CN-122014213-A - Method, device, equipment and storage medium for determining rock component logging skeleton value

CN122014213ACN 122014213 ACN122014213 ACN 122014213ACN-122014213-A

Abstract

The application provides a method, a device, equipment and a storage medium for determining a rock component logging skeleton value, wherein the method comprises the steps of determining residual logging response parameters of components to be solved in samples with different depths according to logging response parameters with different depths, influence parameters of known minerals in the samples with different depths and influence parameters of known fluids; fitting a plurality of groups of to-be-solved component data with different depths, and determining the logging skeleton value of the to-be-solved component according to the fitting result, wherein the to-be-solved component data comprises the residual logging response parameters and the relative volume content of the to-be-solved component in the sample. According to the application, aiming at the reservoir with complex rock skeleton components and large content variation, the residual logging response parameters of the components to be solved are extracted by using the stripping values, and the determination of the logging skeleton values of the components to be solved is realized through the fitting of the residual logging response parameters and the relative volume content, so that the method provides a favorable support for accurate evaluation of the reservoir parameters, description of oil and gas reservoirs and geological deepening research of the oil and gas reservoirs.

Inventors

LIU ZHIYUAN
LI HAO
ZHANG JUN
MINAMI TAKASHI
YAN LINHUI

Assignees

中国石油化工股份有限公司
中国石油化工股份有限公司石油勘探开发研究院

Dates

Publication Date: 20260512
Application Date: 20241112

Claims (13)

1. A method for determining a skeleton value of a logging of a rock composition, comprising: determining the residual logging response parameters of the components to be solved in the samples with different depths according to the logging response parameters with different depths, the influence parameters of the known minerals in the samples with different depths and the influence parameters of the known fluids in the samples with different depths; Fitting a plurality of groups of to-be-solved component data with different depths, and determining the logging skeleton value of the to-be-solved component according to the fitting result, wherein the to-be-solved component data comprises the residual logging response parameters and the relative volume content of the to-be-solved component in the sample.
2. The method of claim 1, wherein prior to determining the remaining logging response parameters for the component to be solved in the sample at the different depths based on the logging response parameters at the different depths, the influence parameters for the known minerals in the sample at the different depths, and the influence parameters for the known fluids in the sample at the different depths, further comprising: Logging response parameters of different depths are obtained through logging; by analyzing the samples with different depths, the relative volume content of the known minerals, the relative volume content of the known fluid and the relative volume content of the components to be solved in the samples with different depths are obtained.
3. The method of claim 2, wherein determining the remaining logging response parameters for the component to be solved in the sample at the different depths based on the logging response parameters at the different depths, the influence parameters for the known minerals in the sample at the different depths, and the influence parameters for the known fluids in the sample at the different depths comprises: according to the formula M x ＝M-∑M mai V i -∑F j V j , determining the residual logging response parameters of the components to be solved in the samples with different depths, wherein M x is the residual logging response parameters of the components to be solved, M is the logging response parameters, M mai is the logging skeleton value of the ith known mineral, V i is the relative volume content of the ith known mineral, F j is the logging skeleton value of the jth known fluid, and V j is the relative volume content of the jth known fluid.
4. The method of claim 1, wherein fitting the plurality of sets of data of the to-be-solved components at different depths, and determining the logging skeleton value of the to-be-solved component according to the fitting result comprises: And carrying out forced zero crossing point fitting on a plurality of groups of to-be-solved component data with different depths, and taking the residual logging response parameters corresponding to 100% of the relative volume content in the fitting result as the logging skeleton value of the to-be-solved component.
5. The method according to claim 4, wherein the performing forced zero crossing fitting on the multiple sets of to-be-solved component data with different depths, taking the remaining logging response parameters corresponding to 100% of the relative volume content in the fitting result as the logging skeleton value of the to-be-solved component, includes: Determining the logging skeleton value of the component to be solved according to a formula M ma ＝∑M xi /∑V xi , wherein M ma is the logging skeleton value of the component to be solved, M xi is the residual logging response parameter of the ith component to be solved, and Sigma V xi is the relative volume content of the ith component to be solved.
6. A device for determining a value of a logging skeleton of a rock composition, comprising: The residual logging response parameter determining module is used for determining residual logging response parameters of components to be solved in the samples with different depths according to the logging response parameters with different depths, the influence parameters of known minerals in the samples with different depths and the influence parameters of known fluids in the samples with different depths; the system comprises a logging framework value determining module for the components to be solved, a logging framework value determining module for determining logging framework values of the components to be solved according to fitting results, wherein the logging framework values of the components to be solved are used for fitting multiple groups of data of the components to be solved of different depths, and the data of the components to be solved comprise residual logging response parameters and relative volume contents of the components to be solved in a sample.
7. The apparatus as recited in claim 6, further comprising: the logging module is used for obtaining logging response parameters of different depths through logging; And the sample analysis module is used for obtaining the relative volume content of the known minerals, the relative volume content of the known fluid and the relative volume content of the components to be solved in the samples with different depths by analyzing the samples with different depths.
8. The apparatus of claim 7, wherein the remaining logging response parameter determination module is configured to: according to the formula M x ＝M-∑M mai V i -∑F j V j , determining the residual logging response parameters of the components to be solved in the samples with different depths, wherein M x is the residual logging response parameters of the components to be solved, M is the logging response parameters, M mai is the logging skeleton value of the ith known mineral, V i is the relative volume content of the ith known mineral, F j is the logging skeleton value of the jth known fluid, and V j is the relative volume content of the jth known fluid.
9. The apparatus of claim 6, wherein the logging framework value determination module of the component to be solved is specifically configured to: And carrying out forced zero crossing point fitting on a plurality of groups of to-be-solved component data with different depths, and taking the residual logging response parameters corresponding to 100% of the relative volume content in the fitting result as the logging skeleton value of the to-be-solved component.
10. The apparatus of claim 9, wherein the logging skeleton value determination module of the component to be solved is specifically configured to: Determining the logging skeleton value of the component to be solved according to a formula M ma ＝∑M xi /∑V xi , wherein M ma is the logging skeleton value of the component to be solved, M xi is the residual logging response parameter of the ith component to be solved, and Sigma V xi is the relative volume content of the ith component to be solved.
11. An electronic device, comprising: A processor; a memory; And a computer program, wherein the computer program is stored in the memory, which computer program, when executed by the processor, implements the method of any of claims 1-5.
12. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-5.
13. A computer program product, characterized in that the computer program product comprises a computer program which, when executed by a processor, implements the method of any one of claims 1-5.

