Reservoir physical property inversion method and device based on rock physical sensitive parameters
By screening the reservoir's physical sensitivity elastic parameters and establishing their petrophysical relationship with the physical properties parameters, the problem of insufficient inversion accuracy in the existing technology is solved, and more accurate reservoir physical properties inversion is achieved, and the accuracy of reservoir quantitative evaluation is improved.
Patent Information
- Application Number
- CN202410151335.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-02-02
- Publication Date
- 2025-08-05
AI Technical Summary
In the prior art, the reservoir physical properties inversion method based on the rock physical physics model has the problem that the vertical and transverse wave velocity or impedance information is poorly sensitive to physical properties parameters, resulting in insufficient inversion accuracy.
By screening out the reservoir's physical properties sensitive elastic parameters, establishing its petrophysical relationship with physical properties parameters, using logging data for inversion training, determining the optimal parameters of the inversion algorithm, and generalizing it to seismic physical properties inversion, improving the inversion accuracy.
A more accurate reservoir physical properties inversion results are achieved, the inversion accuracy is improved, and better support is provided for reservoir quantitative evaluation.
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Figure CN120428320A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of oil and gas geophysical exploration, and particularly to a reservoir physical property inversion method and device based on rock physics sensitive parameters. Background Art
[0002] Reservoir physical property inversion has always been a difficult and hot topic in reservoir geophysical research. Currently, the commonly used methods include two categories: data-driven and model-driven. Model-driven reservoir physical property inversion mainly works based on the established theoretical rock physics model. However, it is often difficult to establish a theoretical rock physics model that can characterize the underground medium situation, which has complex relationships. To solve such highly nonlinear inverse problems, the physical property inversion technology under the Bayesian framework has been developed. Its principle is based on the relationship between the established elastic three parameters (P-wave velocity, S-wave velocity, density) and physical property parameters, introducing prior information to solve the solution of the maximum posterior probability.
[0003] The core of reservoir physical property inversion based on the rock physics model is the rock physics model relationship. Currently, for the inversion based on the rock physics model, since the seismic pre-stack elastic three-parameter inversion obtains the P-wave velocity, S-wave velocity, density or P-wave impedance, S-wave impedance, the rock physics model mainly establishes the relationship between the velocity or impedance of the elastic three-parameter inversion and the physical properties. However, the P-wave and S-wave velocity or impedance information is often less sensitive to some physical property parameters, and the physical property parameters may be more sensitive to other derived elastic factors except the P-wave velocity, S-wave velocity, density or P-wave impedance, S-wave impedance. Establishing the rock physics relationship between the physical properties and sensitive elastic parameters can promote more accurate reservoir physical property inversion.
[0004] Based on this technical background, the present invention studies a reservoir physical property inversion method and device based on rock physics sensitive parameters. Summary of the Invention
[0005] Aiming at the deficiencies of the prior art, the present invention provides a reservoir physical property inversion method and device based on rock physics sensitive parameters. This method selects sensitive elastic parameters for reservoir physical properties through screening, establishes the relationship between the selected sensitive elastic parameters and physical property parameters, determines the inversion algorithm parameters through the logging inversion process, and extends them to seismic physical property inversion to obtain more accurate inversion results, so as to improve the accuracy of reservoir physical property inversion and provide support for reservoir quantitative evaluation.
[0006] To achieve the above object, the first aspect of the present invention provides a reservoir physical property inversion method based on rock physics sensitive parameters, including:
[0007] Select sensitive elastic parameters from basic elastic parameters and their derived elastic parameters;
[0008] Establish the petrophysical relationship between reservoir physical property parameters and the selected sensitive elastic parameters;
[0009] Based on well logging data, use the selected sensitive elastic parameters as inputs for inversion training to determine the optimal parameters of the inversion algorithm;
[0010] Perform seismic inversion on the seismic data volume of the selected sensitive elastic parameters using the inversion algorithm with optimal parameters to obtain the seismic quantitative inversion results of reservoir physical properties.
