141 results about "Thin section" patented technology

The invention provides a rock image classification method based on edge enhancement and multi-scale feature fusion. By combining the edge enhancement and multi-scale feature fusion technology, the accuracy of mineral classification in the rock slice image is remarkably improved. According to the method, rock slice image interference is eliminated through filtering and denoising, and mineral particle edges are enhanced by fusing morphological top-hat transformation and a Laplace operator; and performing multi-scale pyramid decomposition on the enhanced image to extract high-frequency information, performing multi-direction response enhancement to generate a direction feature map, and performing channel-level fusion on the original image, the edge enhanced image and the direction feature map. And finally, inputting the fused image into a convolutional neural network to complete rock type classification. According to the method, mineral boundary expression is effectively enhanced, the classification accuracy is improved, and a reliable technical scheme is provided for geological analysis and lithology identification.

The invention relates to the cross technical field of artificial intelligence and mineralogy, and discloses an AI mineral automatic identification method based on an optical microphotograph, comprising the following steps: step S1: data acquisition and preprocessing; s2, data classification and labeling: constructing a rock microscopic image data set by adopting a two-stage labeling process; s3, building and training a deep learning model; s4, model optimization and quantitative feature learning; s5, developing a software platform; and S6, hardware system combination and full-process automation are carried out. In the aspect of operation convenience, a full-automatic slice scanning hardware system is constructed and combined with an intelligent software platform, so that full-process automatic operation from automatic rock slice scanning, automatic mineral analysis to automatic PDF report generation is realized, manual intervention is greatly reduced, the operation difficulty and complexity are reduced, and the working efficiency is improved. And even an operator without rich experience can easily complete complex mineral identification work.

The invention discloses a fracturing engineering dessert evaluation method based on a while-drilling rock debris indentation mechanical parameter test. The fracturing engineering dessert evaluation method comprises the following steps: S1, obtaining a while-drilling rock debris sample; s2, processing the rock debris sample while drilling into a casting body sheet; s3, acquiring micro-mechanical parameters of each test point; s4, obtaining macromechanical parameters of the rock debris sample; s5, core triaxial mechanical parameters of reservoir rock are obtained; s6, establishing relevance between micromechanical parameters of the while-drilling debris sample and triaxial mechanical parameters of the rock core, and correcting the micromechanical parameters obtained by a subsequent indentation method to obtain rock mechanical parameters of different well depths of the horizontal well section; S7, respectively carrying out weighted average treatment on the obtained rock mechanical parameters, and proposing a fracturing index; and S8, according to the fracturing indexes, a K-clustering analysis method is adopted, and the horizontal section stratums are classified and evaluated according to the three types of reservoir stratums. The horizontal well mechanical parameter testing and reservoir compressibility evaluation method is formed through the fracturing indexes, and a traditional reservoir evaluation method mainly based on geology is changed.

The invention relates to the technical field of geological rock type identification, and discloses a deep carbonate rock lithology identification method based on gamma-ray spectrum logging, and the method comprises the steps: S1, collecting a large number of rock cores, slices and the like as verification data; s2, performing statistics on slice identification results of 4, 6, 7 and 8 well carbonate rock coring layer sections of the ancient city in the research area, and dividing the carbonate rock in the research area into five types, namely grit limestone, mud crystal, bright crystal grit limestone, mud crystal limestone, cloud limestone and dolomite; and S3, calibrating on a single well histogram, and counting gamma-ray energy spectrum (U, K, TH, PE) values corresponding to the slice samples. According to the method provided by the invention, the lithology of the carbonate rocks in the fourth, sixth, seventh and eighth wells of the ancient city is identified with relatively high accuracy, fine lithology changes are further identified in the originally explained large set of limestone, the accuracy is far higher than that of logging and debris logging through rock core inspection, and the accuracy of identifying the lithology of the carbonate rocks is greatly improved.

A method for generating a training dataset for determining grain boundaries and minerals in a thin section of a rock sample, includes receiving the thin section of the rock sample, generating optical images of the thin section with an optical tool, generating mineral phase images of the thin section with an electron microscopy tool, computing first and second pseudo-images based on different features extracted from the optical images, generating the training dataset based on (1) the optical images, (2) the mineral phase images, and (3) the pseudo-images, and training a single deep neural network, DNN, based on the training dataset to simultaneously determine a mineral type and grain boundaries in the thin section of the rock sample.

