density based on the number of fractures. The fracture density calculation formula is as follows:
[0063] ρ=N / H
[0064] Where ρ is the fracture density, a decimal; N is the number of fractures, an integer; and H is the effective thickness of the reservoir, m.
[0065] Step 2: Acquire array acoustic wave data, and calculate ground stress parameters based on the array acoustic wave data;
[0066] Specifically, in the embodiment of this specification, the array acoustic wave data is obtained by well logging measurement, and the longitudinal wave time difference and the shear wave time difference in step S2 are obtained in the array acoustic wave data. The steps for obtaining them include data loading, depth correction, and calculation of overlying formation pressure, pore pressure and other ground stress parameters.
[0067] Step 3, calculating the shear stress and effective stress of each crack using the in-situ stress parameter, and obtaining the ratio of the shear stress to the effective stress of each crack;
[0068] Specifically, first calculate the ground stress components in the three axial directions:
[0069] l=sinαsinβ
[0070] m=cosαsinβ
[0071] n=cosβ
[0072] Among them, l, m, and n are the component coefficients of the three-axis directions of the ground stress; α is the angle between the crack azimuth and the maximum principal stress direction; β is the crack inclination.
[0073] The shear stress of each crack is calculated as follows:
[0074]
[0075] The calculation formula for the effective stress of each crack is as follows:
[0076] σe=(l 2 *SHmax+m 2 *SHmin+n 2 *SV)-pprs
[0077] The ratio of shear stress to effective stress in each crack is calculated as follows:
[0078]
[0079] Wherein, α is the angle between the fracture azimuth and the maximum principal stress direction, β is the fracture inclination, pprs is the pore pressure, SHmax is the maximum horizontal principal stress, SHmin is the minimum horizontal principal stress, SV is the vertical stress, l, m, and n are the component coefficients of the three axial directions of the in-situ stress, T is the shear stress, σe is the effective stress, and TS is the ratio of the shear stress to the effective stress.
[0080] Among them, the ratio of each crack coefficient TS is as follows Figure 6 shown.
[0081] Step 4: Based on the fracture density and the ratio of the shear stress to the effective stress of each fracture, the test production capacity is obtained by fitting the regression equation, such as Figure 7As shown in the figure, the tight gas reservoir production capacity prediction is completed.
[0082] Specifically, according to Figure 2 As shown in Figure 2, the specific process of fitting to obtain the test oil production capacity is as follows:
[0083] Step 41, setting the average value of the crack density and the ratio of the shear stress to the effective stress of each crack as independent variables;
[0084] Step 42, setting the average production of the oil test data divided by the effective reservoir thickness in the oil test layer as the dependent variable;
[0085] Step 43: Use the least squares fitting method to establish a nonlinear regression model based on the relationship between the independent variable and the dependent variable; use the evaluation index to evaluate and optimize the nonlinear regression model to obtain the optimized nonlinear regression model, and use it to predict the production capacity to obtain the production of the tight gas layer.
[0086] The process of capacity forecasting is as follows:
[0087] The fitted nonlinear regression model was applied to other untested well sections. The average value of the ratio of fracture shear stress to effective stress calculated in the untested well sections was used as the independent variable. The fitted nonlinear model was used to calculate the predicted value of the well test productivity of the well section.
[0088] The present invention provides a method for predicting the productivity of tight gas reservoirs. During specific operation, acoustic logging and electrical imaging logging are combined to respectively obtain electrical imaging logging data and the number of fractures, and the fracture density is calculated based on the number of fractures using the electrical imaging logging data; array acoustic wave data are obtained, and geostress parameters are calculated based on the array acoustic wave data. The shear stress and effective stress of each fracture are calculated using the geostress parameters, and the ratio of the shear stress to the effective stress of each fracture is obtained; finally, the test oil productivity is obtained based on the fracture density and the ratio of the shear stress to the effective stress of each fracture using a regression equation fitting, thereby completing the tight gas reservoir productivity prediction, realizing the evaluation of geostress and fracture effectiveness, thereby realizing the logging productivity prediction considering the reservoir permeability characteristics and engineering factors, and improving the accuracy of the prediction structure.
[0089] Example 2
[0090] according to Figure 3 As shown, the present invention also provides a tight gas reservoir productivity prediction system, comprising:
[0091] The first acquisition module 1 is used to acquire electrical imaging logging data and a number of fracture numbers, and calculate the fracture density based on the electrical imaging logging data and the number of fractures;
[0092] A second acquisition module 2 is used to acquire array acoustic wave data and calculate the ground stress parameters based on the array acoustic wave data;
[0093] Processing module 3, used to calculate the shear stress and effective stress of each fracture using the in-situ stress parameters, and obtain the ratio of the shear stress to the effective stress of each fracture;
[0094] The prediction module 4 is used to obtain the test production capacity based on the fracture density and the ratio of the shear stress to the effective stress of each fracture by using a regression equation fitting method, thereby completing the tight gas reservoir production capacity prediction.
