A method, device, equipment and medium for quickly dividing lithofacies and establishing a lithofacies pattern library

Abstract

The present application provides a kind of fast division lithofacies and the method, device, equipment and medium of establishing lithofacies pattern library, the method includes: obtaining the key characteristic parameter of carbonate rock thin section image;According to the key characteristic parameter of thin section image, in combination with the basic basis of lithofacies division, thin section image of carbonate rock is progressively divided, and independent number is given;Based on the lithofacies type and number of progressively divided, in combination with the basic information of carbonate rock thin section image, the lithofacies pattern library of carbonate rock thin section is established. By quickly dividing lithofacies and establishing lithofacies pattern library, the efficiency of lithofacies identification can be significantly improved, the influence of human factors is reduced, not only data support is provided for subsequent thin section intelligent identification, also important support is provided for oil and gas exploration work.

Description

Technical Field

[0001] This invention belongs to the field of petrology technology in geological exploration, and relates to a method, apparatus, equipment and medium for rapidly classifying lithofacies and establishing a lithofacies graphic library. Background Technology

[0002] Thin section identification is a key technique in geological research for determining the mineral composition, structure, and genesis of rocks. It is particularly important in carbonate rock studies, where thin section analysis is crucial for interpreting sedimentary environments, evaluating reservoirs, and exploring for oil and gas. Traditional thin section identification relies primarily on the expertise and experience of specialists. However, this method has several drawbacks: firstly, it is time-consuming and labor-intensive, requiring significant time and effort; secondly, it is inefficient, failing to meet the needs of large-scale identification; and thirdly, the results are susceptible to subjective influences, with different experts potentially reaching different conclusions, affecting the accuracy and reliability of the identification.

[0003] With the development of big data technology, the geological research field is constantly exploring how to utilize new technologies to improve the efficiency and accuracy of thin section identification. Against this backdrop, rapidly establishing thin section petrographic image libraries and promoting intelligent thin section identification has become a development trend in traditional petrology research. Intelligent identification, through the use of image processing, machine learning, and other technologies, can achieve rapid and accurate identification of thin sections, avoiding the subjectivity of manual identification and improving identification efficiency.

[0004] However, there is currently no method for rapidly segmenting carbonate rock thin sections and establishing graphic libraries, failing to systematically address the issues of large-scale and standardized generation of graphic libraries. Therefore, there is an urgent need to develop a method for rapidly segmenting carbonate rock thin sections and establishing graphic libraries, providing reliable data support for subsequent intelligent identification and promoting the digital and intelligent development of geological research. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the present invention aims to provide a method, apparatus, equipment, and medium for rapidly classifying lithofacies and establishing a lithofacies graphic library. By rapidly classifying lithofacies and establishing a lithofacies graphic library, the efficiency of lithofacies identification can be significantly improved, and the influence of human factors can be reduced. This not only provides data support for subsequent intelligent thin section identification but also provides important support for oil and gas exploration.

[0006] To achieve this objective, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a method for rapidly classifying lithofacies and establishing a lithofacies graphic library, the method comprising:

[0008] Obtain key feature parameters from thin section images of carbonate rocks;

[0009] Based on the key feature parameters of the thin section images and the basic criteria for lithofacies classification, the carbonate rock thin section images are classified step by step and assigned independent numbers.

[0010] Based on the hierarchical classification of lithofacies types and their numbers, and combined with the basic information of carbonate thin section images, a lithofacies graphic library of carbonate thin sections is established.

[0011] The method provided by this invention comprehensively considers parameters such as rock structure, mineral composition, grain type, grain size, principal component content, and crystal size of carbonate rocks, and combines these with the basic criteria for lithofacies classification and the basic information of thin section images to construct a method for rapidly classifying lithofacies and establishing a lithofacies image library. The method first obtains the key feature parameters of carbonate rock thin section images, then, based on the basic criteria for lithofacies classification, classifies the carbonate rock thin section images hierarchically and assigns them independent numbers, and finally, based on the basic information of the carbonate rock thin section images, establishes a lithofacies image library for carbonate rock thin sections.