The invention provides a rock slice detail identification and slice text description generation method, and relates to the technical field of geological analysis. On the basis of constructing a picture and corresponding text description database under the rock polarizing microscope, matching the image under the rock polarizing microscope with a rock name, a mineral composition and a polarizing microscope type by utilizing a fine-tuned CNCLIP contrast learning network; and a text generation model is combined to directly generate slice description from the picture under the rock polarizing microscope. By adopting the method disclosed by the invention, the organization, classification and identification of the rock picture data under the polarizing microscope can be effectively realized, the rock slice image under the microscope can be directly read by the method constructed by the invention, and the content in the picture is matched with the rock name, the mineral and the polarizing microscope type; and on the basis, a text generation model is combined, and finally, the function of automatically carrying out text description on the image under the rock polarizing microscope is realized.

Systems and methods for determining a diagenesis level in a reservoir for performing hydrocarbon extraction include receiving a set of open hole (OH) log data from one or more wells in the reservoir, the OH log data representing a subsurface of the reservoir; executing a diagenesis model to process the set of OH log data, the diagenesis model trained by thin-section image data correlated to labeled OH log data, the labeling identifying values for a quartz overgrowth rate (QOR), a clay coating rate (CCR), or a thin-section porosity in the subsurface based on a value in the OH log data; determining a prediction of a porosity of the reservoir, a permeability of the reservoir, or both the porosity and the permeability; and generating a control signal representing a recommendation to drill a well at a location in the reservoir.

The invention relates to the technical field of material analysis, in particular to a light microscope-field emission electron microscope combined shale rock slice analysis and identification method, which comprises the following steps of: firstly, combining a brightness distribution condition represented by a bright field image of a shale rock slice and a birefringence deviation condition represented by a cross polarization image from an optical angle; determining a mineral discrimination index representing an atomic number agent; applying offsets of different sizes based on the distribution condition of mineral discrimination indexes and the surface fluctuation condition of shale rock slices, and determining a position offset vector by combining gray scale gradient distribution on the basis; and finally, determining the cross-scale positioning and more accurate electron microscope objective table coordinates corresponding to each pixel point in combination with a position offset vector in a pixel coordinate conversion process based on an image physical distance scale and rotation translation processing, so that the cross-scale positioning accuracy of the optical microscope and the field emission scanning electron microscope is higher. The reliability of analyzing and identifying the shale rock slice is obviously improved.

The invention belongs to the field of petroleum geology and fluid geochemistry, and discloses a method for limiting opening duration of a natural horizontal hydraulic fracturing tensible fracture in a reservoir. The method comprises the following steps: determining that a horizontal tensional fracture is developed in rock, collecting a rock sample, making a fluid inclusion slice, determining the characteristics of filling blocky crystals in the fracture, combining fluid inclusion lithofacies observation and laser Raman spectroscopy analysis technologies, and selecting a methane-rich fluid inclusion combination as a research object. And carrying out subsequent microscopic temperature measurement analysis and PVT simulation, combining a PVT simulation result with a basin evolution history, and finally obtaining the opening duration of the natural horizontal hydraulic fracturing tensible fracture on the basis of judging the existence of the ultra-quiet rock fluid pressure. The method is low in cost and high in operability, and a certain judgment basis is provided for enrichment and loss of shale gas and tight sandstone gas.

The invention discloses a rock slice image component identification method based on deep learning. According to the method, a data set is constructed through PCA dimension reduction and normalization, segmentation parameters are generated by utilizing ResNet50 and a self-attention mechanism, automatic classification of quartz, rock debris, kaolinite, pores and matrixes is realized by combining a VGG16 model, two-stage segmentation is performed by adopting AGLFIF and Otsu algorithms, and high-precision identification and geological analysis automation of rock slice images are realized. According to the method, real-time component identification of the rock slice image can be realized, and automation and reliability of geological analysis are improved.