[0011] The second aspect of the present invention provides a reservoir physical property inversion device based on petrophysical sensitive parameters, including:
[0012] A screening module for selecting sensitive elastic parameters from basic elastic parameters and their derived elastic parameters;
[0013] A relationship establishment module for establishing the petrophysical relationship between reservoir physical property parameters and the selected sensitive elastic parameters;
[0014] An optimal parameter determination module for performing inversion training based on well logging data using the selected sensitive elastic parameters as inputs to determine the optimal parameters of the inversion algorithm;
[0015] A seismic inversion module for performing seismic inversion on the seismic data volume of the selected sensitive elastic parameters using the inversion algorithm with optimal parameters to obtain the seismic quantitative inversion results of reservoir physical properties.
[0016] The third aspect of the present invention provides an electronic device, which includes:
[0017] A memory storing executable instructions;
[0018] A processor that runs the executable instructions in the memory to implement the reservoir physical property inversion method based on petrophysical sensitive parameters described in the first aspect.
[0019] [[ID=XXX]]The fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the reservoir physical property inversion method based on petrophysical sensitive parameters described in the first aspect.
[0020] The beneficial effects of the present invention include:
[0021] (1) The reservoir physical property inversion method based on petrophysical sensitive parameters proposed by the present invention screens and selects sensitive elastic parameters of reservoir physical properties, establishes the relationship between the selected sensitive elastic parameters and physical property parameters, determines the parameters of the inversion algorithm through the well logging inversion process, and extends it to seismic physical property inversion to obtain more accurate inversion results, so as to improve the accuracy of reservoir physical property inversion and provide support for reservoir quantitative evaluation.
[0022] (2) The reservoir physical property inversion method based on rock physical sensitive parameters proposed by the present invention is more accurate than the conventional elastic three-parameter reservoir physical property inversion result in terms of the inversion result of the preferred sensitive elastic parameters. At the same time, through inversion training, the optimal parameters of the inversion algorithm are determined, further improving the inversion accuracy.
[0023] Other features and advantages of the present invention will be described in detail in the following specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] By describing the exemplary embodiments of the present invention in more detail in conjunction with the drawings, the above and other objects, features, and advantages of the present invention will become more apparent.
[0025] Figure 1 It is a schematic flow chart of the reservoir physical property inversion method based on rock physical sensitive parameters proposed by the present invention.
[0026] Figure 2 It is a schematic flow chart of a specific implementation of the reservoir physical property inversion method based on rock physical sensitive parameters proposed by the present invention.
[0027] Figure 3 It is the logging data in a specific implementation of the reservoir physical property inversion method based on rock physical sensitive parameters proposed by the present invention.
[0028] Figure 4 It is a comparison chart of the conventional elastic three-parameter physical property inversion and the inversion result of the present invention in a specific implementation of the reservoir physical property inversion method based on rock physical sensitive parameters proposed by the present invention.
[0029] Figure 5 It is a comparison chart of the conventional elastic three-parameter physical property inversion and the inversion result of the present invention in other wells in a specific implementation of the reservoir physical property inversion method based on rock physical sensitive parameters proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0030] The preferred embodiments of the present invention will be described in more detail below. Although the following describes the preferred embodiments of the present invention, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.
[0031] The present invention provides a reservoir physical property inversion method based on rock physical sensitive parameters, as Figure 1 shown, including:
[0032] Select sensitive elastic parameters from basic elastic parameters and their derived elastic parameters;
[0033] Establish the petrophysical relationship between reservoir physical property parameters and the selected sensitive elastic parameters;
[0034] Based on well logging data, use the selected sensitive elastic parameters as inputs for inversion training to determine the optimal parameters of the inversion algorithm;
[0035] Use the inversion algorithm with optimal parameters to perform seismic inversion on the seismic data volume of the selected sensitive elastic parameters to obtain the seismic quantitative inversion results of reservoir physical properties.
[0036] In the present invention, by screening and selecting the reservoir physical property sensitive elastic parameters, establishing the relationship between the selected sensitive elastic parameters and the physical property parameters, determining the inversion algorithm parameters through the well logging inversion process, and extending it to seismic physical property inversion, more accurate inversion results are obtained to improve the accuracy of reservoir physical property inversion and provide support for reservoir quantitative evaluation.