[0012] It should be noted that by classifying lithofacies types and assigning numbers at each level, combined with basic image information, lithofacies types can be identified quickly and accurately, thereby reducing the time and errors associated with manual identification. The lithofacies image library established on this basis can significantly improve the efficiency of lithofacies identification. The image library provides researchers with standardized references, helping to improve identification accuracy and reduce the influence of human factors. Furthermore, the image library centrally stores a large number of images and information, providing data support not only for subsequent intelligent thin-section identification but also for important support in oil and gas exploration.

[0013] Preferably, the key characteristic parameters include rock structure, mineral composition content, particle type, particle size, main component content of particles, and crystal size.

[0014] Preferably, the step-by-step division of the carbonate rock thin section image includes:

[0015] The first-level classification of carbonate rock thin section images is performed to obtain the first-level lithofacies classification.

[0016] Each primary lithofacies is further subdivided into secondary lithofacies to obtain secondary lithofacies.

[0017] Each secondary lithofacies is further subdivided into tertiary lithofacies to obtain a tertiary lithofacies classification.

[0018] Preferably, the first-level division includes: classifying the carbonate rock thin section image according to the rock structure and mineral composition content, further subdividing it into granular carbonate rocks, bonded carbonate rocks, crystalline rocks, and other rocks.

[0019] Preferably, the second-level classification of the granular carbonate rock includes: classifying the granular carbonate rock into granular limestone and granular dolomite based on the content of the main granular components and mineral components, further subdividing it into granular limestone and granular dolomite; mudstone and mudstone dolomite; granular mudstone and granular mudstone dolomite; micritic limestone and micritic dolomite.

[0020] Preferably, the second-level classification of the bonded carbonate rock includes: further subdividing the bonded carbonate rock into microbial rocks and bioherm rocks based on rock-forming organisms and rock structure.

[0021] Preferably, the second-level classification of the crystalline rock includes: classifying the crystalline rock according to its mineral composition content, further subdividing it into crystalline limestone and crystalline dolomite.

[0022] Preferably, the second-level classification of the other rocks includes: classifying the other rocks according to their mineral composition content, further subdividing them into gypsum-salt rocks, siliceous rocks, and transitional rocks.

[0023] Preferably, the third-level classification includes: classifying the secondary-level lithofacies based on particle type, particle size, fossil type, crystal size, mineral composition content, or rock microstructure characteristics.

[0024] Preferably, the basic information includes the region, well number, stratigraphy, depth, section type, and photograph type.

[0025] Preferably, the establishment of the petrographic image library for carbonate rock thin sections includes:

[0026] Based on the hierarchical classification of lithofacies types and numbers, and combined with the basic information from the collected thin section images, initial data were established.

[0027] Based on the requirements of deep learning algorithms, thin sections of carbonate rock castings were ground, and multi-angle photographs with scale bars were taken under a polarizing microscope in various optical observation modes to establish a rich image library of carbonate rock thin sections. An efficient retrieval mechanism was established through indexing and weighting.

[0028] Secondly, the present invention provides a device for rapidly classifying lithofacies and establishing a lithofacies graphic library, the device comprising:

[0029] The thin section image parameter acquisition module is used to acquire key feature parameters of carbonate rock thin section images;

[0030] The thin section image segmentation and location determination module is used to segment carbonate rock thin section images step by step based on key feature parameters of the thin section images and the basic criteria for lithofacies segmentation, and assign them independent numbers.

[0031] A lithofacies library module is established to create a lithofacies library of carbonate thin sections based on hierarchically classified lithofacies types and numbers, combined with basic information from carbonate thin section images.

