Method and device for determining rock component logging skeleton value, equipment and storage medium
By fitting well logging response parameters with known mineral and fluid influence parameters, the well logging framework values of lithic sandstone or volcanic clastic rock reservoirs are determined, solving the problem of well logging evaluation errors in complex reservoirs and achieving accurate reservoir parameter evaluation and oil and gas reservoir description.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2024-11-12
- Publication Date
- 2026-05-12
AI Technical Summary
Existing technologies have significant logging errors when evaluating reservoirs with complex rock skeletons and large variations in rock content, such as lithic sandstone or volcanic clastic rocks, making it difficult to accurately identify effective reservoirs.
By using logging response parameters and known mineral and fluid influence parameters, the remaining logging response parameters of the component to be determined are determined, and the logging skeleton value is obtained by fitting. The logging skeleton value of the component to be determined is then determined by fitting the remaining logging response parameters and relative volume content.
It improves the accuracy of reservoir parameter evaluation and oil and gas reservoir description, providing strong support for in-depth geological research on oil and gas reservoirs. It is simple and inexpensive to operate.
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Figure CN122014213A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of petroleum exploration and development technology, specifically to a method, apparatus, equipment, and storage medium for determining rock composition logging skeleton values. Background Technology
[0002] Rock skeleton logging response parameters are the foundation of various logging evaluations. Currently, there are two main methods for obtaining them. One method directly uses classic mineral skeletons, such as quartz, calcite, and dolomite. These are widely used in various theoretical logging volume models, cross-plot complex mineral evaluations, and multi-mineral optimization evaluation models. For example, Yong Shihe (2007) proposed a volume model formula for argillaceous sandstone logging based on sonic waves and density in logging data processing and comprehensive interpretation. In the CRA processing method, two classic minerals and water points are used to form a triangle to simultaneously evaluate mineral composition and porosity. The other method calculates a mixed skeleton through simple regression to establish various empirical formulas, such as those proposed by Fu Dong (2016). Given the difficulty in determining the volcanic skeleton parameters of the Eocene Shahejie Formation in the Nanpu No. 5 tectonic structure, under the premise of accurately classifying rock types, the skeleton parameters of different lithologies are determined by integrating core data, conventional logging curves, and element capture spectrum logging data, and by using methods such as cross plots and multiple regression. Chen Ganghua (2000) conducted statistical analysis on different reservoir types in different regions of volcanic rocks to determine the rock skeleton parameters of various reservoirs in the study area. Zhou Yue (2008) used core analysis data and logging data to obtain neutron, acoustic, and density skeleton parameters using five different methods to accurately determine the skeleton values of tuffaceous sandstone in the Wuerxun area, and then used the obtained skeleton values to calculate porosity.
[0003] These methods are effective for evaluating reservoirs with simple components or mineral composition. However, for reservoirs with complex rock skeletons and large variations in content, such as lithic sandstone or volcanic clastic rocks, well logging evaluation often results in large errors. For example, Gao Yang (2016) found that in the tight sandstone and conglomerate of the lower sub-section of the Sha-4 section in the steep slope zone of the northern Dongying Depression, the diverse lithology and complex composition of the sandstone and conglomerate led to a large difference between the porosity interpreted by well logging and the measured porosity. This directly affected the identification of effective reservoirs in the sandstone and conglomerate in this area, making it difficult to evaluate the increasingly complex oil and gas reservoir parameters. The main reason for this is that the well logging response parameters of some of the major components are unknown, making it difficult to establish an evaluation model.
[0004] It should be noted that the information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application, and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention
[0005] In view of this, this application provides a method, apparatus, equipment and storage medium for determining rock component logging skeleton values, in order to solve the problem of large logging evaluation errors in existing technologies for reservoirs such as lithic sandstone or volcanic clastic rocks with complex rock skeleton components and large content variations.
[0006] In a first aspect, embodiments of this application provide a method for determining the logging framework values of rock components, including:
[0007] Based on the logging response parameters at different depths, the influence parameters of known minerals in samples at different depths, and the influence parameters of known fluids in samples at different depths, the residual logging response parameters of the components to be determined in samples at different depths are determined.
