Lithology analysis method, apparatus and device for rock debris, and medium

Abstract

The invention discloses a lithology analysis method, device and equipment for rock debris and a medium. The method comprises the following steps: segmenting a white light image of a rock debris sample to obtain at least two sub-images; the rock debris sample contains at least two rock debris particles, and each subgraph contains at least one rock debris particle; for each sub-graph, determining the category of the sub-graph according to the quantitative proportion of pixel points of different categories of rock debris particles in the sub-graph; and determining the overall category of the rock debris sample according to the voting result of the category of each sub-graph. According to the embodiment of the invention, the accuracy of rock debris lithology analysis can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of lithology analysis of rock cuttings, and in particular to a lithology analysis method, device, equipment and medium for rock cuttings. Background Art

[0002] During underground drilling operations, such as those in oil and gas exploration and development, the drilling process generates rock cuttings. These cuttings, crucial for understanding the properties and structure of the subsurface rock formations, are often manually inspected and analyzed. The typical process involves separating the cuttings on a vibrating screen, cleaning them, and capturing images for analysis.

[0003] In recent years, the industry has begun exploring automated or semi-automated rock cuttings evaluation to reduce labor costs and improve analysis efficiency. However, this automation process faces significant technical obstacles. These include: first, the high diversity of rock cuttings in size, shape, surface texture, and color; second, the tendency for rock cuttings to overlap and adhere to their edges during imaging; and third, artifacts such as varying lighting and shadows can occur in images collected by different devices.

[0004] In early studies, scholars have tried a variety of image segmentation techniques, including watershed algorithms and normalized cut methods to solve these problems. Among them, the watershed algorithm has become a popular choice due to its simple calculation, efficient operation, and wide support of open source libraries. This algorithm usually requires morphological processing before performing refinement operations. However, its segmentation effect is extremely sensitive to hyperparameter settings and is prone to under-segmentation or over-segmentation problems. In addition, when the target objects vary greatly in size, the design of the preprocessing link often faces great challenges, and it is difficult to ensure a stable segmentation effect. Despite these research attempts, the accuracy of automatic segmentation and recognition of rock chip images has not yet reached the ideal level, resulting in severe challenges in the development and popularization of automated analysis systems. Summary of the Invention

[0005] The present invention provides a rock cuttings lithology analysis method, device, equipment and medium to improve the accuracy of rock cuttings lithology analysis.

[0006] According to one aspect of the present invention, a method for lithology analysis of rock cuttings is provided, comprising: Segmenting a white light image of a rock chip sample to obtain at least two sub-images; the rock chip sample contains at least two rock chip particles, and each sub-image contains at least one rock chip particle; For each sub-image, determining the category of the sub-image according to the ratio of the number of pixels of different categories of rock debris particles in the sub-image; The overall category of the rock cuttings sample is determined according to the voting results of the categories of each sub-graph.

[0007] According to another aspect of the present invention, there is provided a lithology analysis device for rock cuttings, comprising: a segmentation module for segmenting the white light image of the rock cuttings sample to obtain at least two sub-images; the rock cuttings sample contains at least two rock cutting particles, and each sub-image contains at least one rock cutting particle; A classification module, configured to determine the category of each sub-image according to the ratio of the number of pixels of different categories of rock debris particles in the sub-image; The determination module is configured to determine the overall category of the rock cuttings sample according to the voting results of the categories of each sub-graph.

[0008] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the lithology analysis method of rock cuttings according to any embodiment of the present invention.

[0009] According to another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the rock cuttings lithology analysis method described in any embodiment of the present invention.

[0010] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the rock cuttings lithology analysis method according to any embodiment of the present invention when executed.

[0011] The embodiments of the present invention combine white light image multi-region segmentation detection and voting mechanism to solve the problems of low efficiency and strong subjectivity caused by traditional rock fragment lithology analysis relying on manual experience, realize accurate identification of rock fragment lithology categories, and provide intelligent tools for geological exploration and oil and gas reservoir evaluation.

[0012] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0014] Figure 1 This is a flow chart of a method for lithologic analysis of rock cuttings provided according to one embodiment of the present invention; Figure 2A is a flow chart of a method for lithologic analysis of rock cuttings provided according to another embodiment of the present invention; Figure 2B is a schematic diagram of a lithology analysis process of rock cuttings provided according to another embodiment of the present invention; Figure 3 1 is a schematic structural diagram of a rock cuttings lithology analysis device provided according to another embodiment of the present invention; Figure 4 It is a schematic structural diagram of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0016] It should be noted that the terms "first," "second," and the like in the present invention are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatuses.