[0037] According to the present invention, the basic elastic parameters and their derived elastic parameters are calculated from well logging curves;
[0038] The basic elastic parameters include Vp, Vs, ρ;
[0039] The derived elastic parameters include, but are not limited to, λ and μ, E, σ, K, M, λ / μ, IP, IS, Vp / Vs, and Vp 2 ;
[0040] Among them, Vp is the longitudinal wave velocity, Vs is the shear wave velocity, ρ is the density, λ and μ are the Lame coefficients, E is the Young's modulus, σ is the Poisson's ratio, K is the bulk modulus, M is the longitudinal wave modulus, IP is the longitudinal wave impedance, IS is the shear wave impedance, Vp / Vs is the ratio of longitudinal wave velocity to shear wave velocity, and Vp 2 is the square of the longitudinal wave velocity.
[0041] In the present invention, the derived elastic parameters also include the elastic parameters that actually need to be quantified during the parameter selection process.
[0042] According to the present invention, selecting the sensitive elastic parameters from the basic elastic parameters and their derived elastic parameters includes:
[0043] Calculate the correlation relationship between the reservoir physical property parameters and each elastic parameter in the basic elastic parameters and their derived elastic parameters respectively, and select the elastic parameter with a high degree of correlation as the sensitive elastic parameter.
[0044] Preferably, the correlation relationship is the correlation coefficient between the two.
[0045] According to the present invention, establishing the petrophysical relationship between reservoir physical property parameters and the selected sensitive elastic parameters includes:
[0046] Based on the relationships between the basic elastic parameters and reservoir physical properties parameters, as well as the relationships between the basic elastic parameters and sensitive elastic parameters, the petrophysical relationship between the reservoir physical properties parameters and the selected sensitive elastic parameters is calculated.
[0047] According to the present invention, based on well logging data, using the selected sensitive elastic parameters as inputs for inversion training, determining the optimal parameters of the inversion algorithm includes:
[0048] Using the selected sensitive elastic parameters as inputs for inversion to obtain an inversion result, and comparing the inversion result with the well logging physical property result in the well logging data to determine the optimal parameters of the inversion algorithm.
[0049] Preferably, before performing seismic inversion on the seismic data volume of the sensitive elastic parameters using the inversion algorithm with the optimal parameters, the following is also performed:
[0050] Based on the basic elastic parameter data volume obtained from prestack seismic inversion, calculating the seismic data volume of the selected sensitive elastic parameters using the basic elastic parameter data volume.
[0051] The inversion method proposed by the present invention, compared with the conventional elastic three-parameter reservoir physical property inversion result, the reservoir physical property inversion result of the selected sensitive elastic parameters is more accurate. At the same time, through inversion training, determining the optimal parameters of the inversion algorithm further improves the inversion accuracy.
[0052] The present invention will be described in more detail below through embodiments.
[0053] Embodiment 1:
[0054] As Figure 2 shown, this embodiment provides a reservoir physical property inversion method based on petrophysical sensitive parameters, and the specific steps are as follows:
[0055] The first step is to calculate the elastic three parameters (P-wave velocity, S-wave velocity, density) and their derived elastic factors from well logging curves;
[0056] As Figure 3 shown, the measured well logging data for petrophysical modeling includes: P-wave velocity (VP), S-wave velocity (VS), density (DEN), shale content (Vclay), sand content (VSAND), calcite content (VCA), porosity (PHI), saturation (SW);
[0057] In this embodiment, the derived elastic factors calculated from well logging curves are μ, λ, E, σ, K, M, λ / μ, IP, IS, VP / VS, VP 2 ;
[0058] The second step is to select the sensitive elastic parameters of the reservoir physical properties parameters;
[0059] Based on the elastic three parameters and their derived elastic factors, the correlation coefficients between reservoir physical property parameters and each elastic parameter are calculated respectively, and the elastic parameter with a high degree of correlation is taken as the sensitive elastic parameter; in this embodiment, porosity and mineral content need to be inverted. From the inversion results of the conventional elastic three parameters, the results of each mineral content are relatively accurate, but the porosity inversion result still needs to be improved. Therefore, the optimization of the porosity sensitive factor is carried out.