[0008] Multiple sets of data for the components to be determined at different depths are fitted, and the logging framework values of the components to be determined are determined based on the fitting results. The data for the components to be determined include the remaining logging response parameters and relative volume content of the components to be determined in the sample.
[0009] In one possible implementation, before determining the residual logging response parameters of the component to be determined in samples at different depths based on logging response parameters at different depths, influence parameters of known minerals in samples at different depths, and influence parameters of known fluids in samples at different depths, the method further includes:
[0010] Well logging was used to obtain logging response parameters at different depths.
[0011] By analyzing samples at different depths, the relative volume contents of known minerals, known fluids, and the components to be determined are obtained.
[0012] In one possible implementation, determining the residual logging response parameters of the component to be determined in samples at different depths, based on logging response parameters at different depths, influence parameters of known minerals in samples at different depths, and influence parameters of known fluids in samples at different depths, includes:
[0013] According to the formula: M x =M-∑M mai V i -∑F j V j To determine the residual logging response parameters of the components to be determined in samples at different depths, where M x Let M be the residual logging response parameter of the component to be determined, and M be the logging response parameter. mai V represents the logging framework value for the i-th known mineral. i F represents the relative volume content of the i-th known mineral. j V is the logging skeleton value for the j-th known fluid. jLet be the relative volume content of the j-th known fluid.
[0014] In one possible implementation, fitting multiple sets of data for the components to be determined at different depths and determining the logging framework value of the components to be determined based on the fitting results includes:
[0015] Forced zero-crossing fitting is performed on multiple sets of data of the components to be determined at different depths, and the remaining logging response parameters corresponding to the relative volume content of 100% in the fitting results are used as the logging skeleton values of the components to be determined.
[0016] In one possible implementation, the forced zero-crossing fitting of multiple sets of data on the components to be determined at different depths, and the use of the remaining logging response parameters corresponding to 100% relative volume content in the fitting results as the logging skeleton values of the components to be determined, includes:
[0017] According to the formula: M ma =∑M xi / ∑V xi Determine the logging framework value of the component to be determined, where M ma M is the logging framework value of the component to be determined. xi Let ∑V be the residual logging response parameter of the i-th component to be determined. xi Let be the relative volume content of the i-th component to be determined.
[0018] Secondly, embodiments of this application provide an apparatus for determining the logging skeleton value of rock components, including:
[0019] The residual logging response parameter determination module is used to determine the residual logging response parameters of the components to be determined in samples at different depths based on the logging response parameters at different depths, the influence parameters of known minerals in samples at different depths, and the influence parameters of known fluids in samples at different depths.
[0020] The well logging skeleton value determination module for the component to be determined is used to fit multiple sets of data of the component to be determined at different depths, and determine the well logging skeleton value of the component to be determined based on the fitting results. The data of the component to be determined includes the remaining well logging response parameters and relative volume content of the component to be determined in the sample.
[0021] One possible implementation also includes:
[0022] The logging module is used to obtain logging response parameters at different depths through logging.
[0023] The sample analysis module is used to analyze samples at different depths to obtain the relative volume content of known minerals, known fluids, and the relative volume content of the component to be determined in the samples at different depths.
[0024] In one possible implementation, the remaining logging response parameter determination module is specifically used for:
[0025] According to the formula: M x =M-∑M mai V i -∑F j V j To determine the residual logging response parameters of the components to be determined in samples at different depths, where M x Let M be the residual logging response parameter of the component to be determined, and M be the logging response parameter. mai V represents the logging framework value for the i-th known mineral. i F represents the relative volume content of the i-th known mineral. j V is the logging skeleton value for the j-th known fluid. j Let be the relative volume content of the j-th known fluid.
[0026] In one possible implementation, the well logging skeleton value determination module for the component to be determined is specifically used for:
[0027] Forced zero-crossing fitting is performed on multiple sets of data of the components to be determined at different depths, and the remaining logging response parameters corresponding to the relative volume content of 100% in the fitting results are used as the logging skeleton values of the components to be determined.
[0028] In one possible implementation, the well logging skeleton value determination module for the component to be determined is specifically used for:
[0029] According to the formula: M ma =∑M xi / ∑V xi Determine the logging framework value of the component to be determined, where M ma M is the logging framework value of the component to be determined. xi Let ∑V be the residual logging response parameter of the i-th component to be determined. xi Let be the relative volume content of the i-th component to be determined.
