Electric shock combined gas-bearing prediction method, electronic equipment, storage medium and device

Abstract

The invention discloses an electric shock combined gas-bearing prediction method, electronic equipment, a storage medium and a device. The method comprises the following steps: constructing a rock physical model based on a rock core data test result, and establishing a relationship among an elastic parameter, an electrical parameter, mineral content, porosity and gas saturation; carrying out pre-stack inversion based on the logging data and the seismic data to obtain elastic parameter data; acquiring electrical parameter data based on the electrical prospecting data; bayesian inversion is carried out based on the rock physical model, the elastic parameter data and the electrical parameter data, and gas-bearing prediction is completed. According to the method, the electrical method and the seismic multi-domain data are combined as input, compared with gas-bearing prediction by independently using the seismic data, the multiplicity of prediction results is obviously reduced, and the gas-bearing prediction precision is improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of oil exploration, and more particularly relates to an electric shock combined gas-bearing property prediction method, an electronic device, a storage medium and an apparatus. BACKGROUND

[0002] At present, the most important gas-bearing property prediction is to obtain elastic parameters or attenuation information from seismic data to evaluate the reservoir gas-bearing property. The problem is that the elastic parameters are limited by the reservoir lithology and physical properties more than the gas-bearing property, so the influence of other factors of the reservoir needs to be eliminated when the elastic parameters are used for gas-bearing property prediction, and the precision is not high. Although the attenuation attribute has a good response to the reservoir gas-bearing property, it is also affected by the reservoir physical property to some extent, and many factors can cause attenuation in the process of seismic wave propagation, so the use of attenuation attribute for gas-bearing property prediction has multiple solutions.

[0003] The information disclosed in the background section of this application is only intended to deepen the understanding of the general background of the present application, and should not be regarded as acknowledging or implying in any form that this information constitutes prior art known to those skilled in the art. SUMMARY

[0004] The purpose of the present application is to provide an electric shock combined gas-bearing property prediction method, an electronic device, a storage medium and an apparatus, which realizes gas-bearing property prediction based on electric method data and seismic data, reduces the multiple solutions of prediction results, and improves the prediction accuracy of gas-bearing property.

[0005] To achieve the above purpose, the present application provides an electric shock combined gas-bearing property prediction method, an electronic device, a storage medium and an apparatus.

[0006] According to the first aspect of the present application, an electric shock combined gas-bearing property prediction method is provided, comprising:

[0007] A rock physics model is constructed based on the test results of core data, and the relationship between elastic parameters, electrical parameters and mineral content, porosity and gas saturation is established;

[0008] Pre-stack inversion is carried out based on logging data and seismic data to obtain elastic parameter data;

[0009] Electrical parameter data are obtained based on electric exploration data;

[0010] Bayesian inversion is carried out based on the rock physics model, elastic parameter data and electrical parameter data to complete gas-bearing property prediction.

[0011] Optionally, the rock physics model is:

[0012] (mineral content, porosity, gas saturation) = fx(elastic parameter, electrical parameter).

[0013] Optionally, the electrical parameter comprises:

[0014] resistivity and polarization rate.

[0015] Optionally, the elastic parameter comprises:

[0016] longitudinal wave velocity, transverse wave velocity and density.

[0017] Optionally, based on the rock physical model, the elastic parameter data and the electrical parameter data are input, and the gas saturation is obtained through Bayesian inversion.

[0018] According to a second aspect of the present application, an electrical and seismic combined gas-bearing property prediction device is provided, comprising:

[0019] a construction module configured to construct a rock physical model based on core data test results, and establish a relationship between elastic parameters, electrical parameters and mineral content, porosity and gas saturation;

[0020] a pre-stack inversion module configured to perform pre-stack inversion based on logging data and seismic data, and obtain elastic parameter data;

[0021] an acquisition module configured to acquire electrical parameter data based on electrical prospecting data;

[0022] a prediction module configured to perform Bayesian inversion based on the rock physical model, the elastic parameter data and the electrical parameter data, and complete gas-bearing property prediction.

[0023] Optionally, the rock physical model is:

[0024] (mineral content, porosity, gas saturation) = fx(elastic parameter, electrical parameter).

[0025] Optionally, the electrical parameter comprises:

[0026] resistivity and polarization rate.

[0027] According to a third aspect of the present application, an electronic device is provided, comprising:

[0028] at least one processor; and

[0029] a memory connected in communication with the at least one processor; wherein

[0030] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for predicting gas-bearing property of an electroseismic combination according to any one of the first aspect.