The invention discloses a method for in-situ leaching mining of sandstone uranium ore, the concentration of bicarbonate radical ions in ore bed water in the sandstone uranium ore is lower than 0.5 g / L, the sandstone uranium ore comprises yttrium-containing uranium ore, and the carbonate content is 1.5%-2.5%; the ore bed water serves as a solvent, 500 mL of leaching agents with different pH values adjusted through sulfuric acid are prepared, and the concentration of hydrogen peroxide in the leaching agents is 2 g / L; and determining the maximum pH value when the uranium ore in the slice sample is corroded by using the leaching agents with different pH values, and taking the maximum pH value as a critical pH value. The leaching agent with the critical pH value is used for exploiting the sandstone uranium ore, corrosion to gangue minerals in the leaching process can be reduced, the number of formed colloids and CaSO4 precipitates is reduced, the precipitates are prevented from blocking an ore bed, in addition, the method can be used for target corrosion of the refractory uranium ore, and therefore efficient leaching is achieved.

The invention discloses a method and a device for quantitatively characterizing the influence intensity of ancient volcanic activities, which can be used in the field of geophysical exploration, and the method comprises the following steps: determining the compaction recovery coefficient of vacant tuff according to the sorting coefficient and the current porosity; according to the compaction recovery coefficient and the thickness of the bare slice hollow falling type tuff, the original deposition thickness of the hollow falling type tuff is determined; according to the original deposition thickness and the number of layers of the empty tuff, determining the volcanic activity influence intensity of the to-be-detected area; wherein the sorting coefficient is the sorting coefficient of the empty tuff and is obtained by carrying out particle size analysis on a polished thin section; the current porosity is the current porosity of the empty tuff corresponding to the polished section and is obtained by measuring the porosity of the polished section; the thickness and the number of layers of the empty tuff corresponding to the bare slice are obtained by identifying the bare slice. According to the method, evaluation standards of the volcanic activity influence can be established, and the intensity of the volcanic activity influence on the to-be-detected area can be quantitatively represented.

The invention discloses a volcanic rock lithology identification method based on latent attribute characterization and Bayesian inference. According to the method, volcanic rock core / rock debris slice identification and related geochemical data are taken as calibration basis, multiple groups of logging curves sensitive to the lithologic characteristics of volcanic rock are preferably selected, and quality control and normalization processing are carried out; the method comprises the following steps: learning from a multi-logging curve through an encoder-decoder to obtain a group of mutually decoupled latent attribute representations, and respectively constructing a silicon dioxide content Bayesian classification model and a volcanic rock cause Bayesian classification model by taking the latent attributes as input; the probability prediction based on the silicon dioxide content range (basic / neutral / acidic) and the volcanic rock cause type is realized, the two prediction results are combined, and finally the volcanic rock lithology of the corresponding well section is determined. Meanwhile, the quantitative output uncertainty is used for multi-solution risk prompt, so that the accuracy, the stability and the credibility of a prediction result are improved.

The invention belongs to the field of basic research of sandstone type uranium ore associated resource evaluation, and particularly relates to a method for judging the existence form of associated gallium in a sandstone type uranium deposit, which comprises the following steps of: measuring the contents of major elements and trace elements of a plurality of uranium ore samples, and judging whether gallium meets the lowest grade requirement of associated resources of the uranium deposit; for uranium ore samples exceeding the lowest grade requirement of gallium associated resources, correlation analysis of gallium elements, main elements and trace elements is carried out, and possible key mineral types closely related to gallium are preliminarily speculated; carrying out slice analysis, and circling an approximate range of speculated possible key mineral types; carrying out scanning electron microscope and energy spectrum analysis combined observation, and determining the existence form of gallium in the uranium deposit; the position exceeding the associated grade gallium is determined through laser in-situ analysis; the gallium-related mineral type is directly determined through electron probe analysis; and comprehensively judging the existence form of gallium in the uranium deposit. According to the method, the existence form of associated gallium in the sandstone type uranium deposit can be accurately and quickly judged.

A chlorite growth process reconstruction method relates to the technical field of geological exploration, and mainly comprises the following steps: collecting well drilling core sample analysis related data, and preparing rock slices; analyzing the relationship between different deposition periods and environmental conditions and chlorite generation and growth; representing the composition and content of chlorite and other minerals in the rock slice; establishing the relationship between chlorite components and diagenesis evolution stages; collecting rock core sample diagenesis environment sediment samples; the method comprises the following steps: evaluating the influence of organic matters in sediments on chlorite formation, performing multi-scale coupling numerical simulation modeling, establishing a chlorite growth model, and reconstructing a chlorite growth process by using the chlorite growth model; according to the method, core sample analysis, sedimentary environment parameters and multi-scale coupling numerical simulation are comprehensively combined, so that growth evolution of chlorite in each diagenesis stage is clearly reconstructed, and a more accurate and reliable chlorite growth process is obtained.