[0030] Thirdly, embodiments of this application provide an electronic device, including:
[0031] processor;
[0032] Memory;
[0033] And a computer program, wherein the computer program is stored in the memory, and when the computer program is executed by the processor, implements the method described in any one of the first aspects.
[0034] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method described in any one of the first aspects.
[0035] Fifthly, embodiments of this application provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the method described in any one of the first aspects.
[0036] In this embodiment of the application, for reservoirs with complex rock skeleton components and large content variations, the residual logging response parameters of the component to be determined are extracted using stripping values. By fitting the residual logging response parameters and the relative volume content, the logging skeleton value of the component to be determined is determined, which provides favorable support for accurate evaluation of reservoir parameters, description of oil and gas reservoirs, and in-depth geological research of oil and gas reservoirs.
[0037] In addition, this method is based on conventional logging and core drilling data, which are abundant and inexpensive, and the operation steps are simple and quick, with good identification and evaluation results. Attached Figure Description
[0038] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0039] Figure 1 A schematic flowchart illustrating a method for determining rock composition logging skeleton values provided in this application embodiment;
[0040] Figure 2 This is a schematic diagram illustrating the relationship between the content and density of residual rock fragments, provided in an embodiment of this application.
[0041] Figure 3 A structural block diagram of a device for determining rock composition logging skeleton values is also provided in this application embodiment;
[0042] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0043] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.
[0044] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.
[0045] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.
[0046] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0047] Existing technologies often suffer from significant logging evaluation errors in reservoirs with complex rock skeletons and highly variable rock contents, such as lithic sandstone or volcanic clastic rocks. This application provides a method for determining the logging skeleton values of rock components. The theoretical basis lies in the core section of the wellbore. Thin section analysis of the core can determine the component content in rock samples at different depths. Core analysis experiments yield the porosity and saturation at the corresponding depths. Logging provides various logging response parameters for that depth. After removing the influence of known minerals and fluids, the logging response parameters for specific components at that depth can be determined. Since there are differences between samples at different depths, the logging skeleton values can be determined by regressing the logging response and content. The specific implementation method is described in detail below.
[0048] See Figure 1 This is a schematic diagram of the method for determining the rock composition logging framework value provided in an embodiment of this application. Figure 1 As shown, it mainly includes the following steps.
[0049] Step S101: Based on the logging response parameters at different depths, the influence parameters of known minerals in samples at different depths, and the influence parameters of known fluids in samples at different depths, determine the remaining logging response parameters of the components to be determined in samples at different depths.
[0050] In practical applications, well logging can be used to obtain well logging response parameters at different depths. By analyzing samples at different depths, the relative volumetric content of known minerals, the relative volumetric content of known fluids, and the relative volumetric content of the component to be determined can be obtained. It should be noted that the sample depth and the depth of the well logging response parameters in the same set of data should match.
[0051] Specifically, the mineral content, porosity, and saturation of rock thin sections (samples) can be normalized to obtain the relative contents of components, fluids, and components to be determined in the rock at each sample depth. A volume model can then be constructed to obtain the relative volume contents of known minerals, known fluids, and components to be determined in samples at different depths.
[0052] Among them, the well logging framework values of known minerals and known fluids are known theoretical values. Therefore, the influence parameters of known minerals and known fluids can be determined based on the relative volume content of known minerals, the relative volume content of known fluids, the well logging framework values of known minerals and known fluids.
[0053] In practice, the formula for calculating the residual logging response parameters of the component to be determined is as follows.
[0054] Formula 1:
[0055] M x =M-∑M mai V i -∑F j V j
[0056] Among them, M x Let M be the residual logging response parameter of the component to be determined, and M be the logging response parameter. mai V represents the logging framework value for the i-th known mineral. i F represents the relative volume content of the i-th known mineral. j V is the logging skeleton value for the j-th known fluid. j The relative volume fraction of the j-th known fluid
[0057] Step S102: Fit multiple sets of data of the components to be determined at different depths, and determine the logging skeleton value of the components to be determined based on the fitting results. The data of the components to be determined include the remaining logging response parameters and relative volume content of the components to be determined in the sample.
