Microbial rock optimization interpretation method and device, electronic equipment and storage medium

Abstract

The invention provides a microbial rock optimization interpretation method and device, electronic equipment and a storage medium, and the method comprises the steps: extracting a mineral proportion of a dry rock through element logging interpretation, taking the mineral proportion as a constraint curve of a corresponding mineral, converting the mineral proportion of the dry rock of element logging into the mineral content of a wet rock through a particle density equation, and calculating the mineral content of the wet rock according to the mineral content. The effective porosity of wet rocks is extracted through nuclear magnetic well logging interpretation to serve as a constraint curve of oil and water volumes, a more perfect well logging interpretation model suitable for microbial carbonate rocks is established by combining conventional gamma, depth and depth conductivity, sound waves, density, neutrons, photoelectric absorption section indexes and the like, and a reliable lithologic section with accurate mineral content and porosity is obtained. The method solves the problem that the mineral content and the porosity are not matched with the core and cannot be reconciled, and has important significance for improving the precision of optimal logging interpretation of the microbial carbonate rocks and reservoir research.

Description

Technical Field

[0001] The present application relates to the field of well logging technology, and in particular to a method, device, electronic equipment and storage medium for optimizing interpretation of microbial rocks incorporating element logging and nuclear magnetic logging. Background Art

[0002] Brazil's subsalt microbial carbonate reservoirs have always been the focus and difficulty of oil and gas reservoir development in the region. Due to the superposition of early deposition and later transformation, there are many mineral components (usually including illite, kaolinite, feldspar, siliceous, dolomite, lime, volcanic rock, etc.), the logging response is complex, and some parameters (skeleton parameters such as illite, kaolinite, volcanic rock, etc.) are uncertain. The logging interpretation is difficult, and conventional optimization interpretation is difficult to obtain accurate mineral content and porosity. Mineral content and porosity are the basis of lithology naming and lithofacies analysis. How to improve the accuracy of logging interpretation to obtain reliable lithology profiles (mineral content and porosity) is the difficulty and challenge faced in the reservoir research process of this type of oil and gas reservoir development plan.

[0003] In the prior art, in order to obtain the lithological profile of mineral content and porosity, multi-mineral model deterministic method and optimization method are usually used. For the multi-mineral model deterministic method, it is usually only applicable to three minerals at most; for the optimization method, it is applicable to more than three minerals, and conventional curves (natural gamma, resistivity, acoustic wave, density, neutron, photoelectric absorption cross-section index, etc.) are usually used for optimization interpretation. There is no precedent in the literature for combining element logging and nuclear magnetic logging for optimization logging interpretation. The study believes that for the complex Brazilian subsalt microbial carbonate rocks, element logging provides the original dry rock mineral content, while nuclear magnetic logging provides reliable porosity, and both are indispensable.

[0004] The traditional optimization interpretation method does not fully integrate the element logging results and nuclear magnetic resonance logging results into the optimization interpretation. In the process of optimizing logging interpretation of actual microbial carbonate wells, due to the diversity of microbial carbonate minerals, the uncertainty of some parameters, and the limitations of conventional logging curves, it often leads to irreconcilable problems such as mismatch between mineral content and porosity and core. Summary of the invention

[0005] In view of this, in the process of optimizing logging interpretation of actual wells, due to the diversity of microbial carbonate minerals and the uncertainty of some parameters, and the limitations of conventional logging curves, the mineral content and porosity often lead to irreconcilable mismatch with the core. This application proposes a method for optimizing logging interpretation of microbial carbonates that integrates element logging and nuclear magnetic logging on the basis of conventional optimization interpretation. This invention is of great significance for improving the accuracy of optimizing logging interpretation of microbial carbonates and reservoir research.