[0032] Thirdly, the present invention provides an electronic device, the electronic device comprising:

[0033] At least one processor; and

[0034] A memory communicatively connected to the at least one processor; wherein,

[0035] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for rapidly classifying lithofacies and establishing a lithofacies graphic library as described in the first aspect.

[0036] Fourthly, the present invention provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, the computer instructions being used to cause a processor to execute the method described in the first aspect for rapidly classifying lithofacies and establishing a lithofacies graphic library.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] The method provided by this invention comprehensively considers parameters such as rock structure, mineral composition, grain type, grain size, principal component content, and crystal size of carbonate rocks, and combines these with the basic criteria for lithofacies classification and basic information from thin section images to construct a method for rapidly classifying lithofacies and establishing a lithofacies image library. The method first obtains the key feature parameters of carbonate rock thin section images, then classifies the carbonate rock thin section images hierarchically based on the basic criteria for lithofacies classification and assigns them independent numbers, and finally establishes a lithofacies image library of carbonate rock thin sections based on the basic information of the carbonate rock thin section images. This method considers more comprehensive factors, significantly improves the efficiency of lithofacies identification, reduces the influence of human factors, and not only provides data support for subsequent intelligent thin section identification but also provides important support for oil and gas exploration. Attached Figure Description

[0039] Figure 1 This is a flowchart of the method for rapidly classifying lithofacies and establishing a lithofacies graphic library provided in Example 1.

[0040] Figure 2 These are the 12 typical carbonate rock thin-section lithofacies images provided in Example 1;

[0041] Among them, 1-granular limestone; 2-mudstone; 3-granular mudstone; 4-mimicryl limestone; 5-granular dolomite; 6-mudstone dolomite; 7-granular mudstone dolomite; 8-mimicryl dolomite; 9-microbial rock; 10-biogenic reef rock; 11-crystalline dolomite; 12-gypsum-salt rock.

[0042] Figure 3 This is a schematic diagram of the device for rapidly classifying lithofacies and establishing a lithofacies graphic library provided in Example 2.

[0043] Figure 4 This is a schematic diagram of the electronic device structure provided in Example 3 for a method of rapidly classifying lithofacies and establishing a lithofacies graphic library. Detailed Implementation

[0044] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0045] It should be noted that the terms "first," "second," "candidate," "target," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0046] Example 1

[0047] This embodiment provides a method for rapidly classifying lithofacies and establishing a lithofacies graphic library. This method is applicable to rapidly classifying lithofacies and establishing a lithofacies graphic library based on key feature parameters of carbonate rock thin section images and the basic criteria for lithofacies classification. This method can be executed by a device for rapidly classifying lithofacies and establishing a lithofacies graphic library. The device for rapidly classifying lithofacies and establishing a lithofacies graphic library can be implemented in hardware and / or software and can be configured in an electronic device that has the capability for rapidly classifying lithofacies and establishing a lithofacies graphic library.

[0048] likeFigure 1 As shown, the method provided in this embodiment includes:

[0049] S101. Obtain key feature parameters of carbonate rock thin section images.

[0050] In carbonate rock research, thin section analysis is of great significance for sedimentary environment interpretation, reservoir evaluation, and oil and gas exploration. Currently, thin section identification mainly relies on the analysis and judgment of professionals based on their expertise and experience, which results in low identification efficiency. There is currently a lack of rapid segmentation and graphic library development for carbonate rock thin sections.

[0051] In this embodiment, a thin section of carbonate rock without a cover slide is selected and stained with alizarin red solution. Then, the thin section is photographed or scanned under a microscope or a thin section scanning system to obtain a thin section image that meets the requirements, thereby obtaining the key feature parameters of the carbonate rock thin section image.

[0052] Specifically, this embodiment obtains a series of key characteristic parameters such as rock structure, mineral composition content, grain type, grain size, main component content of grains, and crystal size, providing a basis for subsequent thin section image segmentation.