Description

Method, device, equipment and storage medium for determining rock component logging skeleton value Technical Field The application relates to the technical field of petroleum exploration and development, in particular to a method, a device, equipment and a storage medium for determining a rock component logging skeleton value. Background The rock skeleton well logging response parameters are the basis of various well logging evaluation, the prior obtaining method mainly comprises two types, one type directly adopts classical mineral skeletons, such as quartz, calcite, dolomite and the like, the method is widely applied to various theoretical well logging volume models, intersection complex mineral evaluation, multi-mineral optimization evaluation models and the like, such as acoustic wave and density and other well logging argillaceous sandstone volume model formulas proposed by Yong and (2007) in well logging data processing and comprehensive interpretation, in a CRA processing method, two classical minerals and water points are adopted to form triangles to realize simultaneous evaluation on mineral components and porosity, the other type directly adopts a simple regression calculation mixed skeleton, so that various empirical formulas are established, such as Fu Dong (2016) for the current situation that the initial system sand river street volcanic skeleton parameters are difficult to determine, comprehensive data, conventional well logging curves and element capture spectrum well logging data are accurately divided on the premise of rock types, different lithologic rock core types are determined by using intersection graphs, multiple regression methods and the like, different lithologic rock core types are obtained by means of Chen Ganghua (2000), the different lithologic rock stratum are subjected to statistical analysis of different areas by using acoustic wave and the method, and the rock skeleton values of 35 are accurately calculated, and the well skeleton values of the well logging skeleton values are accurately calculated for obtaining the well skeleton values of 35, and the well logging skeleton values are respectively analyzed by using the method of the rock skeleton values of 35. The method has good evaluation effect on reservoirs with single components or simple mineral components, and has great error on well logging evaluation on reservoirs with complex components and large content variation of rock skeletons such as rock scraps sandstone or volcaniclastic rock, for example, high sun (2016) in the four-sub Duan Zhimi sandstone of the steep slope with sand in the northeast pit, and because of the diversity of the lithology and complex components of the sandstone, the porosity of well logging interpretation and the actually measured porosity have great difference, so that the effective reservoir identification of the sandstone in the area is directly influenced, the current increasingly complex reservoir parameters of the oil and gas reservoirs are difficult to evaluate, and the reason is mainly that the well logging response parameters of some main components are unknown, and an evaluation model is difficult to establish. It should be noted that the information disclosed in the background section of the present application is only for enhancement of understanding of the general background of the present application and should not be taken as an admission or any form of suggestion that this information forms the prior art that is well known to a person skilled in the art. Disclosure of Invention In view of the above, the application provides a method, a device, equipment and a storage medium for determining a rock component logging skeleton value, which are beneficial to solving the problems of the prior art that the rock skeleton components such as rock debris sandstone or volcaniclastic rock are complex and the content of the rock skeleton components is greatly changed, and the logging evaluation error is larger. In a first aspect, an embodiment of the present application provides a method for determining a skeleton value of a logging of a rock composition, including: determining the residual logging response parameters of the components to be solved in the samples with different depths according to the logging response parameters with different depths, the influence parameters of the known minerals in the samples with different depths and the influence parameters of the known fluids in the samples with different depths; Fitting a plurality of groups of to-be-solved component data with different depths, and determining the logging skeleton value of the to-be-solved component according to the fitting result, wherein the to-be-solved component data comprises the residual logging response parameters and the relative volume content of the to-be-solved component in the sample. In one possible implementation, before determining the remaining logging response parameters of the component to be solve