[0095] Specifically, according to Figure 4 As shown, the prediction module 4 is provided with a first setting module 41, a second setting module 42 and a fitting module 43;
[0096] A first setting module 41 is used to set the crack density and the average value of the ratio of the shear stress to the effective stress of each crack as independent variables;
[0097] The second setting module 42 is used to set the average production of the oil test data divided by the effective reservoir thickness in the oil test layer as a dependent variable;
[0098] The fitting module 43 is used to use the least squares fitting method to establish a nonlinear regression model based on the relationship between the independent variable and the dependent variable; use the evaluation index to evaluate and optimize the nonlinear regression model to obtain the optimized nonlinear regression model, and use it for production capacity prediction to obtain the production of the tight gas layer.
[0099] Example 3
[0100] The present invention also provides a mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, such as a tight gas reservoir productivity prediction program.
[0101] When the processor executes the computer program, the steps of the above-mentioned method for predicting the productivity of tight gas reservoirs are implemented, for example:
[0102] Acquiring electrical imaging logging data and a number of fracture numbers, and calculating fracture density based on the electrical imaging logging data and the number of fractures;
[0103] Acquiring array acoustic wave data, and calculating ground stress parameters based on the array acoustic wave data;
[0104] Calculating the shear stress and effective stress of each crack using the in-situ stress parameters, and obtaining the ratio of the shear stress to the effective stress of each crack;
[0105] According to the fracture density and the ratio of the shear stress to the effective stress of each fracture, the production capacity of the oil test is obtained by fitting the regression equation, thereby completing the production capacity prediction of the tight gas reservoir.
[0106] Alternatively, when the processor executes the computer program, the functions of each module in the above system are realized, for example:
[0107] The first acquisition module 1 is used to acquire electrical imaging logging data and a number of fracture numbers, and calculate the fracture density based on the electrical imaging logging data and the number of fractures;
[0108] A second acquisition module 2 is used to acquire array acoustic wave data and calculate the ground stress parameters based on the array acoustic wave data;
[0109] Processing module 3, used to calculate the shear stress and effective stress of each fracture using the in-situ stress parameters, and obtain the ratio of the shear stress to the effective stress of each fracture;
[0110] The prediction module 4 is used to obtain the test production capacity based on the fracture density and the ratio of the shear stress to the effective stress of each fracture by using a regression equation fitting method, thereby completing the tight gas reservoir production capacity prediction.
[0111] Specifically, the prediction module 4 includes a first setting module 41, a second setting module 42 and a fitting module 43;
[0112] A first setting module 41 is used to set the crack density and the average value of the ratio of the shear stress to the effective stress of each crack as independent variables;
[0113] The second setting module 42 is used to set the average production of the oil test data divided by the effective reservoir thickness in the oil test layer as a dependent variable;
[0114] The fitting module 43 is used to use the least squares fitting method to establish a nonlinear regression model based on the relationship between the independent variable and the dependent variable; use the evaluation index to evaluate and optimize the nonlinear regression model to obtain the optimized nonlinear regression model, and use it for production capacity prediction to obtain the production of the tight gas layer.
[0115] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the mobile terminal.
[0116] For example, the computer program may be divided into a first acquisition module 1, a second acquisition module 2, a processing module 3 and a prediction module 4; wherein the prediction module 4 includes a first setting module 41, a second setting module 42 and a fitting module 43;
[0117] The specific functions of each module are as follows:
[0118] The first acquisition module 1 is used to acquire electrical imaging logging data and a number of fracture numbers, and calculate the fracture density based on the electrical imaging logging data and the number of fractures;
[0119] A second acquisition module 2 is used to acquire array acoustic wave data and calculate the ground stress parameters based on the array acoustic wave data;
[0120] Processing module 3, used to calculate the shear stress and effective stress of each fracture using the in-situ stress parameters, and obtain the ratio of the shear stress to the effective stress of each fracture;
[0121] The prediction module 4 is used to obtain the test production capacity based on the fracture density and the ratio of the shear stress to the effective stress of each fracture by using a regression equation fitting method, thereby completing the tight gas reservoir production capacity prediction.
[0122] A first setting module 41 is used to set the crack density and the average value of the ratio of the shear stress to the effective stress of each crack as independent variables;
[0123] The second setting module 42 is used to set the average production of the oil test data divided by the effective reservoir thickness in the oil test layer as a dependent variable;
[0124] The mobile terminal may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The mobile terminal may include, but is not limited to, a processor and a memory.
[0125] The processor may be a central processing unit (CPU), or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the mobile terminal, and uses various interfaces and lines to connect various parts of the entire mobile terminal.
[0126] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the mobile terminal by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.
[0127] The memory may mainly include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.
[0128] Example 4
[0129] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for predicting the productivity of a tight gas reservoir are implemented.
[0130] If the module / unit integrated in the mobile terminal is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.
[0131] Based on this understanding, the present invention can implement all or part of the processes in the above-mentioned method by using a computer program to instruct relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-mentioned method for predicting the productivity of tight gas reservoirs. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form.