[0017] Figure 1 This is a flow chart of a rock cuttings lithology analysis method provided by one embodiment of the present invention. This embodiment is applicable to situations where it is necessary to analyze the rock cuttings sample type and whether it contains oil and other lithology. The method can be performed by a rock cuttings lithology analysis device, which can be implemented in the form of hardware and / or software. The device can be configured in an electronic device with corresponding data processing capabilities, such as a rock cuttings lithology analysis system. Figure 1 As shown, the method includes: S110 , segmenting the white light image of the rock cuttings sample to obtain at least two sub-images.

[0018] S120 : For each sub-image, determine the category of the sub-image according to the ratio of the number of pixels of different categories of rock debris particles in the sub-image.

[0019] S130 : Determine the overall category of the rock cuttings sample according to the voting results of the categories of each sub-graph.

[0020] There are at least two rock chip particles in the rock chip sample, and each sub-graph contains at least one rock chip particle.

[0021] Specifically, a rock cutting sample is placed on a sample tray at the imaging station, and the system turns on a white light source above the rock cutting sample. Under the illumination of the white light source, the industrial camera is controlled to capture a white light image of the rock cutting sample and return it to the system.

[0022] The system preprocesses the white-light image sequentially, performing image denoising, brightness equalization, image white balancing, and edge cropping. Edge cropping uses a cross-platform computer vision and machine learning software library to detect the rock chip image's outline boundaries, intercepting the main rock chip area with a minimum bounding rectangle and removing background interference pixels. The preprocessed white-light image is then segmented, for example, using an N×N grid for equal division. The segmentation process ensures that each sub-image contains at least one rock chip particle.

[0023] Each sub-image is segmented at the pixel level using a pre-trained semantic segmentation model to determine the category of each rock fragment particle in the sub-image (e.g., mudstone, sandstone, gypsum, etc.). The ratio of the number of pixels of different rock fragment particle categories in each sub-image is then counted, and the sub-image category is determined based on this ratio.

[0024] Voting is performed according to the category of each sub-image, and the overall category of the rock cuttings sample photographed by the industrial camera is determined based on the voting results.

[0025] The embodiments of the present invention combine white light image multi-region segmentation detection and voting mechanism to solve the problems of low efficiency and strong subjectivity caused by traditional rock fragment lithology analysis relying on manual experience, realize accurate identification of rock fragment lithology categories, and provide intelligent tools for geological exploration and oil and gas reservoir evaluation.

[0026] Figure 2A This is a flow chart of a method for lithologic analysis of rock fragments provided by another embodiment of the present invention. This embodiment is optimized and improved on the basis of the above embodiment. Figure 2A As shown, the method includes: S210 , segmenting the white light image of the rock cuttings sample to obtain at least two sub-images.

[0027] S220 : For each sub-image, perform pixel-level annotation on each rock chip particle in the sub-image using a semantic segmentation model to obtain rock chip pixel points and categories of each rock chip pixel point in the sub-image.

[0028] S230 , determining the proportion of the number of rock chip pixels in each category to the total number of rock chip pixels according to the category of each rock chip pixel in the sub-image; and determining the category with the highest proportion that is greater than a proportion threshold as the category of the sub-image.

[0029] The total number of rock chip pixels is the sum of the number of rock chip pixels in each category.

[0030] Specifically, pixel-level annotation is performed on each rock chip particle in the image to obtain rock chip pixels and non-rock chip pixels in the sub-image, as well as the labeled category of each rock chip pixel (such as mudstone, sandstone, gypsum, quartz, dolomite, etc.).

[0031] Count the proportion of all rock chip pixels marked as mudstone to the total number of rock chip pixels (P1); count the proportion of all rock chip pixels marked as sandstone to the total number of rock chip pixels (P2); count the proportion of all rock chip pixels marked as gypsum to the total number of rock chip pixels (P3); and so on to statistically identify the corresponding proportions of all categories.

[0032] Each statistically obtained ratio is initially compared against a preset ratio threshold. If any ratio is less than the ratio threshold, it is discarded. If it is greater than the ratio threshold, it is retained and further compared to distinguish between primary and secondary components. After completing the initial comparison of all ratios, the remaining ratios are compared in size, and the category corresponding to the highest ratio is determined as the subgraph category. For example, if P1>P2>P3, the subgraph category is determined to be sand-bearing mudstone; if P3>P2>P1, the subgraph category is determined to be gypsum-bearing mudstone.