[0006] In a first aspect, the present application provides a carbonate rock optimization interpretation method integrating element logging and nuclear magnetic logging, including:

[0007] Step S1: Determine the main mineral types through element logging interpretation;

[0008] Step S2: extracting the dry rock mineral proportion of the main mineral type through element logging interpretation, and using the dry rock mineral proportion of the main mineral type as the constraint curve of the main mineral type;

[0009] Step S3: using a particle density equation, converting the dry rock mineral content into the mineral content of the wet rock;

[0010] Step S4: extracting the effective porosity of the wet rock as a constraint curve of oil and water volume through nuclear magnetic logging interpretation;

[0011] Step S5: Establishing a carbonate logging interpretation model by combining the constraint curves of the main mineral types and the constraint curves of oil and water volumes and gamma, deep and shallow conductivity, acoustic wave, density, neutron, and photoelectric absorption cross-section indexes;

[0012] Step S6: Optimizing the carbonate rock logging interpretation model until the forward fitting of the logging curve meets the error requirement, thereby obtaining the optimized carbonate rock logging interpretation model;

[0013] Step S7: using the optimized carbonate logging interpretation model to obtain optimized logging interpretation results.

[0014] In a possible implementation, the mineral types include: illite, kaolinite, feldspar, quartz, calcite, and dolomite.

[0015] In a possible implementation, the dry rock mineral percentage refers to the rock mineral percentage without pores.

[0016] In one possible implementation, the mineral content of the wet rock represents the mineral content of the rock containing pores.

[0017] In a possible implementation, the calculation formula of the mineral content of the wet rock is as follows:

[0018]

[0019] Among them, Wet Vol% represents the mineral content of wet rock, Dry Weight% represents the mineral ratio of dry rock, PhiT represents the total porosity, Rock Grain Density represents the rock density, and Mineral Grain Density represents the mineral density.

[0020] In a possible implementation, the forward equation of the carbonate well logging interpretation model is as follows:

[0021] ρ ma1 V1+ρ ma2 V2+…+ρ mam V m =ρ b

[0022] DT ma1 V1+DT ma2 V2+…+DT mam V m =DT b

[0023] V1+V2+…+V m =1

[0024] Among them, ρ ma1 ,ρ ma2 ,…,ρ mam Respectively represent the skeleton density of the first, second, up to the mth mineral or fluid; V1, V2, ..., V m Respectively represent the volume ratio of the first, second, up to the mth mineral or fluid; ρ b Indicates logging density; DT ma1 ,DT ma2 ,…,DT mam Respectively represent the skeleton sound waves of the first, second, and up to the mth mineral or fluid; DT b Indicates well sound waves.

[0025] In a possible implementation, the well logging interpretation results include mineral content, porosity, etc.

[0026] In a second aspect, the present application provides a carbonate rock optimization interpretation device integrating element logging and nuclear magnetic logging, including:

[0027] Mineral type determination module, used to determine the main mineral types through element logging interpretation;

[0028] A dry rock mineral ratio acquisition module is used to extract the dry rock mineral ratio of the main mineral type through element logging interpretation, and use the dry rock mineral ratio of the main mineral type as a constraint curve of the main mineral type;

[0029] A wet rock mineral content acquisition module, used to convert the dry rock mineral ratio into the wet rock mineral content by using a particle density equation;

[0030] An effective porosity acquisition module is used to extract the effective porosity of the wet rock as a constraint curve of oil and water volume through nuclear magnetic logging interpretation;

[0031] A model building module is used to build a carbonate logging interpretation model by combining the constraint curves of the main mineral types and the constraint curves of oil and water volumes and gamma, deep and shallow conductivity, acoustic wave, density, neutron, and photoelectric absorption cross-section indexes;

[0032] A model optimization module is used to optimize the carbonate logging interpretation model until the forward fitting of the logging curve meets the error requirement, thereby obtaining the most optimized carbonate logging interpretation model;

[0033] The result acquisition module is used to obtain the optimized well logging interpretation result according to the optimized carbonate well logging interpretation model.

[0034] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0035] processor;

[0036] Memory;

[0037] And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, and when the instructions are executed by the processor, the electronic device executes any one of the methods described in the first aspect.