[0060] In this embodiment, the correlation coefficients between the elastic three parameters and the derived elastic factors obtained in the first step and porosity are calculated respectively; the absolute values of the correlation coefficients between the porosity of the logging interval and each elastic parameter are: VP is 0.4111, VS is 0.2481, DEN is 0.4223, μ is 0.3369, λ is 0.3791, E is 0.4341, σ is 0.2752, K is 0.4569, M is 0.4768, λ / μ is 0.2863, IP is 0.5375, IS is 0.4235, VP / VS is 0.2883, VP 2 is 0.4079; it can be seen that the three with the highest correlation coefficients are IP, K, and M.
[0061] In the third step, based on the relationships between the optimized sensitive elastic parameters and the P-wave velocity, S-wave velocity, and density, the petrophysical relationships between the physical properties and the optimized sensitive elastic parameters are established.
[0062] The relationships between IP, K, M and the elastic three parameters are:
[0063] ip = Vp·ρ
[0064] k = ρ(Vp 2 - 4Vs 2 / 3)
[0065] M = ρ·Vp 2
[0066] In the formula, Vp is the P-wave velocity, Vs is the S-wave velocity, and ρ is the density.
[0067] The relationships between the conventional elastic three parameters and the physical property parameters are: d = f(m), d is [VP, VS, DEN], m is [VSAND, VCA, PHI, SW], and f represents the functional relationship between the elastic three parameters d and the physical property parameters m.
[0068] Based on the relationships between IP, K, M and the elastic three parameters, the relationships between IP, K, M and the physical property parameters m can be deduced, expressed as d1 = g(m), d1 is [IP, K, M], and g represents the functional relationship between the optimized elastic parameters d1 and the physical property parameters m.
[0069] Step 4: Optimize the parameters of the inversion algorithm based on well logging data to determine the optimal parameters for generalization to seismic inversion;
[0070] In this embodiment, the EMGM inversion algorithm under the Bayesian framework is selected as the inversion algorithm. The optimal algorithm parameters are determined by comparing the inversion results of the conventional elastic three parameters in the well with the actual well logging physical property results, and these algorithm parameters are used for the inversion of the selected sensitive elastic parameters. The difference between the two is as follows: The conventional elastic three parameter well logging inversion uses VP, VS, and DEN as the inversion inputs, and trains the relationship between the well logging physical property curves and the conventional elastic three parameters based on the EMGM algorithm; The inversion of the selected sensitive elastic parameters uses IP, K, and M as the inversion inputs, and trains the relationship between the well logging physical property curves and the conventional elastic three parameters based on the EMGM algorithm;
[0071] The inversion results of the two methods are as Figure 4 shown. Figure 4 (a) shows the inversion results of the conventional elastic three parameters in the well, Figure 4 (b) shows the inversion results of the selected sensitive elastic parameters. The solid line is the measured well logging value, and the dashed line is the inversion result. Figure 4 In (a), the correlation coefficients between the inversion results of PHI, VQU, Vcaqu, and SW and the measured results are 0.6071, 0.9581, 0.9702, and 0.4438 respectively; Figure 4 In (b), the correlation coefficients between the inversion results of PHI, VQU, Vcaqu, and SW and the measured results are 0.6679, 0.9693, 0.9789, and 0.6403 respectively; It can be seen that the inversion results of the sand mineral content VQU and the brittle mineral content Vcaqu of the two methods are quite similar, but the method of the selected sensitive elastic parameters proposed in this invention is better than the inversion results of the conventional elastic three parameters in the well for the porosity inversion result. In the depth range of [3400m - 3550m], the method of this invention is significantly more consistent with the measured curve;
[0072] In addition, in order to illustrate that the method can be generalized to seismic, the inversion results of other wells in the work area are tested; The input is the elastic parameters measured by well logging. Since there is no saturation curve for the other wells selected in the work area, and the change in saturation has little impact on the seismic elastic parameters when the porosity is small, the water saturation of the other wells selected in the work area is set to 100%; The inversion results of the two methods for the other wells in the work area are as Figure 5 shown. Figure 5 (a) shows the inversion results of the conventional elastic three parameters in the well, Figure 5 (b) shows the inversion results of the selected sensitive elastic parameters. The solid line is the measured well logging value, and the dashed line is the inversion result; Figure 5 In (a), the correlation coefficients between the inversion results of PHI, VQU, and Vcaqu and the measured results are 0.6892, 0.8571, and 0.9173 respectively; Figure 5In (b), the correlation coefficients between the inversion results of PHI, VQU, and Vcaqu and the measured results are 0.6920, 0.8582, and 0.8827, respectively. It can be seen that the inversion results of the sand mineral content VQU and the brittle mineral content Vcaqu by the two methods are quite similar. The porosity inversion result by the preferred sensitive elastic parameter method proposed in this invention is more consistent with the measured porosity, indicating that the method can be extended to seismic inversion.