[0033] S240: Determine the overall category of the rock cuttings sample according to the voting results of the categories of each sub-graph.

[0034] Based on the above embodiment, optionally, determining the overall category of the rock cuttings sample according to the voting results of the categories of each subgraph includes: Based on a Figure 1 The voting principle of the ticket is to determine the number of votes for each category based on the category of each sub-graph; If there is no candidate category tied for first place in vote count, the category with the highest number of votes is determined as the overall category of the rock chip sample.

[0035] Specifically, based on a Figure 1The voting principle is to vote on the categories of each sub-image and determine the number of votes for each category. Similar to the process of determining sub-image categories, the votes for each category are initially compared to identify the category with a vote count exceeding a preset threshold. These categories are then further compared to each other to determine the category with the highest number of votes. If there is only one category with the highest number of votes (i.e., there is no tie for first place candidate category), the category with the highest number of votes is directly determined as the overall category of the rock cuttings sample.

[0036] Based on the above embodiment, optionally, determining the overall category of the rock cuttings sample according to the voting results of the categories of each subgraph further includes: If there are candidate categories that are tied for first place in the number of votes, the average number of rock chip pixels of the rock chip particles corresponding to each candidate category is calculated; The candidate class with the highest average number of rock chip pixels is determined as the overall class of the rock chip sample.

[0037] Specifically, if there are multiple categories with the highest number of votes, that is, if there are at least two candidate categories tied for first place, further comparison of the candidate categories is required. For each candidate category, the number of rock debris particles labeled with that category and the total number of rock debris pixels labeled with that category in all sub-images (i.e., the complete white light image) are counted. The quotient of these two numbers is used to obtain the average number of rock debris pixels for the rock debris particles corresponding to the candidate category, that is, the average number of pixels per rock debris particle.

[0038] The average number of rock chip pixels reflects the average size of rock chip particles. The higher the average number of rock chip pixels, the larger the average size of the rock chip particles. The candidate class with the highest average number of rock chip pixels, that is, the largest average size of rock chip particles, is determined as the overall class of the rock chip sample.

[0039] S250: Determine whether the rock cuttings sample contains oil based on the fluorescence characteristics of the fluorescence in the ultraviolet image of the rock cuttings sample.

[0040] The fluorescence characteristics include fluorescence intensity and fluorescence range.

[0041] Specifically, after capturing the white light image, the system turns off the white light source and turns on the UV light source above the rock cuttings sample. Under the UV light, the industrial camera captures a UV image of the rock cuttings sample and returns it to the system. The system preprocesses the UV image. Unlike white light images, this preprocessing only involves image denoising.

[0042] The fluorescence intensity and fluorescence range of the fluorescence in the ultraviolet image are analyzed. If both the fluorescence intensity and the fluorescence range exceed the normal range, it is determined that the rock cuttings sample contains oil; if they do not exceed the normal range, it is determined that the rock cuttings sample does not contain oil.

[0043] Based on the above embodiment, optionally, after determining whether the rock cuttings contain oil based on the fluorescence intensity and fluorescence range of the fluorescence in the ultraviolet image of the rock cuttings, the method further includes: If the rock chip sample contains oil, determining similar cases of the rock chip sample from a correlation database based on the fluorescence characteristics and the overall category; The oil-containing probability of the rock cuttings sample is determined according to the similarity between the ultraviolet image and the fluorescence features in the similar case.

[0044] Specifically, a pre-built rock cuttings category-rock cuttings oil content association database stores ultraviolet images of various oil-containing rock cuttings as selectable similar cases.

[0045] When it is necessary to specifically analyze the oil-bearing probability of a lithologic sample, appropriate UV images are selected from the associated database as similar cases based on the fluorescence characteristics and overall category.

[0046] Calculate the similarity between the fluorescence characteristics of the fluorescence in the similar case and the fluorescence characteristics in the UV image of the rock chip sample, and calculate the oil-containing probability of the rock chip sample based on the similarity. The higher the similarity, the higher the probability of containing oil, and the lower the similarity, the lower the probability of containing oil.

[0047] Based on the above embodiment, optionally, the method further includes: The semantic segmentation model is incrementally learned according to the correction results of the rock chip pixels and the categories of the rock chip pixels by the user in the visual interface.