[0038] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the methods described in the first aspect.

[0039] This application extracts the mineral proportion of dry rock as the constraint curve of the corresponding mineral through the element logging interpretation results, converts the mineral proportion of dry rock (without pores) of element logging into the mineral content of wet rock (with pores) by using the particle density equation, extracts the effective porosity as the constraint curve of oil and water volume through the nuclear magnetic logging interpretation results, and establishes a more perfect logging interpretation model suitable for microbial carbonate rocks by combining conventional gamma, deep and shallow conductivity, acoustic wave, density, neutron, photoelectric absorption cross-section index, etc., to obtain a reliable lithology profile with accurate mineral content and porosity. This invention is of great significance for improving the accuracy of microbial carbonate rock optimization logging interpretation and reservoir research. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0041] Figure 1 A schematic flow chart of a carbonate rock optimization interpretation method incorporating element logging and nuclear magnetic logging provided in an embodiment of the present application;

[0042] Figure 2 A carbonate rock optimization interpretation method incorporating element logging and nuclear magnetic logging provided in an embodiment of the present application is applied to the optimization logging interpretation result diagram of the PDA-A2 well;

[0043] Figure 3 A carbonate rock optimization interpretation method incorporating element logging and nuclear magnetic logging provided in an embodiment of the present application is applied to the optimization logging interpretation result diagram of the PDA-1 well;

[0044] Figure 4 An optimized logging interpretation mineral content (quartz, calcite, dolomite) and element logging mineral cross-plot of a carbonate rock optimization interpretation method incorporating element logging and nuclear magnetic logging provided in an embodiment of the present application;

[0045] Figure 5 A cross-plot of optimized logging interpretation porosity and nuclear magnetic logging porosity for a carbonate rock optimization interpretation method incorporating element logging and nuclear magnetic logging provided in an embodiment of the present application;

[0046] Figure 6 A structural block diagram of a carbonate rock optimization interpretation device integrating element logging and nuclear magnetic logging provided in an embodiment of the present application;

[0047] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0049] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0050] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0051] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0052] See also Figure 1 , is a flow chart of a carbonate rock optimization interpretation method integrating element logging and nuclear magnetic logging provided in an embodiment of the present application. Figure 1 As shown, it mainly includes the following steps.

[0053] Step S1: Determine the main mineral types through element logging interpretation.

[0054] The mineral types include: illite, kaolinite, feldspar, quartz, calcite, and dolomite.

[0055] Step S2: extract the dry rock mineral proportion of the main mineral type through element logging interpretation, and use the dry rock mineral proportion of the main mineral type as the constraint curve of the main mineral type, such as WQFM is the constraint curve of quartz, WCLA is the constraint curve of illite and kaolinite, WCAR is the constraint curve of calcite and dolomite, etc.

[0056] Step S3: Using the particle density equation, convert the dry rock mineral content into the mineral content of the wet rock.

[0057] The mineral content of the dry rock is the mineral content without pores, and the mineral content of the wet rock is the mineral content with pores.

[0058] The calculation equation for the mineral content of the wet rock is as follows:

[0059]

[0060] Among them, Wet Vol% represents the mineral content of wet rock, Dry Weight% represents the mineral ratio of dry rock, PhiT represents the total porosity, Rock Grain Density represents the rock density, and Mineral Grain Density represents the mineral density.

[0061] Step S4: Through nuclear magnetic logging interpretation, the effective porosity of the wet rock is extracted to form a constraint curve for oil and water volumes.

[0062] Step S5: Establish a carbonate logging interpretation model by combining the constraint curves of the main mineral types and the constraint curves of oil and water volumes and gamma, deep and shallow conductivity, acoustic wave, density, neutron, and photoelectric absorption cross-section indexes.