The application provides a rock slice image data management method, device and equipment, and relates to the technical field of geological exploration. The method comprises the following steps: acquiring image data and original identification data of a rock slice to be managed, which have first annotation information; identifying key features of the rock through machine learning based on the first annotation information and an identification standard to obtain second annotation information, wherein the second annotation information contains information carrying a first annotation information label; preliminarily checking the original identification data by using the second annotation information carrying the first annotation information label to obtain a checking result and annotation deviation information; performing secondary checking on the original identification data by using the second annotation information not carrying the first annotation information label to obtain annotation missing information; and managing the image data based on the checking result, the annotation deviation information and the annotation missing information in combination with double-expert collaborative identification. The application can improve the accuracy of rock slice image data management.

This invention relates to the field of carbonate rock lithology identification technology, and particularly to a method, system, and apparatus for automatically identifying carbonate rock lithology based on well logging curves; comprising the following steps: S1, acquiring well logging curve data, core data, and thin section data from multiple wells in the area to be identified; S2, standardizing and environmentally correcting the well logging curve data; S3, using an optimization model to solve for the lithological profile of the area to be identified, thereby determining the clay content, porosity, and water saturation; S4, constructing rock structure curves and formation fracture porosity curves; S5, constructing a machine learning model, setting a learning sample set, and inputting the learning sample set into the machine learning model for training, so that the trained machine learning model can automatically identify carbonate rock lithology; this invention improves the accuracy of carbonate rock lithology identification.

The embodiment of the application provides a preparation method of a fluid inclusion microthermometric ore slice, comprising the following steps: providing a fluid inclusion slice which has been observed and circled under an optical microscope in detail for microthermometric target fluid inclusion position; putting the whole fluid inclusion slice into a glass culture dish, injecting a slice adhesive dissolving solution into the culture dish, and immersing the whole fluid inclusion slice in the slice adhesive dissolving solution to dissolve the slice adhesive and separate the fluid inclusion ore slice from the bonding surface of the glass slide; after the separation, placing the fluid inclusion ore slice with the adhesive surface upward on the surface of the glass slide and immersing it in the slice adhesive dissolving solution; after the slice adhesive on the surface of the fluid inclusion ore slice is completely dissolved, cutting the fluid inclusion ore slice according to the position of the target inclusion to be measured and thermometric, and preparing a rock and mineral fragment containing the target fluid inclusion which is suitable in size to be placed in a cold and hot stage quartz crucible.

The invention relates to the technical field of strata facies type identification, in particular to a method and device for rapidly identifying shale strata facies, and the method comprises the following steps: obtaining a to-be-detected strata-containing shale sample; the method comprises the following steps: scanning a to-be-detected shale sample containing the strata by using a portable handheld X-ray fluorescence spectrometer, constructing a measuring point position, and measuring elements in the sample point by point at the measuring point position to obtain the mass percent content of the elements; performing contrast microscopic analysis, selecting corresponding part of point positions at the element measuring point positions to prepare rock slices, and identifying the exact lamina phase type of the rock slices under a microscope; according to the method, the element content in the to-be-detected shale sample containing the lamina is measured through the portable handheld X-ray fluorescence spectrophotometer, the lamina phase type is judged and identified through the element content, identification of a single lamina can be completed within one minute, identification of the lamina phase type can be rapidly completed in a field environment in a lossless and low-cost mode, and the identification accuracy is high. And the identification efficiency of the lamina phase type is improved.