[0058] In one possible implementation, forced zero-crossing fitting is performed on multiple sets of data of the components to be determined at different depths, and the remaining logging response parameters corresponding to the 100% relative volume content in the fitting results are used as the logging skeleton values of the components to be determined.
[0059] In practice, the calculation formula for the logging skeleton value of the component to be determined is as follows.
[0060] Formula 2:
[0061] M ma =∑M xi / ∑V xi
[0062] Among them, M ma M represents the logging framework value of the component to be determined. xi Let ∑V be the residual logging response parameter of the i-th component to be determined. xi Let be the relative volume content of the i-th component to be determined.
[0063] In this embodiment of the application, for reservoirs with complex rock skeleton components and large content variations, the residual logging response parameters of the component to be determined are extracted using stripping values. By fitting the residual logging response parameters and the relative volume content, the logging skeleton value of the component to be determined is determined, which provides favorable support for accurate evaluation of reservoir parameters, description of oil and gas reservoirs, and in-depth geological research of oil and gas reservoirs.
[0064] In addition, this method is based on conventional logging and core drilling data, which are abundant and inexpensive, and the operation steps are simple and quick, with good identification and evaluation results.
[0065] For ease of explanation, the technical solutions provided in the embodiments of this application will be described in detail below with reference to specific application scenarios.
[0066] For the Jurassic strata in a certain oilfield in Northwest China that contain lithic sandstone, the mineral, porosity and saturation of thin sections of samples from multiple depths in the core wells of this strata were normalized to obtain the volume contents of clay, quartz, feldspar, calcite, lithic fragments, as well as the pore volume contents of oil and water. Based on Formula 1, the model is constructed as follows.
[0067] ρ Debi =ρ b -(V cl ρ cl +V Qt ρ Qt +V lime ρ lime +V oil ρ oil +V Wat ρ Wat )
[0068] Where, ρ Debi Density of remaining rock debris in the sample, in g / cm³ 3 ;ρ b This is the logging compensation density, in g / cm³. 3 ;ρ cl This refers to the density of clay, expressed in g / cm³. 3 ;ρ Qt This refers to the density of quartz, expressed in g / cm³. 3 ;ρ lime This refers to the density of calcite, expressed in g / cm³. 3 ;ρ oil This refers to the density of petroleum, expressed in g / cm³.3 ;ρ Wat This is the density of water, expressed in g / cm³. 3 V cl V represents the volume content of clay. Qt V represents the volume content of quartz. lime V represents the volume content of calcite. oil V represents the pore volume of oil. Wat The water pore volume is represented by the well logging skeleton parameters for clay, quartz, feldspar, calcite, oil, and water, which are known theoretical values, as detailed in Table 1.
[0069] Table 1:
[0070] Minerals and Fluids clay quartz calcite oil water Skeletal density 2.45 2.65 2.71 0.7 1.0
[0071] The residual rock fragment density calculated by the above model for each depth sample was then fitted to the residual rock fragment volume content using a forced zero-crossing method to obtain the correlation between the residual rock fragment content and the residual rock fragment density, as follows: Figure 2 As shown. Furthermore, the density of the remaining rock fragments corresponding to a 100% remaining rock fragment content was determined as the skeletal density of the remaining rock fragments, which is 2.65 g / cm³. 3 .
[0072] Corresponding to the above embodiments, this application also provides a device for determining the logging skeleton value of rock composition.
[0073] See Figure 3 The diagram below shows a structural block diagram of a device for determining rock composition logging skeleton values, as provided in this application embodiment. Figure 3 As shown, it mainly includes the following modules.
[0074] The residual logging response parameter determination module 301 is used to determine the residual logging response parameters of the components to be determined in samples at different depths based on the logging response parameters at different depths, the influence parameters of known minerals in samples at different depths, and the influence parameters of known fluids in samples at different depths.
[0075] The well logging skeleton value determination module 302 is used to fit multiple sets of data of the component to be determined at different depths, and determine the well logging skeleton value of the component to be determined based on the fitting results. The data of the component to be determined includes the remaining well logging response parameters and relative volume content of the component to be determined in the sample.
[0076] In one possible implementation, the device for determining the logging framework value of rock components further includes: a logging module for obtaining logging response parameters at different depths through logging; and a sample analysis module for obtaining the relative volume content of known minerals, the relative volume content of known fluids, and the relative volume content of the component to be determined in samples at different depths by analyzing the samples at different depths.