[0073] In the fifth step, based on the elastic three-parameter data volume obtained from seismic pre-stack inversion, calculate the seismic data volume of the preferred sensitive elastic parameters, and extend the well logging inversion process in the fourth step to seismic inversion to obtain the seismic quantitative inversion result of reservoir physical properties.
[0074] Example 2:
[0075] As Figure 1 shown, this embodiment provides a reservoir physical property inversion method based on rock physics sensitive parameters, including:
[0076] Select sensitive elastic parameters from basic elastic parameters and their derived elastic parameters;
[0077] Establish the rock physics relationship between reservoir physical property parameters and the selected sensitive elastic parameters;
[0078] Based on well logging data, use the selected sensitive elastic parameters as inputs for inversion training to determine the optimal parameters of the inversion algorithm;
[0079] Use the inversion algorithm with optimal parameters to perform seismic inversion on the seismic data volume of the selected sensitive elastic parameters to obtain the seismic quantitative inversion result of reservoir physical properties;
[0080] The basic elastic parameters and their derived elastic parameters are calculated from well logging curves;
[0081] The basic elastic parameters include Vp, Vs, and ρ;
[0082] The derived elastic parameters include λ and μ, E, σ, K, M, λ / μ, IP, IS, Vp / Vs, and Vp 2 ;
[0083] Among them, Vp is the longitudinal wave velocity, Vs is the shear wave velocity, ρ is the density, λ and μ are the Lame coefficients, E is the Young's modulus, σ is the Poisson's ratio, K is the bulk modulus, M is the longitudinal wave modulus, IP is the longitudinal wave impedance, IS is the shear wave impedance, Vp / Vs is the ratio of longitudinal wave velocity to shear wave velocity, and Vp 2 is the square of the longitudinal wave velocity;
[0084] Selecting sensitive elastic parameters from basic elastic parameters and their derived elastic parameters includes:
[0085] Calculate the correlation between each elastic parameter of reservoir physical properties parameters, basic elastic parameters and their derived elastic parameters respectively, and select the elastic parameter with a high degree of correlation as the sensitive elastic parameter;
[0086] The correlation is the correlation coefficient between the two;
[0087] Establishing the petrophysical relationship between reservoir physical properties parameters and the selected sensitive elastic parameters includes:
[0088] Based on the relationship between basic elastic parameters and reservoir physical properties parameters, and the relationship between basic elastic parameters and sensitive elastic parameters, calculate the petrophysical relationship between reservoir physical properties parameters and the selected sensitive elastic parameters;
[0089] Based on well logging data, use the selected sensitive elastic parameters as inputs for inversion training to determine the optimal parameters of the inversion algorithm, including:
[0090] Use the selected sensitive elastic parameters as inputs for inversion to obtain the inversion result, and compare the inversion result with the well logging physical property result in the well logging data to determine the optimal parameters of the inversion algorithm;
[0091] Before performing seismic inversion on the seismic data volume of sensitive elastic parameters using the inversion algorithm with optimal parameters, the following is also carried out:
[0092] Based on the basic elastic parameter data volume obtained from seismic pre-stack inversion, calculate the seismic data volume of the selected sensitive elastic parameters using the basic elastic parameter data volume.