[0031] According to a fourth aspect of the present application, a non-transitory computer readable storage medium is provided, characterized in that the non-transitory computer readable storage medium stores computer instructions for causing a computer to perform the method for predicting gas-bearing property of an electroseismic combination according to any one of the first aspect.

[0032] The present application has the beneficial effect that the present application establishes the relationship between the elastic parameters, the electrical parameters and the gas saturation through the rock physics model, and then obtains the final gas saturation prediction result based on the Bayesian algorithm and taking the elastic parameters of pre-stack inversion and the electrical parameters of electrical prospecting as inputs. The present application combines the electrical method and the seismic multi-domain data as inputs, and the prediction result has a significantly reduced multi-solution property compared with the prediction of gas-bearing property using the seismic data alone, thereby improving the prediction accuracy of the gas-bearing property.

[0033] The system of the present application has other characteristics and advantages that will be apparent from and / or set forth in the accompanying drawings and subsequent detailed description, which together serve to explain the specific principles of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0034] The above and other objects, features and advantages of the present application will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings in which like reference characters refer to like parts throughout the figures, and wherein:

[0035] Figure 1 A flow chart showing the steps of a method for predicting gas-bearing property of an electroseismic combination according to the present application is shown.

[0036] Figure 2 A flow chart showing the steps of a method for predicting gas-bearing property of an electroseismic combination according to embodiment two of the present application is shown. DETAILED DESCRIPTION

[0037] The present application will be described in more detail by referring to the attached drawings. Although preferred embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present application will be more thoroughly and completely conveyed to those skilled in the art, and so that the scope of the present application will be fully conveyed to those skilled in the art.

[0038] As Figure 1As shown, according to the gas-bearing property prediction method combined with electric shock in the application, the method comprises the following steps:

[0039] A rock physical model is constructed based on the core data test results, and the relationship between the elastic parameters, the electrical parameters and the mineral content, the porosity and the gas saturation is established;

[0040] The elastic parameter data is obtained through pre-stack inversion based on the logging data and the seismic data;

[0041] The electrical parameter data is obtained based on the electric exploration data;

[0042] The gas-bearing property prediction is completed through Bayesian inversion based on the rock physical model, the elastic parameter data and the electrical parameter data.

[0043] Specifically, the logging data, the seismic data, the resistivity and the polarization rate data obtained through electric exploration inversion and the core of the target layer in the actual work area are collected; the rock physical model containing the resistivity and the polarization rate suitable for the work area is established based on the core data test results, (mineral content, porosity, gas saturation) = fx (elastic parameter, resistivity, polarization rate), and the relationship between the elastic parameter, the resistivity, the polarization rate and the mineral content, the porosity and the gas saturation is established; the conventional rock physical model is (mineral content, porosity, gas saturation) = fx (elastic parameter), and the relationship between the elastic parameter and the mineral content, the porosity and the gas saturation is established; and the step is the key of the Bayesian physical parameter inversion. The elastic parameter is obtained through pre-stack inversion based on the collected logging data and the seismic data; and the gas saturation is obtained through Bayesian inversion based on the rock physical model, the elastic parameter obtained through seismic inversion and the resistivity and the polarization rate data obtained through electric exploration as inputs. The application learns from the idea of the Bayesian physical parameter inversion; the conventional Bayesian physical parameter inversion firstly constructs the relationship between the elastic parameter (i.e. the compressional wave velocity, the shear wave velocity and the density) and the reservoir parameters (i.e. the shale content, the porosity and the saturation) through rock physical modeling, and then obtains the reservoir parameters (i.e. the shale content, the porosity and the saturation) through Bayesian algorithm with the compressional wave velocity, the shear wave velocity and the density obtained through pre-stack inversion as inputs. The application constructs the relationship between the elastic parameter (i.e. the compressional wave velocity, the shear wave velocity and the density), the electrical parameter and the reservoir parameters through the rock physical model containing the electrical parameter (i.e. the resistivity and the polarization rate) based on the core experiment data, and then obtains the reservoir parameters (i.e. the shale content, the porosity and the gas saturation) through Bayesian algorithm with the compressional wave velocity, the shear wave velocity and the density obtained through pre-stack inversion and the electrical parameter (i.e. the resistivity and the polarization rate) obtained through electric exploration as inputs. The application combines the electrical method and the seismic multi-domain data as inputs, and the prediction result has obviously reduced ambiguity and improved the prediction accuracy of the gas-bearing property compared with the prediction of the gas-bearing property using the seismic data alone.