[0048] Specifically, such as Figure 2B As shown, the system's hardware components, in addition to the aforementioned industrial tray for holding rock cuttings samples, an industrial camera for capturing white-light / violet-light images, a white-light source for illumination, and a violet light source for stimulating fluorescence, also include a touchscreen display for displaying analysis results and accepting modifications. The system generates a visual interface on the touchscreen display, displaying real-time white-light images, sub-image segmentation results, and analysis reports on fluorescence features. The visual interface accepts user input for corrections to the segmentation results (i.e., rock cutting pixels and their categories) and feeds these corrections into the semantic segmentation model for incremental learning. The visual interface also displays pseudo-color segmentation results for rock cuttings particle categories; pie charts showing category ratios within each sub-image; pseudo-color annotations of fluorescent areas in the UV image; and a comparative display panel for historical recognition results.

[0049] Continue to refer Figure 2B From a logical function perspective, the system's software (i.e., computing processing unit) mainly consists of: An image preprocessing module that performs the above image preprocessing steps on the white light / violet light image; An ultraviolet fluorescence analysis module for performing fluorescence analysis on ultraviolet images; A lithology-oil database (i.e., a relational database) storing data required for analyzing the oil-bearing probability of rock cuttings samples; A debris proportion statistics module is used to analyze the segmentation results of the semantic segmentation model - pixel-level labels to determine the proportion of debris lithology in each sub-image; A voting decision module is used to vote on the lithology determination results of each sub-graph.

[0050] The embodiment of the present invention significantly reduces the misjudgment of oil content by performing dual judgment on fluorescence intensity and range when analyzing whether oil is present, and combining it with a correlation database.

[0051] Figure 3 A schematic diagram of the structure of a rock cuttings lithology analysis device provided in another embodiment of the present invention. Figure 3 As shown, the device includes: The segmentation module 310 is configured to segment the white light image of the rock cutting sample to obtain at least two sub-images; the rock cutting sample contains at least two rock cutting particles, and each sub-image contains at least one rock cutting particle; A classification module 320 is configured to determine the category of each sub-image based on the ratio of the number of pixels of different categories of rock debris particles in the sub-image; The determination module 330 is configured to determine the overall category of the rock cuttings sample according to the voting results of the categories of each sub-graph.

[0052] The rock cuttings lithology analysis device provided in the embodiment of the present invention can execute the rock cuttings lithology analysis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0053] Optionally, the segmentation module 310 includes: a pixel annotation unit, configured to perform pixel-level annotation on each rock chip particle in the sub-image using a semantic segmentation model to obtain rock chip pixels and categories of the rock chip pixels in the sub-image; a proportion determining unit for determining, based on the category of each rock chip pixel point in the sub-image, a proportion of the number of rock chip pixels in each category to the total number of rock chip pixels; the total number of rock chip pixels being the sum of the number of rock chip pixels in each category; The category determination unit is configured to determine the category with the highest ratio and greater than a ratio threshold as the category of the sub-image.

[0054] Optionally, the determination module 330 includes: The vote determination unit is used to determine the number of votes based on a Figure 1 The voting principle of the ticket is to determine the number of votes for each category based on the category of each sub-graph; The first determining unit is configured to determine the category with the highest number of votes as the overall category of the rock cuttings sample if there is no candidate category with the highest number of votes.

[0055] Optionally, the determining module 330 further includes: an average number determination unit, configured to calculate the average number of rock chip pixels corresponding to each candidate category if there are candidate categories that are tied for first place in terms of the number of votes; The second determining unit is configured to determine the candidate category with the highest average number of rock chip pixels as the overall category of the rock chip sample.

[0056] Optionally, the device further includes: The incremental learning module is used to perform incremental learning on the semantic segmentation model according to the correction results of the rock chip pixels and the categories of the rock chip pixels by the user in the visual interface.

[0057] Optionally, the device further includes: The oil content determination module is used to determine whether the rock cuttings sample contains oil based on the fluorescence characteristics of the fluorescence in the ultraviolet image of the rock cuttings sample; the fluorescence characteristics include fluorescence intensity and fluorescence range.

[0058] Optionally, the device further includes: a case determination module, configured to determine similar cases of the rock cuttings sample from a correlation database based on the fluorescence characteristics and the overall category if the rock cuttings sample contains oil; A probability determination module is used to determine the oil-containing probability of the rock cuttings sample based on the similarity between the ultraviolet image and the fluorescence features in the similar cases.