[0077] In one possible implementation, the residual logging response parameter determination module 301 is specifically used to: determine the parameters according to the formula: M x =M-∑M mai V i -∑F j V j To determine the residual logging response parameters of the components to be determined in samples at different depths, where M x Let M be the residual logging response parameter of the component to be determined, and M be the logging response parameter. mai V represents the logging framework value for the i-th known mineral. i F represents the relative volume content of the i-th known mineral. j V is the logging skeleton value for the j-th known fluid. j Let be the relative volume content of the j-th known fluid.
[0078] In one possible implementation, the logging skeleton value determination module 302 for the component to be determined is specifically used to: perform forced zero-crossing fitting on multiple sets of data of the component to be determined at different depths, and use the remaining logging response parameters corresponding to the relative volume content of 100% in the fitting results as the logging skeleton value of the component to be determined.
[0079] In one possible implementation, the logging skeleton value determination module 302 for the component to be determined is specifically used to: determine the value according to the formula: M ma =∑M xi / ∑V xi Determine the logging framework values for the component to be determined, where M ma M represents the logging framework value of the component to be determined. xi Let ∑V be the residual logging response parameter of the i-th component to be determined. xi Let be the relative volume content of the i-th component to be determined.
[0080] In this embodiment of the application, for reservoirs with complex rock skeleton components and large content variations, the residual logging response parameters of the component to be determined are extracted using stripping values. By fitting the residual logging response parameters and the relative volume content, the logging skeleton value of the component to be determined is determined, which provides favorable support for accurate evaluation of reservoir parameters, description of oil and gas reservoirs, and in-depth geological research of oil and gas reservoirs.
[0081] In addition, this method is based on conventional logging and core drilling data, which are abundant and inexpensive, and the operation steps are simple and quick, with good identification and evaluation results.
[0082] It should be noted that the specific content involved in the embodiments of this application can be found in the description of the above method embodiments, and will not be repeated here for the sake of brevity.
[0083] Corresponding to the above embodiments, this application also provides an electronic device.
[0084] See Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 400 may include a processor 401, a memory 402, and a communication unit 403. These components communicate via one or more buses. Those skilled in the art will understand that the electronic device structure shown in the figures does not constitute a limitation on the embodiments of this application. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.
[0085] The communication unit 403 is used to establish a communication channel, thereby enabling the electronic device to communicate with other devices.
[0086] The processor 401 serves as the control center of the electronic device, connecting various parts of the device via various interfaces and lines. It executes software programs and / or modules stored in the memory 402, and calls data stored in the memory to perform various functions and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 401 may consist only of a central processing unit (CPU). In this embodiment, the CPU may have a single processing core or include multiple processing cores.
[0087] Memory 402 is used to store the execution instructions of processor 401. Memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.
[0088] When the execution instructions in memory 402 are executed by processor 401, the electronic device 400 is able to perform some or all of the steps in the above method embodiments.
[0089] Corresponding to the above embodiments, this application also provides a computer-readable storage medium, wherein the computer-readable storage medium may store a computer program, and when the computer program is executed by a processor, it may implement some or all of the steps in the above method embodiments.
[0090] In specific implementations, the computer-readable storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.
[0091] Corresponding to the above embodiments, this application also provides a computer program product, which includes a computer program that, when executed by a processor, can implement some or all of the steps in the above method embodiments.
[0092] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.
[0093] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0094] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.
[0095] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0096] The above are merely specific embodiments of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application shall be determined by the protection scope of the claims.
Claims
1. A method for determining the logging skeleton values of rock components, characterized in that, include: Based on the logging response parameters at different depths, the influence parameters of known minerals in samples at different depths, and the influence parameters of known fluids in samples at different depths, the residual logging response parameters of the components to be determined in samples at different depths are determined. Multiple sets of data for the components to be determined at different depths are fitted, and the logging framework values of the components to be determined are determined based on the fitting results. The data for the components to be determined include the remaining logging response parameters and relative volume content of the components to be determined in the sample.