[0044] In one example, the petrophysical model is:

[0045] (mineral content, porosity, gas saturation) = fx(elastic parameter, electrical parameter).

[0046] In one example, the electrical parameter includes:

[0047] resistivity and polarizability.

[0048] In one example, the elastic parameter includes:

[0049] P-wave velocity, S-wave velocity and density.

[0050] In one example, based on the petrophysical model, the elastic parameter data and the electrical parameter data are input, and the gas saturation is obtained through Bayesian inversion.

[0051] The application will be further described below in conjunction with the drawings and specific embodiments, but not as a limitation of the application. It should be noted that the embodiments in the application and the features in the embodiments can be combined with each other without conflict.

[0052] Embodiment one

[0053] The embodiment provides a gas-bearing property prediction method combined with electricity and seismic, which comprises:

[0054] A petrophysical model is constructed based on core data test results, and a relationship between elastic parameters, electrical parameters and mineral content, porosity and gas saturation is established.

[0055] The petrophysical model is:

[0056] (mineral content, porosity, gas saturation) = fx(elastic parameter, electrical parameter).

[0057] The electrical parameter includes resistivity and polarizability.

[0058] Pre-stack inversion is carried out based on logging data and seismic data to obtain elastic parameter data; the elastic parameter includes P-wave velocity, S-wave velocity and density.

[0059] Electrical parameter data are obtained based on electrical prospecting data.

[0060] Bayesian inversion is carried out based on the petrophysical model, the elastic parameter data and the electrical parameter data to complete gas-bearing property prediction, that is, based on the petrophysical model, the elastic parameter data and the electrical parameter data are input, and the gas saturation is obtained through Bayesian inversion.

[0061] Embodiment two

[0062] As Figure 2As shown, the embodiment provides a gas-bearing prediction method combined with electric shock, comprising:

[0063] Collecting logging data, seismic data, resistivity and polarization rate data obtained by inversion of electric prospecting, and cores of target layers in actual work area.

[0064] Based on the test results of core data, a rock physics model suitable for the work area is established, which must include electrical parameters (resistivity and polarization rate). The reliability of the rock physics model is verified by comparing the logging curves of longitudinal and transverse wave velocities and resistivity calculated according to the rock physics model based on the logging data of shale content, porosity and gas saturation data. The rock physics model is: (mineral content, porosity, gas saturation) = fx (elastic parameters, resistivity, polarization rate).

[0065] According to the collected logging data and seismic data, pre-stack inversion is carried out to obtain the longitudinal and transverse wave velocities and density of the elastic parameters.

[0066] Based on the rock physics model, the elastic parameter longitudinal and transverse wave velocities and density obtained by seismic inversion and the resistivity and polarization rate data obtained by electric prospecting are input, and the gas saturation is obtained by Bayesian inversion.

[0067] The embodiment combines the electric prospecting data and the seismic exploration data through the rock physics model established by the core to obtain the relationship between the gas saturation and the rock physics model, and then carries out the gas-bearing prediction through the Bayesian inversion.

[0068] Embodiment three

[0069] The embodiment provides a gas-bearing prediction device combined with electric shock, comprising:

[0070] The construction module is configured to construct a rock physics model based on the test results of core data, and establish a relationship between elastic parameters, electrical parameters and mineral content, porosity and gas saturation. The rock physics model is: (mineral content, porosity, gas saturation) = fx (elastic parameters, electrical parameters). The electrical parameters include resistivity and polarization rate.

[0071] The pre-stack inversion module is configured to carry out pre-stack inversion based on the logging data and the seismic data to obtain elastic parameter data. The elastic parameters include longitudinal wave velocity, transverse wave velocity and density.

[0072] The acquisition module is configured to acquire electrical parameter data based on electric prospecting data.

[0073] The prediction module is configured to carry out Bayesian inversion based on the rock physics model, the elastic parameter data and the electrical parameter data to complete the gas-bearing prediction. That is, based on the rock physics model, the elastic parameter data and the electrical parameter data are input, and the gas saturation is obtained by Bayesian inversion.

[0074] Embodiment Four

[0075] The embodiments of the present disclosure also provide an electronic device, which comprises:

[0076] at least one processor; and

[0077] a memory connected with the at least one processor; wherein

[0078] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for predicting gas content combined with electroshock in the embodiment one.