[0059] The rock cuttings lithology analysis device further described can also execute the rock cuttings lithology analysis method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0060] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The 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. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, 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 examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0061] like Figure 4As shown, electronic device 40 includes at least one processor 41 and memory, such as read-only memory (ROM) 42 and random access memory (RAM) 43, communicatively connected to at least one processor 41. The memory stores computer programs executable by the at least one processor. Processor 41 can perform various appropriate actions and processes based on the computer programs stored in ROM 42 or loaded from storage unit 48 into RAM 43. RAM 43 can also store various programs and data required for the operation of electronic device 40. Processor 41, ROM 42, and RAM 43 are interconnected via bus 44. An input / output (I / O) interface 45 is also connected to bus 44.

[0062] Multiple components in the electronic device 40 are connected to the I / O interface 45, including an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0063] Processor 41 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any other suitable processor, controller, microcontroller, etc. Processor 41 executes the various methods and processes described above, such as the rock cuttings lithology analysis method.

[0064] In some embodiments, the rock cuttings lithologic analysis method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the rock cuttings lithologic analysis method described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to execute the rock cuttings lithologic analysis method in any other suitable manner (e.g., via firmware).

[0065] Various embodiments of the systems and techniques described above 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), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0066] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0067] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0068] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: 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 can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0069] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0070] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0071] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0072] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and voting principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for lithologic analysis of rock cuttings, characterized in that: The method comprises: Segmenting a white light image of a rock chip sample to obtain at least two sub-images; the rock chip sample contains at least two rock chip particles, and each sub-image contains at least one rock chip particle; For each sub-image, determining the category of the sub-image according to the ratio of the number of pixels of different categories of rock debris particles in the sub-image; The overall category of the rock cuttings sample is determined according to the voting results of the categories of each sub-graph.

2. The method according to claim 1, characterized in that Determining the category of the sub-image according to the ratio of the number of pixels of different categories of rock debris particles in the sub-image includes: Perform pixel-level annotation on each rock chip particle in the sub-image using a semantic segmentation model to obtain rock chip pixel points and categories of each rock chip pixel point in the sub-image; According to the category of each rock chip pixel point in the sub-image, the proportion of the number of rock chip pixels in each category to the total number of rock chip pixels is determined; the total number of rock chip pixels is the sum of the number of rock chip pixels in each category; The category with the highest ratio and greater than a ratio threshold is determined as the category of the sub-image.

3. The method according to claim 2, characterized in that Determining the overall category of the rock cuttings sample according to the voting results of the categories of each sub-graph includes: Based on the one-image-one-vote voting principle, the number of votes for each category is determined according to the category of each sub-image; If there is no candidate category tied for first place in vote count, the category with the highest number of votes is determined as the overall category of the rock chip sample.

4. The method according to claim 3, characterized in that Determining the overall category of the rock cuttings sample according to the voting results of the categories of each sub-graph further includes: If there are candidate categories that are tied for first place in the number of votes, the average number of rock chip pixels of the rock chip particles corresponding to each candidate category is calculated; The candidate class with the highest average number of rock chip pixels is determined as the overall class of the rock chip sample.

5. The method according to claim 2, characterized in that The method further comprises: The semantic segmentation model is incrementally learned according to the correction results of the rock chip pixels and the categories of the rock chip pixels by the user in the visual interface.

6. The method according to claim 1, characterized in that The method further comprises: Whether the rock cuttings sample contains oil is determined based on fluorescence characteristics of fluorescence in the ultraviolet image of the rock cuttings sample; the fluorescence characteristics include fluorescence intensity and fluorescence range.

7. The method according to claim 6, characterized in that After determining whether the rock cuttings sample contains oil based on the fluorescence characteristics of the fluorescence in the ultraviolet image of the rock cuttings sample, the method further includes: If the rock chip sample contains oil, determining similar cases of the rock chip sample from a correlation database based on the fluorescence characteristics and the overall category; The oil-containing probability of the rock cuttings sample is determined according to the similarity between the ultraviolet image and the fluorescence features in the similar case.

8. A rock cuttings lithology analysis device, characterized in that: The device comprises: a segmentation module for segmenting the white light image of the rock cuttings sample to obtain at least two sub-images; the rock cuttings sample contains at least two rock cutting particles, and each sub-image contains at least one rock cutting particle; A classification module, configured to determine the category of each sub-image according to the ratio of the number of pixels of different categories of rock debris particles in the sub-image; The determination module is configured to determine the overall category of the rock cuttings sample according to the voting results of the categories of each sub-graph.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the rock cuttings lithology analysis method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the rock cuttings lithology analysis method according to any one of claims 1 to 7 when executed.

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