2. The method according to claim 1, characterized in that, Before determining the residual logging response parameters of the component to be determined in samples at different depths based on logging response parameters at different depths, influence parameters of known minerals in samples at different depths, and influence parameters of known fluids in samples at different depths, the method further includes: Well logging was used to obtain logging response parameters at different depths. By analyzing samples at different depths, the relative volume contents of known minerals, known fluids, and the components to be determined are obtained.
3. The method according to claim 2, characterized in that, The process of determining the residual logging response parameters of the component to be determined in samples at different depths based on logging response parameters at different depths, influence parameters of known minerals in samples at different depths, and influence parameters of known fluids in samples at different depths includes: According to the formula: M x =M-∑M mai V i -∑F j V j To determine the residual logging response parameters of the components to be determined in samples at different depths, where M x Let M be the residual logging response parameter of the component to be determined, and M be the logging response parameter. mai V represents the logging framework value for the i-th known mineral. i F represents the relative volume content of the i-th known mineral. j V is the logging skeleton value for the j-th known fluid. j Let be the relative volume content of the j-th known fluid.
4. The method according to claim 1, characterized in that, The process of fitting multiple sets of data for the components to be determined at different depths, and determining the logging framework values of the components to be determined based on the fitting results, includes: Forced zero-crossing fitting is performed on multiple sets of data of the components to be determined at different depths, and the remaining logging response parameters corresponding to the relative volume content of 100% in the fitting results are used as the logging skeleton values of the components to be determined.
5. The method according to claim 4, characterized in that, The forced zero-crossing fitting of multiple sets of data for the components to be determined at different depths, and the use of the remaining logging response parameters corresponding to 100% relative volume content in the fitting results as the logging skeleton values of the components to be determined, includes: According to the formula: M ma =∑M xi / ∑V xi Determine the logging framework value of the component to be determined, where M ma M is the logging framework value of the component to be determined. xi Let ∑V be the residual logging response parameter of the i-th component to be determined. xi Let be the relative volume content of the i-th component to be determined.
6. A device for determining the logging skeleton value of rock composition, characterized in that, include: The residual logging response parameter determination module is used to determine the residual logging response parameters of the components to be determined in samples at different depths based on the logging response parameters at different depths, the influence parameters of known minerals in samples at different depths, and the influence parameters of known fluids in samples at different depths. The well logging skeleton value determination module for the component to be determined is used to fit multiple sets of data of the component to be determined at different depths, and determine the well logging skeleton value of the component to be determined based on the fitting results. The data of the component to be determined includes the remaining well logging response parameters and relative volume content of the component to be determined in the sample.
7. The apparatus according to claim 6, characterized in that, Also includes: The logging module is used to obtain logging response parameters at different depths through logging. The sample analysis module is used to analyze samples at different depths to obtain the relative volume content of known minerals, known fluids, and the relative volume content of the component to be determined in the samples at different depths.
8. The apparatus according to claim 7, characterized in that, The remaining logging response parameter determination module is specifically used for: According to the formula: M x =M-∑M mai V i -∑F j V j To determine the residual logging response parameters of the components to be determined in samples at different depths, where M x Let M be the residual logging response parameter of the component to be determined, and M be the logging response parameter. mai V represents the logging framework value for the i-th known mineral. i F represents the relative volume content of the i-th known mineral. j V is the logging skeleton value for the j-th known fluid. j Let be the relative volume content of the j-th known fluid.
9. The apparatus according to claim 6, characterized in that, The well logging skeleton value determination module for the component to be determined is specifically used for: Forced zero-crossing fitting is performed on multiple sets of data of the components to be determined at different depths, and the remaining logging response parameters corresponding to the relative volume content of 100% in the fitting results are used as the logging skeleton values of the components to be determined.
10. The apparatus according to claim 9, characterized in that, The well logging skeleton value determination module for the component to be determined is specifically used for: According to the formula: M ma =∑M xi / ∑V xi Determine the logging framework value of the component to be determined, where M ma M is the logging framework value of the component to be determined. xi Let ∑V be the residual logging response parameter of the i-th component to be determined. xi Let be the relative volume content of the i-th component to be determined.
11. An electronic device, characterized in that, include: processor; Memory; And a computer program, wherein the computer program is stored in the memory, and when executed by the processor, the computer program implements the method of any one of claims 1-5.
12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-5.
13. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-5.