[0053] S102. Based on the key feature parameters of the thin section images and the basic criteria for lithofacies classification, the carbonate rock thin section images are classified step by step and assigned independent numbers.

[0054] In this embodiment, the basic criteria for classifying carbonate rock facies mainly include sedimentary structure and texture (such as grain type and sedimentary structure), composition and mineral characteristics (such as mineral composition and chemical composition), biostratigraphy and paleontological characteristics (such as biological communities and biological remains), sedimentary environment and paleogeographic background (such as sedimentary microfacies type and paleogeographic location), and diagenesis and epigenesis (such as diagenetic type and the influence of epigenesis).

[0055] By classifying and assigning independent numbers to each level, the accuracy and operability of lithofacies classification are improved, which also facilitates subsequent geological research and data management, providing a scientific, standardized and efficient technical means for in-depth research on carbonate rocks.

[0056] Specifically, in this embodiment, the carbonate rock thin section image is divided into levels according to the structural composition parameters of the carbonate rock, including:

[0057] Step A: Perform first-level classification on the thin section images of carbonate rocks to obtain the first-level lithofacies classification;

[0058] Step B: Perform a second-level subdivision on each primary lithofacies to obtain a secondary lithofacies;

[0059] Step C: Perform a third-level classification on each secondary-level lithofacies to obtain a tertiary-level lithofacies.

[0060] In step A, the thin section images of carbonate rocks are classified into first-level subdivisions based on rock structure and mineral composition, further subdivided into granular carbonate rocks, bonded carbonate rocks, crystalline rocks, and other rocks.

[0061] Specifically, granular carbonate rocks are a type of carbonate rock primarily supported by grains, with the spaces between the grains typically filled by cement or micritic matrix; their main mineral components include calcite, dolomite, and clay minerals. Cemented carbonate rocks are mainly composed of a biogenic framework, a bonding matrix, and cement; their main mineral components include calcite, aragonite, dolomite, and clay minerals. Crystalline rocks are primarily formed by secondary recrystallization, with grains as the main structure; their main mineral components include calcite, dolomite, and quartz. Other types of carbonate rocks are designated as "Other Rocks."

[0062] In step B, the granular carbonate rocks are further subdivided into granular limestone and granular dolomite based on the content of the main component and mineral components of the granules, and then further subdivided into granular limestone and granular dolomite; mudstone and mudstone dolomite; granular mudstone and granular mudstone dolomite; micritic limestone and micritic dolomite.

[0063] In step B, the bonded carbonate rocks are further subdivided into microbial rocks and bioherm rocks based on the rock-forming organisms and rock structure.

[0064] In step B, the crystalline rocks are further subdivided into crystalline limestone and crystalline dolomite based on their mineral composition.

[0065] In step B, other rocks are further classified into gypsum-salt rocks, siliceous rocks, and transitional rocks based on their mineral composition.

[0066] In step C, the secondary-classified lithofacies are further classified into a third level based on particle type, particle size, fossil type, crystal size, mineral composition content, or rock microstructure characteristics.

[0067] Specifically, the lithofacies images of typical carbonate thin sections were classified according to the above classification criteria. The results are shown in Table 1.

[0068] Table 1

[0069]

[0070]

[0071] Furthermore, taking 12 typical thin-section lithofacies images of carbonate rocks as an example (e.g.) Figure 2 As shown in Table 2, the data were divided according to the above classification criteria.

[0072] Table 2

[0073]

[0074] In this embodiment, each type of carbonate rock is assigned a unique number based on the segmentation results of the thin section images.

[0075] S103. Based on the hierarchical classification of lithofacies types and numbers, and combined with the basic information of carbonate thin section images, establish a lithofacies graphic library of carbonate thin sections.