[0063] The forward equation of the carbonate logging interpretation model is as follows:

[0064] ρ ma1 V1+ρ ma2 V2+…+ρ man V m =ρ b

[0065] DT ma1 V1+DT ma2 V2+…+DT mam V m =DT b

[0066] V1+V2+…+V m =1

[0067] Among them, ρ ma1 ,ρ ma2 ,…,ρ mam Respectively represent the skeleton density of the first, second, up to the mth mineral or fluid; V1, V2, ..., V m Respectively represent the volume ratio of the first, second, up to the mth mineral or fluid; ρ b Indicates logging density; DT ma1 ,DT ma2 ,…,DT mam Respectively represent the skeleton sound waves of the first, second, and up to the mth mineral or fluid; DT b Indicates well sound waves.

[0068] Step S6: Select mineral and fluid parameters, and optimize the carbonate logging interpretation model until the forward fitting of the logging curve meets the error requirement, thereby obtaining the optimized carbonate logging interpretation model.

[0069] Step S7: based on the optimized carbonate logging interpretation model, obtaining an optimized logging interpretation result.

[0070] The logging interpretation results include mineral content, porosity, etc.

[0071] The present application provides an optimized carbonate interpretation method incorporating element logging and nuclear magnetic logging, which was applied to 157 wells in the BMC-33 block and Lula oil field in Brazil from 2019 to 2022, such as PDA-A2 well and PDA-1 well. The optimized logging interpretation result of PDA-A2 well is shown in the attached figure. Figure 2 As shown, attached Figure 2 From left to right in the figure are depth, horizon, ECS element logging mineral volume, optimized logging interpretation lithology profile, net thickness, DST, optimized interpretation porosity and core porosity, NMR porosity and core porosity, NMR T2 spectrum, GR and well diameter, resistivity, density, neutron, and acoustic wave. The optimized logging interpretation lithology profile and element logging mineral composition and content are basically consistent, and the logging interpretation porosity, NMR porosity and core porosity are basically consistent. The optimized logging interpretation results of PDA-1 well are shown in the attached figure. Figure 3 As shown, attached Figure 3 From left to right in the figure are depth, horizon, ECS element logging mineral volume, optimized logging interpretation lithology profile, net thickness, DST, optimized interpretation porosity and core porosity, NMR porosity and core porosity, NMR T2 spectrum, GR and well diameter, resistivity, density, neutron, and acoustic wave. The optimized logging interpretation lithology profile and element logging mineral composition and content are basically consistent, and the logging interpretation porosity, NMR porosity, and core porosity are basically consistent. The mineral proportion of dry rock is extracted from the element logging interpretation results as the constraint curve of the corresponding minerals. The effective porosity is extracted from the nuclear magnetic logging interpretation results as the constraint curve of oil and water volume. Combined with conventional gamma, deep and shallow conductivity, acoustic wave, density, neutron, photoelectric absorption cross-section index, etc., a more complete logging interpretation model suitable for microbial carbonate rocks is established to obtain a reliable lithology profile with accurate mineral content and porosity. The optimized logging interpretation lithology profile is basically consistent with the mineral composition and content of element logging. The intersection diagram of the optimized logging interpretation mineral content (quartz, calcite, dolomite) and the element logging mineral is shown in the attached figure. Figure 4 As shown, from Figure 4 It can be seen that the optimized interpretation of mineral content and element logging mineralogy are basically linear, indicating that the optimization result is reliable. The porosity interpreted by logging, nuclear magnetic porosity and core porosity are basically consistent. The cross-plot of the optimized logging interpretation porosity and nuclear magnetic logging porosity is shown in the attached figure. Figure 5 As shown, from Figure 5 It can be seen that the two have a good correlation, indicating that the optimized logging interpretation of porosity is more reliable.

[0072] In summary, the present application provides an optimized interpretation method for carbonate rocks that incorporates element logging and nuclear magnetic logging. Through application in 157 wells in the BMC-33 block and Lula oil field in Brazil, the accuracy of logging interpretation has been greatly improved, and the mineral content and porosity interpreted by logging have a 95% consistency with the core. Reliable logging interpretation of mineral content and porosity has laid an important foundation for lithofacies analysis, reservoir research and modeling for the development of subsalt oil and gas reservoirs in Brazil.