[0079] The electronic device according to the embodiments of the present disclosure comprises a memory and a processor, the memory is used to store non-transitory computer readable instructions. Specifically, the memory can comprise one or more computer program products, which can comprise 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), cache memory and the like. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory and the like.

[0080] The processor can be a central processing unit (CPU) or other forms of processing units with data processing and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions. In one embodiment of the present disclosure, the processor is used to run the computer readable instructions stored in the memory.

[0081] Those skilled in the art shall understand that, in order to solve the technical problem of how to obtain a good user experience effect, the embodiments can also comprise well-known structures such as communication bus, interface and the like, which shall also be included in the protection scope of the present disclosure.

[0082] Detailed description of the embodiments can refer to the corresponding description in the foregoing embodiments, which will not be repeated here.

[0083] Embodiment Five

[0084] The embodiments of the present disclosure provide a non-transitory computer readable storage medium, which stores computer instructions for causing a computer to execute the method for predicting gas content combined with electroshock in the embodiment one.

[0085] A computer readable storage medium according to embodiments of the present disclosure has non-transitory computer readable instructions stored thereon. When the non-transitory computer readable instructions are run by a processor, all or part of the steps of the method of the embodiments of the present disclosure described above are performed.

[0086] The computer readable storage medium described above includes, but is not limited to, an optical storage medium (for example, a CD-ROM and a DVD), a magneto-optical storage medium (for example, an MO), a magnetic storage medium (for example, a magnetic tape or a moving hard disk), a medium having a built-in rewritable nonvolatile memory (for example, a memory card), and a medium having a built-in ROM (for example, a ROM cartridge).

[0087] The embodiments of the present application have been described above with the foregoing description being exemplary rather than limiting in nature. Since numerous modifications and changes will occur to those skilled in the art, we desire it to be understood that only the illustrative embodiments have been described and that no limitation on the scope of the application is intended insofar as it does not depart from the scope and spirit of the described embodiments.

Claims

1. A method for predicting gas content using a combination of electro-seismic and magnetic fields, characterized in that, include: A rock physics model was constructed based on the test results of core data, and the relationship between elastic parameters, electrical parameters and mineral content, porosity, and gas saturation was established. Pre-stack inversion was performed based on well logging data and seismic data to obtain elastic parameter data; Electrical parameter data are obtained based on electrical exploration data; Based on the aforementioned rock physics model, elastic parameter data, and electrical parameter data, Bayesian inversion is performed to complete the gas-bearing prediction.

2. The electro-seismic combined gas-bearing prediction method according to claim 1, characterized in that, The rock physics model is as follows: (Mineral content, porosity, gas saturation) = fx(elastic parameters, electrical parameters).

3. The electro-seismic combined gas-bearing prediction method according to claim 1, characterized in that, The electrical parameters include: Resistivity and polarizability.

4. The electro-seismic combined gas-bearing prediction method according to claim 1, characterized in that, The elastic parameters include: Longitudinal wave velocity, transverse wave velocity, and density.

5. The electro-seismic combined gas-bearing prediction method according to claim 1, characterized in that, Based on the rock physics model, and using the elastic and electrical parameter data as inputs, the gas saturation is obtained through Bayesian inversion.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, which, when executed, enables the at least one processor to perform the electro-seismic combined gas-bearing prediction method according to any one of claims 1-5.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to perform the electro-seismic combined gas-bearing prediction method according to any one of claims 1-5.

8. A gas-bearing prediction device combining electro-seismic analysis, characterized in that, include: The module is used to build a rock physics model based on core data test results and establish the relationship between elastic parameters, electrical parameters and mineral content, porosity and gas saturation. The pre-stack inversion module is used to perform pre-stack inversion based on well logging data and seismic data to obtain elastic parameter data. The acquisition module is used to acquire electrical parameter data based on electrical exploration data; The prediction module is used to perform Bayesian inversion based on the rock physics model, elastic parameter data, and electrical parameter data to complete the gas content prediction.

9. The electro-seismic combined gas-bearing prediction device according to claim 8, characterized in that, The rock physics model is as follows: (Mineral content, porosity, gas saturation) = fx(elastic parameters, electrical parameters).

10. The electro-seismic combined gas-bearing prediction device according to claim 8, characterized in that, The electrical parameters include: Resistivity and polarizability.

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