[0076] The establishment of the carbonate rock thin-section lithofacies image library is achieved using a thin-section lithofacies identification system. This system includes a data storage system, a data management system, a user terminal, and a server. The data storage system, data management system, and user terminal are all connected to the server. The data management system manages the graphic image data and basic information data of the carbonate rock thin sections. The user terminal pushes the thin-section lithofacies identification results and associated basic information data of the carbonate rock thin sections to the user.

[0077] By classifying lithofacies types and assigning numbers at each level, and combining this with basic image information, lithofacies types can be identified quickly and accurately, thus reducing the time and errors associated with manual identification. The lithofacies image library established on this basis significantly improves the efficiency of lithofacies identification. The library provides researchers with standardized references, helping to improve identification accuracy and reduce the influence of human factors. Furthermore, the library centrally stores a large amount of images and information, providing data support not only for subsequent intelligent thin-section identification but also for oil and gas exploration.

[0078] Specifically, this embodiment acquires a series of basic information such as the region of entry, well number, stratigraphic position, depth, thin section type, and photograph type of carbonate rock thin section images, providing basic information for subsequent thin section image division.

[0079] Based on the hierarchical classification and numbering of lithofacies in S102, and combined with the basic information of collected thin section images, initial data was established. Then, according to the requirements of deep learning algorithms, carbonate rock castings were ground, and multi-angle photographs with scale bars were taken under a polarizing microscope in various optical observation modes to establish a rich carbonate rock thin section image library. An efficient retrieval mechanism was established through indexing and weighting. The optical observation modes include, but are not limited to, single-polarized light and / or orthogonal light.

[0080] The method provided in this embodiment comprehensively considers parameters such as rock structure, mineral composition, grain type, grain size, principal component content, and crystal size of carbonate rocks, and combines them with the basic criteria for lithofacies classification and basic information from thin section images to construct a method for rapidly classifying lithofacies and establishing a lithofacies image library. The method first obtains the key feature parameters of carbonate rock thin section images, then classifies the carbonate rock thin section images hierarchically based on the basic criteria for lithofacies classification and assigns them independent numbers, and finally establishes a lithofacies image library of carbonate rock thin sections based on the basic information of the carbonate rock thin section images. This method considers more comprehensive factors, significantly improves the efficiency of lithofacies identification, reduces the influence of human factors, and not only provides support for subsequent intelligent thin section identification but also provides important support for oil and gas exploration work.

[0081] Example 2

[0082] This embodiment provides a device for rapidly classifying lithofacies and establishing a lithofacies graphic library, such as... Figure 3 As shown, the device includes: a module 201 for acquiring thin-slice image parameters, a module 202 for dividing and determining the position of thin-slice images, and a module 203 for establishing a graphics library. Among them,

[0083] The thin section image parameter acquisition module 201 is used to acquire key feature parameters of carbonate rock thin section images;

[0084] The thin section image segmentation and location determination module 202 is used to segment carbonate rock thin section images step by step according to the key feature parameters of the thin section images and the basic criteria for lithofacies segmentation, and assign them independent numbers.

[0085] A graphic library module 203 is established to create a lithofacies graphic library of carbonate thin sections based on the hierarchically classified lithofacies types and numbers, combined with the basic information of carbonate thin section images.

[0086] The apparatus provided in this embodiment can execute the method for rapidly classifying lithofacies and establishing a lithofacies graphic library provided in this embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0087] Example 3

[0088] This embodiment provides an electronic device for implementing a method for rapidly classifying lithofacies and establishing a lithofacies graphic library, such as... Figure 4As shown, this electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. This electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present application described and / or claimed herein.

[0089] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0090] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as secondary storage area, optical disc, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0091] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for rapidly classifying lithofacies and building lithofacies graphic libraries.

[0092] In some embodiments, the method for rapidly classifying lithofacies and establishing a lithofacies graphic library can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for rapidly classifying lithofacies and establishing a lithofacies graphic library described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for rapidly classifying lithofacies and establishing a lithofacies graphic library by any other suitable means (e.g., by means of firmware).