[0073] Corresponding to the above-mentioned embodiments, the present application also provides a carbonate rock optimization interpretation device integrating element logging and nuclear magnetic logging.

[0074] See also Figure 6 , is a structural block diagram of a carbonate rock optimization interpretation device integrating element logging and nuclear magnetic logging provided in an embodiment of the present application. Figure 6 As shown, it mainly includes the following modules.

[0075] The mineral type determination module 601 is used to determine the main mineral type through element logging interpretation.

[0076] The dry rock mineral proportion acquisition module 602 is used to extract the dry rock mineral proportion of the main mineral type through element logging interpretation, and use the dry rock mineral proportion of the main mineral type as the constraint curve of the main mineral type.

[0077] The wet rock mineral content acquisition module 603 is used to convert the dry rock mineral ratio into the wet rock mineral content by using the particle density equation.

[0078] The effective porosity acquisition module 604 is used to extract the effective porosity of the wet rock as the constraint curve of oil and water volume through nuclear magnetic logging interpretation.

[0079] The model building module 605 is used to build a carbonate logging interpretation model by combining the constraint curves of the main mineral types and the constraint curves of oil and water volumes and gamma, deep and shallow conductivity, acoustic wave, density, neutron, and photoelectric absorption cross-section index.

[0080] The model optimization module 606 is used to select mineral and fluid parameters and optimize the carbonate logging interpretation model until the forward fitting of the logging curve meets the error requirement, thereby obtaining the optimized carbonate logging interpretation model.

[0081] The result acquisition module 607 is used to obtain the optimized well logging interpretation result according to the optimized carbonate well logging interpretation model.

[0082] It should be pointed out that the specific contents involved in the embodiments of the present application can be found in the description of the above method embodiments, and for the sake of brevity, they will not be repeated here.

[0083] Corresponding to the above embodiment, an embodiment of the present application further provides an electronic device.

[0084] See also Figure 7 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 7 As shown, the electronic device 700 may include: a processor 701, a memory 702 and a communication unit 703. These components communicate via one or more buses. Those skilled in the art may understand that the electronic device structure shown in the figure does not constitute a limitation on the embodiments of the present application. It may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

[0085] The communication unit 703 is used to establish a communication channel so that the electronic device can communicate with other devices.

[0086] The processor 701 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs and / or modules stored in the memory 702, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 701 can include only a central processing unit (CPU). In the embodiment of the present application, the CPU can be a single computing core or multiple computing cores.

[0087] The memory 702 is used to store the execution instructions of the processor 701. The memory 702 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 memory, flash memory, magnetic disk or optical disk.

[0088] When the execution instructions in the memory 702 are executed by the processor 701 , the electronic device 700 is enabled to execute part or all of the steps in the above method embodiment.

[0089] Corresponding to the above embodiment, the embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium may store a program, wherein when the program is running, the device where the computer-readable storage medium is located may be controlled to perform some or all of the steps in the above method embodiment. In a specific implementation, the computer-readable storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0090] Corresponding to the above-mentioned embodiment, the embodiment of the present application further provides a computer program product, which includes executable instructions. When the executable instructions are executed on a computer, the computer executes some or all of the steps in the above-mentioned method embodiment.