The invention provides a carbonate rock seismic lithology depicting method and system, a storage medium and equipment. The method comprises the following steps: collecting basic geological data of a research area; lithology identification is carried out on the rock core and the slice of the target layer based on drilling and logging data, the calibration relation between different lithology and a logging curve is obtained, and the content of different lithology in the target layer is calculated; establishing a through-well seismic synthesis record based on the logging curve data, performing target stratum top and bottom stratum tracking in the three-dimensional seismic data volume, and extracting the internal seismic attributes of the target stratum; performing correlation analysis on the contents of the different lithologies and the seismic attributes, obtaining a correlation coefficient between the lithology contents and the seismic attributes, selecting the seismic attributes based on the correlation coefficient, and determining a lithology regression equation; and calculating and acquiring content ratio results of different lithologic components, and integrating the content ratio results to obtain stratum lithofacies distribution characteristics of the target stratum. According to the carbonate rock seismic lithology depicting method, the depicting accuracy is improved.

The invention discloses a rock slice image classification method based on domain self-adaption, and the method comprises the steps: collecting rock slice image data, the method comprises the following steps: acquiring a rock slice image, performing multi-scale data preprocessing and geological field data enhancement, extracting multi-level visual features of the rock slice image by utilizing a pre-trained DINOv3 model, performing field specialized adaptation on general visual features through a geological field adaptive Adapter module, and enhancing rock slice discriminative feature representation by adopting a double-path attention mechanism. Constructing a progressive hierarchical classification head to realize coarse-to-fine rock classification; and designing a multi-stage progressive training strategy to optimize the overall performance of the model. According to the method, mineral composition and structural features of the rock slices under different scales can be accurately captured, and multi-scale features and an attention mechanism are fully utilized, so that accurate classification of the rock slices is realized, and the accuracy and reliability of rock slice identification are remarkably improved.

This invention relates to the field of petroleum exploration and development technology, and is a method and system for fluid property identification based on micro-fluorescence thin section technology. The method includes acquiring the micro-fluorescence thin section spectrum of a rock sample; obtaining the true fluorescence area under blue light and the true fluorescence area under ultraviolet light based on the micro-fluorescence thin section spectrum of the rock sample; substituting the true fluorescence area under blue light and the true fluorescence area under ultraviolet light into an extinction index model to obtain an extinction index; and comparing the extinction index with an extinction index evaluation standard to identify fluid properties. This invention establishes an extinction index for identifying reservoir fluid properties. Its extinction index model extracts the pair of sensitive parameters—fluorescence area under blue light and ultraviolet light—by utilizing the spectral variation characteristics between different crude oils and different proportions of water under micro-fluorescence thin section technology. This effectively solves the problem of reservoir fluid property identification, and the method can quantitatively identify reservoir fluid properties.

Systems and methods for determining a diagenesis level in a reservoir for performing hydrocarbon extraction include receiving a set of open hole (OH) log data from one or more wells in the reservoir, the OH log data representing a subsurface of the reservoir; executing a diagenesis model to process the set of OH log data, the diagenesis model trained by thin-section image data correlated to labeled OH log data, the labeling identifying values for a quartz overgrowth rate (QOR), a clay coating rate (CCR), or a thin-section porosity in the subsurface based on a value in the OH log data; determining a prediction of a porosity of the reservoir, a permeability of the reservoir, or both the porosity and the permeability; and generating a control signal representing a recommendation to drill a well at a location in the reservoir.

The present invention belongs to the technical field of resource data collection and assessment, and in particular relates to a method for identifying the time scales of ecological environment recovery at different scales after volcanic activity. Based on a variety of analytical test data methods such as major elements and trace elements, the method analyzes changes in the shale sedimentary environment and environmental disturbances caused by sudden events, and establishes a method for identifying the time scales of ecological environment recovery at the centimeter-level and micrometer-level scales of cores and thin sections. This method provides a new approach to calculating the time scale of environmental impacts after volcanic activity. Based on the analysis of changes in the shale sedimentary environment and environmental disturbances caused by sudden environmental events, the method calculates the time scale of shale sedimentary sections during the ecological environment recovery period under the influence of volcanic activity, establishes a comparative scale from ecological environment chaos to stability, clarifies the time depth of the specific environmental impact of volcanic activity, improves the distribution and positioning accuracy of high-organic matter shale, and provides guidance for the exploration of high-quality organic-rich shale layers.