[0132] The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.
[0133] It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media do not include electrical carrier signals and telecommunication signals.
[0134] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.
Claims
1. A method for predicting the productivity of tight gas reservoirs, characterized in that: include: Acquiring electrical imaging logging data and a number of fracture numbers, and calculating fracture density based on the electrical imaging logging data and the number of fractures; Acquiring array acoustic wave data, and calculating ground stress parameters based on the array acoustic wave data; Calculating the shear stress and effective stress of each crack using the in-situ stress parameters, and obtaining the ratio of the shear stress to the effective stress of each crack; According to the fracture density and the ratio of the shear stress to the effective stress of each fracture, the production capacity of the oil test is obtained by fitting the regression equation, thereby completing the production capacity prediction of the tight gas reservoir.
2. A method for predicting the productivity of a tight gas reservoir according to claim 1, characterized in that: The electrical imaging logging data is used to calculate the fracture density based on the number of fractures, where the fracture density calculation formula is as follows: ρ=N / H Where ρ is the fracture density, a decimal; N is the number of fractures, an integer; and H is the effective thickness of the reservoir, m.
3. The method for predicting the productivity of a tight gas reservoir according to claim 1, wherein: The ground stress parameters are calculated based on the array acoustic wave data, wherein the calculation formula of the ground stress parameters is as follows: l=sinαsinβ m=cosαsinβ n=cosβ Among them, l, m, and n are the component coefficients of the three-axis directions of the ground stress; α is the angle between the crack azimuth and the maximum principal stress direction; β is the crack inclination.
4. The method for predicting the productivity of a tight gas reservoir according to claim 1, wherein: The shear stress through each crack is calculated as follows: The calculation formula for the effective stress of each crack is as follows: σe=(l 2 *SHmax+m 2 *SHmin+n 2 *SV)-pprs The ratio of shear stress to effective stress in each crack is calculated as follows: Wherein, α is the angle between the fracture azimuth and the maximum principal stress direction, β is the fracture inclination, pprs is the pore pressure, SHmax is the maximum horizontal principal stress, SHmin is the minimum horizontal principal stress, SV is the vertical stress, l, m, and n are the component coefficients of the three axial directions of the in-situ stress, T is the shear stress, σe is the effective stress, and TS is the ratio of the shear stress to the effective stress.
5. The method for predicting the productivity of a tight gas reservoir according to claim 1, wherein: The specific process of fitting to obtain the test oil production capacity is as follows: The average values of the crack density and the ratio of the shear stress to the effective stress of each crack were set as independent variables; The average production of the oil test data divided by the effective reservoir thickness in the oil test interval is set as the dependent variable; Using the least squares fitting method, a nonlinear regression model was established based on the relationship between the independent and dependent variables; The nonlinear regression model is evaluated and optimized using evaluation indicators to obtain an optimized nonlinear regression model, which is then used to predict production capacity and obtain the production of tight gas formations.
6. A method for predicting the productivity of a tight gas reservoir according to claim 5, characterized in that: The process of capacity forecasting is as follows: The fitted nonlinear regression model was applied to other untested well sections. The average value of the ratio of fracture shear stress to effective stress calculated in the untested well sections was used as the independent variable. The fitted nonlinear model was used to calculate the predicted value of the well test productivity of the well section.
7. A tight gas reservoir productivity prediction system, characterized in that: include: A first acquisition module (1) is used to acquire electrical imaging logging data and a number of fracture numbers, and calculate the fracture density based on the electrical imaging logging data and the number of fractures; A second acquisition module (2) is used to acquire array acoustic wave data and calculate the ground stress parameters based on the array acoustic wave data; A processing module (3) is used to calculate the shear stress and effective stress of each crack using the in-situ stress parameter, and obtain the ratio of the shear stress to the effective stress of each crack; The prediction module (4) is used to obtain the test oil production capacity by fitting the regression equation based on the fracture density and the ratio of the shear stress to the effective stress of each fracture, thereby completing the tight gas reservoir production capacity prediction.
8. A tight gas reservoir productivity prediction system according to claim 7, characterized in that: The prediction module (4) is provided with a first setting module (41), a second setting module (42) and a fitting module (43); A first setting module (41) is used to set the average value of the crack density and the ratio of the shear stress to the effective stress of each crack as independent variables; A second setting module (42) is used to set the average production of the oil test data divided by the effective reservoir thickness in the oil test layer as a dependent variable; The fitting module (43) is used to use the least squares fitting method to establish a nonlinear regression model based on the relationship between the independent variable and the dependent variable; the nonlinear regression model is evaluated and optimized using the evaluation index to obtain the optimized nonlinear regression model, and the optimized nonlinear regression model is used for production capacity prediction to obtain the production of the tight gas layer.
9. A mobile terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for predicting the productivity of a tight gas reservoir as described in any one of claims 1 to 6 are implemented.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for predicting the productivity of a tight gas reservoir as claimed in any one of claims 1 to 6 are implemented.