[0093] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0094] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable target-determining device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0095] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0096] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0097] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0098] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0099] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired information of the technical solution of this application can be achieved, and this is not limited herein.

Claims

1. A method for rapidly classifying lithofacies and establishing a lithofacies graphic library, characterized in that, The method includes: Obtain key feature parameters from thin section images of carbonate rocks; Based on the key feature parameters of the thin section images and the basic criteria for lithofacies classification, the carbonate rock thin section images are classified step by step and assigned independent numbers. Based on the hierarchical classification of lithofacies types and their numbers, and combined with the basic information of carbonate thin section images, a lithofacies graphic library of carbonate thin sections is established.

2. The method according to claim 1, characterized in that, The key characteristic parameters include rock structure, mineral composition content, grain type, grain size, main component content of grains, and crystal size.

3. The method according to claim 1 or 2, characterized in that, The step-by-step division of carbonate rock thin section images includes: The first-level classification of carbonate rock thin section images is performed to obtain the first-level lithofacies classification. Each primary lithofacies is further subdivided into secondary lithofacies to obtain secondary lithofacies. Each secondary lithofacies is further subdivided into tertiary lithofacies to obtain a tertiary lithofacies classification.

4. The method according to claim 3, characterized in that, The first-level classification includes: classifying carbonate rock thin section images according to rock structure and mineral composition, further subdividing them into granular carbonate rocks, bonded carbonate rocks, crystalline rocks, and other rocks.

5. The method according to claim 3 or 4, characterized in that, The second-level classification of the granular carbonate rocks includes: based on the content of the main granular components and the mineral composition, the granular carbonate rocks are further subdivided into granular limestone and granular dolomite; mudstone and mudstone dolomite; granular mudstone and granular mudstone dolomite; micritic limestone and micritic dolomite. Preferably, the second-level classification of the bonded carbonate rock includes: classifying the bonded carbonate rock into microbial rocks and bioherm rocks based on rock-forming organisms and rock structure; Preferably, the second-level classification of the crystalline rock includes: classifying the crystalline rock according to its mineral composition content, further subdividing it into crystalline limestone and crystalline dolomite; Preferably, the second-level classification of the other rocks includes: classifying the other rocks according to their mineral composition content, further subdividing them into gypsum-salt rocks, siliceous rocks, and transitional rocks.

6. The method according to any one of claims 3-5, characterized in that, The third-level classification includes: classifying the secondary-level lithofacies based on particle type, particle size, fossil type, crystal size, mineral composition content, or rock microstructure characteristics.

7. The method according to any one of claims 1-6, characterized in that, The basic information includes the region, well number, stratigraphy, depth, section type, and photograph type. Preferably, the establishment of the petrographic image library for carbonate rock thin sections includes: Based on the hierarchical classification of lithofacies types and numbers, and combined with the basic information from the collected thin section images, initial data were established. Based on the requirements of deep learning algorithms, thin sections of carbonate rock castings were ground, and multi-angle photographs with scale bars were taken under a polarizing microscope in various optical observation modes to establish a rich image library of carbonate rock thin sections. An efficient retrieval mechanism was established through indexing and weighting.

8. A device for rapidly classifying lithofacies and establishing a lithofacies graphic library, characterized in that, The device includes: The thin section image parameter acquisition module is used to acquire key feature parameters of carbonate rock thin section images; The thin section image segmentation and location determination module is used to segment carbonate rock thin section images step by step based on key feature parameters of the thin section images and the basic criteria for lithofacies segmentation, and assign them independent numbers. A lithofacies library module is established to create a lithofacies library of carbonate thin sections based on hierarchically classified lithofacies types and numbers, combined with basic information from carbonate thin section images.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for rapidly classifying lithofacies and establishing a lithofacies graphic library as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for rapidly classifying lithofacies and establishing a lithofacies graphic library as described in any one of claims 1-7.

Images