[0093] Example 3:
[0094] This embodiment provides a reservoir physical property inversion device based on petrophysical sensitive parameters, including:
[0095] A screening module for selecting sensitive elastic parameters from basic elastic parameters and their derived elastic parameters;
[0096] A relationship establishment module for establishing the petrophysical relationship between reservoir physical property parameters and the selected sensitive elastic parameters;
[0097] An optimal parameter determination module for performing inversion training based on well logging data using the selected sensitive elastic parameters as inputs to determine the optimal parameters of the inversion algorithm;
[0098] A seismic inversion module for performing seismic inversion on the seismic data volume of the selected sensitive elastic parameters using the inversion algorithm with optimal parameters to obtain the seismic quantitative inversion result of reservoir physical properties;
[0099] The basic elastic parameters and their derived elastic parameters are calculated from well logging curves;
[0100] The basic elastic parameters include Vp, Vs, and ρ;
[0101] The derived elastic parameters include λ and μ, E, σ, K, M, λ / μ, IP, IS, Vp / Vs, and Vp² 2 ;
[0102] Among them, Vp is the longitudinal wave velocity, Vs is the shear wave velocity, ρ is the density, λ and μ are the Lamé coefficients, E is the Young's modulus, σ is the Poisson's ratio, K is the bulk modulus, M is the longitudinal wave modulus, IP is the longitudinal wave impedance, IS is the shear wave impedance, Vp / Vs is the ratio of longitudinal to shear wave velocities, and Vp² 2 is the square of the longitudinal wave velocity;
[0103] The sensitive elastic parameters selected from the basic elastic parameters and their derived elastic parameters include:
[0104] Calculate the correlation relationship between each elastic parameter in the reservoir physical property parameters and the basic elastic parameters and their derived elastic parameters respectively, and select the elastic parameter with a high degree of correlation as the sensitive elastic parameter;
[0105] The correlation relationship is the correlation coefficient between the two;
[0106] Establishing the petrophysical relationship between the reservoir physical property parameters and the selected sensitive elastic parameters includes:
[0107] Based on the relationship between the basic elastic parameters and the reservoir physical property parameters, and the relationship between the basic elastic parameters and the sensitive elastic parameters, calculate the petrophysical relationship between the reservoir physical property parameters and the selected sensitive elastic parameters;
[0108] Based on well logging data, using the selected sensitive elastic parameters as inputs for inversion training, determining the optimal parameters of the inversion algorithm includes:
[0109] Using the selected sensitive elastic parameters as inputs for inversion to obtain the inversion result, and comparing the inversion result with the well logging physical property result in the well logging data to determine the optimal parameters of the inversion algorithm;
[0110] Before performing seismic inversion on the seismic data volume of the sensitive elastic parameters using the inversion algorithm with the optimal parameters, the following is also carried out:
[0111] Based on the basic elastic parameter data volume obtained from seismic pre-stack inversion, calculate the seismic data volume of the selected sensitive elastic parameters using the basic elastic parameter data volume.
[0112] Example 4:
[0113] An embodiment of the present invention provides an electronic device including a memory and a processor,
[0114] The memory stores executable instructions;
[0115] A processor, which runs executable instructions in a memory to implement a method for reservoir physical property inversion based on rock physics sensitive parameters.
[0116] The memory is used to store non - transient computer - readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer - readable storage media, such as volatile memory and / or non - volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory, etc. The non - volatile memory may, for example, include read - only memory (ROM), hard disk, flash memory, etc.
[0117] The processor may be a central processing unit (CPU) or other forms of processing units with data - processing capabilities and / or instruction - execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present invention, the processor is used to run the computer - readable instructions stored in the memory.
[0118] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user experience effects, this embodiment may also include well - known structures such as communication buses, interfaces, etc., and these well - known structures should also be included in the protection scope of the present invention.
[0119] For a detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.
[0120] Embodiment Five:
[0121] An embodiment of the present invention provides a computer - readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements a method for reservoir physical property inversion based on rock physics sensitive parameters.
[0122] According to the computer - readable storage medium of the embodiment of the present invention, non - transient computer - readable instructions are stored thereon. When the non - transient computer - readable instructions are run by a processor, all or part of the steps of the methods of the foregoing embodiments of the present invention are executed.
[0123] The above - mentioned computer - readable storage medium includes but is not limited to: optical storage media (such as CD - ROM and DVD), magneto - optical storage media (such as MO), magnetic storage media (such as magnetic tapes or external hard drives), media with built - in rewritable non - volatile memory (such as memory cards), and media with built - in ROM (such as ROM cartridges).