[0091] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and subsequent associated 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 be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0092] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0093] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0094] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0095] The above is only a specific implementation of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A carbonate rock optimization interpretation method integrating element logging and nuclear magnetic logging, characterized in that: include: Step S1: Determine the main mineral types through element logging interpretation; Step S2: extracting the dry rock mineral proportion of the main mineral type through element logging interpretation, and using the dry rock mineral proportion of the main mineral type as the constraint curve of the main mineral type; Step S3: using a particle density equation, converting the dry rock mineral content into the mineral content of the wet rock; Step S4: extracting the effective porosity of the wet rock as a constraint curve of oil and water volume through nuclear magnetic logging interpretation; Step S5: Establishing a carbonate logging interpretation model by combining the constraint curves of the main mineral types and the constraint curves of oil and water volumes and gamma, deep and shallow conductivity, acoustic wave, density, neutron, and photoelectric absorption cross-section indexes; Step S6: Optimizing the carbonate rock logging interpretation model until the forward fitting of the logging curve meets the error requirement, thereby obtaining the optimized carbonate rock logging interpretation model; Step S7: using the optimized carbonate logging interpretation model to obtain optimized logging interpretation results.

2. The carbonate rock optimization interpretation method integrating element logging and nuclear magnetic logging according to claim 1, characterized in that: The mineral types include: illite, kaolinite, feldspar, quartz, calcite, and dolomite.

3. The carbonate rock optimization interpretation method integrating element logging and nuclear magnetic logging according to claim 1, characterized in that: The dry rock mineral percentage refers to the rock mineral percentage without pores.

4. The carbonate rock optimization interpretation method integrating element logging and nuclear magnetic logging according to claim 1, characterized in that: The mineral content of the wet rock represents the mineral content of the rock including the pores.

5. The carbonate rock optimization interpretation method integrating element logging and nuclear magnetic logging according to claim 1, characterized in that: The mineral content of the wet rock is calculated as follows: Among them, Wet Vol% represents the mineral content of wet rock, Dry Weight% represents the mineral ratio of dry rock, PhiT represents the total porosity, Rock Grain Density represents the rock density, and Mineral Grain Density represents the mineral density.

6. The carbonate rock optimization interpretation method integrating element logging and nuclear magnetic logging according to claim 1, characterized in that: The forward equation of the carbonate logging interpretation model is as follows: r ma1 V1+r ma2 V2+…+r mam V m =ρ b DT ma1 V1+DT ma2 V2+…+DT mam V m =DT b V1+V2+…+V m =1 Among them, ρ ma1 , ρ ma2 , ..., ρ mam Respectively represent the skeleton density of the first, second, up to the mth mineral or fluid; V1, V2, ..., V m Respectively represent the volume ratio of the first, second, up to the mth mineral or fluid; ρ b Indicates logging density; DT ma1 , DT ma2 , ..., DT mam Respectively represent the skeleton sound waves of the first, second, and up to the mth mineral or fluid; DT b Indicates well sound waves.

7. The carbonate rock optimization interpretation method integrating element logging and nuclear magnetic logging according to claim 1, characterized in that: The logging interpretation results include mineral content, porosity, etc.

8. A carbonate rock optimization interpretation device integrating element logging and nuclear magnetic logging, characterized in that: include: Mineral type determination module, used to determine the main mineral types through element logging interpretation; A dry rock mineral ratio acquisition module is used to extract the dry rock mineral ratio of the main mineral type through element logging interpretation, and use the dry rock mineral ratio of the main mineral type as a constraint curve of the main mineral type; A wet rock mineral content acquisition module, used to convert the dry rock mineral ratio into the wet rock mineral content by using a particle density equation; An effective porosity acquisition module is used to extract the effective porosity of the wet rock as a constraint curve of oil and water volume through nuclear magnetic logging interpretation; A model building module is used to build a carbonate logging interpretation model by combining the constraint curves of the main mineral types and the constraint curves of oil and water volumes and gamma, deep and shallow conductivity, acoustic wave, density, neutron, and photoelectric absorption cross-section indexes; A model optimization module is used to optimize the carbonate well logging interpretation model until the forward fitting of the well logging curve meets the error requirement, thereby obtaining the most optimized carbonate well logging interpretation model; The result acquisition module is used to obtain the optimized well logging interpretation result according to the optimized carbonate well logging interpretation model.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, and when the instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

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