[0124] The reservoir physical property inversion method based on rock physics sensitive parameters proposed by the embodiments of the present invention screens and optimizes the elastic parameters sensitive to reservoir physical properties, establishes the relationship between the selected sensitive elastic parameters and physical property parameters, determines the inversion algorithm parameters through the well logging inversion process, and extends them to seismic physical property inversion to obtain more accurate inversion results, so as to improve the accuracy of reservoir physical property inversion and provide support for reservoir quantitative evaluation.
[0125] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims
1. A reservoir property inversion method based on rock physical sensitive parameters, characterized in that: include: Selecting sensitive elastic parameters from basic elastic parameters and their derived elastic parameters; Establishing a rock physical relationship between reservoir physical property parameters and the preferred sensitive elastic parameters; Performing inversion training based on well logging data using the selected sensitive elastic parameters as input to determine optimal parameters of the inversion algorithm; The seismic inversion algorithm with the optimal parameters is used to perform seismic inversion on the seismic data volume with the selected sensitive elastic parameters to obtain the seismic quantitative inversion results of reservoir physical properties.
2. The inversion method according to claim 1, characterized in that: The basic elastic parameters and the elastic parameters derived therefrom are calculated through well logging curves; The basic elastic parameters include Vp, Vs, and ρ; The derived elastic parameters include λ and μ, E, σ, K, M, λ / μ, IP, IS, Vp / Vs, and Vp 2 and the elastic parameters that actually need to be quantified; Where Vp is the longitudinal wave velocity, Vs is the shear wave velocity, ρ is the density, λ and μ are the Lame coefficients, E is the Young's modulus, σ is the Poisson's ratio, K is the bulk modulus, M is the longitudinal wave modulus, IP is the longitudinal wave impedance, IS is the shear wave impedance, Vp / Vs is the ratio of longitudinal and shear wave velocities, Vp 2 is the square of the longitudinal wave velocity.
3. The inversion method according to claim 1, characterized in that: Sensitive elastic parameters selected from basic elastic parameters and their derived elastic parameters include: The correlation between reservoir physical parameters and each of the basic elastic parameters and their derived elastic parameters is calculated respectively, and the elastic parameter with the highest correlation is taken as the sensitive elastic parameter.
4. The inversion method according to claim 3, characterized in that: The correlation relationship is the correlation coefficient between the two.
5. The inversion method according to claim 1, characterized in that: Establishing a rock physical relationship between reservoir physical parameters and the preferred sensitive elastic parameters includes: Based on the relationship between the basic elastic parameters and the reservoir physical parameters, and the relationship between the basic elastic parameters and the sensitive elastic parameters, the rock physical relationship between the reservoir physical parameters and the preferred sensitive elastic parameters is calculated.
6. The inversion method according to claim 1, characterized in that: Based on the well logging data, the preferred sensitive elastic parameters are used as input to perform inversion training to determine the optimal parameters of the inversion algorithm, including: The preferred sensitive elastic parameters are used as input to perform inversion to obtain inversion results, and the inversion results are compared with the well logging physical property results in the well logging data to determine the optimal parameters of the inversion algorithm.
7. The inversion method according to claim 1, characterized in that: Before performing seismic inversion on a seismic data volume with sensitive elastic parameters using the inversion algorithm with optimal parameters, the following steps are also performed: Based on the basic elastic parameter data volume obtained by seismic pre-stack inversion, the basic elastic parameter data volume is used to calculate the seismic data volume of the optimal sensitive elastic parameters.
8. A sensitive factor construction and favorable reservoir identification device, characterized in that: include: A screening module, for selecting sensitive elastic parameters from basic elastic parameters and elastic parameters derived therefrom; A relationship establishment module, used for establishing a petrophysical relationship between reservoir physical parameters and the preferred sensitive elastic parameters; An optimal parameter determination module is used to perform inversion training based on well logging data using the preferred sensitive elastic parameters as input to determine the optimal parameters of the inversion algorithm; The seismic inversion module is used to perform seismic inversion on the seismic data volume of the selected sensitive elastic parameters using the inversion algorithm with the optimal parameters to obtain the seismic quantitative inversion results of the reservoir physical properties.
9. An electronic device, characterized in that: The electronic device comprises: a memory storing executable instructions; A processor that runs the executable instructions in the memory to implement the reservoir property inversion method based on rock physical sensitive parameters according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the reservoir property inversion method based on rock physical sensitive parameters according to any one of claims 1 to 7.