A method and a system for generating a labeled benchmark dataset are disclosed. The method includes obtaining a plurality of sources related to a thin section using web scraping and extracting a plurality of images from the plurality of sources related to the thin section, the plurality of images including a plurality of thin section images and a plurality of non-thin section images. Further, the method includes determining the plurality of thin section images from the plurality of extracted images and generating a classification of the plurality of thin section images based on a given classification criteria. The geological thin-section based machine learning models is trained based on the generated classification of the plurality of thin section images and a wellbore drilling plan is generated based on the geological thin-section based machine learning models.

The present invention belongs to the field of petroleum geology and fluid geochemistry, and discloses a method for limiting the duration of opening of natural horizontal hydraulic fractures in a reservoir. By determining that horizontal tensile fractures are developed in the rock, collecting the rock sample and making fluid inclusion thin sections, determining the characteristics of the massive crystals filling the fractures, combining fluid inclusion petrographic observation with laser Raman spectroscopy analysis technology, selecting a methane-rich fluid inclusion combination as the research object, conducting subsequent microthermometry analysis and PVT simulation, combining the PVT simulation results with the basin evolution history, and finally obtaining the duration of opening of natural horizontal hydraulic fractures based on the identification of the existence of ultra-static rock fluid pressure. The method of the present invention is low in cost and highly operational, and provides a certain basis for judging the enrichment and loss of shale gas and tight sandstone gas.

This invention relates to the field of materials analysis technology, specifically to a method for analyzing and identifying shale thin sections using a combination of optical microscopy and field emission scanning electron microscopy. First, from an optical perspective, the method combines the brightness distribution of bright-field images of shale thin sections with the birefringence deviation of cross-polarized images to determine mineral discrimination indices representing atomic number proxies. Then, based on the distribution of the mineral discrimination indices and the surface undulations of the shale thin sections, different biases are applied, and the positional bias vector is determined by combining this with the gray-level gradient distribution. Finally, based on the physical distance scale of the image and the pixel coordinate transformation process involving rotation and translation, the positional bias vector is used to determine the electron microscope stage coordinates corresponding to each pixel point for more accurate cross-scale positioning. This improves the accuracy of cross-scale positioning using optical microscopy and field emission scanning electron microscopy, significantly enhancing the reliability of shale thin section analysis and identification.

The invention provides an intelligent sandstone slice identification system and an intelligent sandstone slice identification method, and belongs to the technical field of geological exploration. In the system, a data analysis module integrates three core algorithms: a data enhancement module adopts an improved conditional cycle consistency generative adversarial network to solve the problem of scarcity of rock samples; the multi-modal feature understanding module fuses image visual features and petrology text features by using a cross-modal attention mechanism; and the intelligent identification module completes recognition and classification of mineral composition, structure construction and diagenesis in parallel based on a multi-task learning architecture. The method comprises a system training stage and a sample identification stage. According to the method, the model generalization ability is improved by generating a large number of synthetic samples, the geological rationality of the identification result is enhanced through multi-modal semantic fusion, efficient, accurate and objective automatic intelligent identification of the sandstone slice is realized, and a key technical support is provided for reservoir evaluation in the field of petroleum geology.

The application discloses a kind of carbonate rock flake identification method and system, belong to image processing technical field, will take the carbonate rock flake image of different geological scene respectively input pre-trained network model and identify, output attribute identification result.Network model includes classification module, segmentation module, feature fusion module and classifier, after classification module extracts mineral texture feature of carbonate rock flake image by mineral texture attention module, respectively input classification head and the regression head of embedding dynamic scaling constraint regression mechanism and carry out detection;Segmentation module carries out regional segmentation to carbonate rock flake image, outputs the pixel proportion of real mask corresponding to the prediction mask of different segmentation region respectively;Feature fusion module carries out fusion to detection result and pixel proportion;Classifier carries out coordinated inference to fusion feature, and outputs attribute identification result.The method is identified by cooperation and dynamic constraint, ensure the scientificity and reliability of identification result.

The invention discloses a high-precision recovery marine facies compact limestone sedimentary environment and oil-gas dessert area prediction method. The method comprises the steps that S1, an argillaceous limestone phase, an argillaceous limestone phase, a mud-powder crystal limestone phase and a paste limestone phase are divided through X-ray diffraction and slice identification; s2, utilizing a chemical weathering index CIA, a Sr / Ca salinity index and delta 13Corg organic carbon isotope to synergistically recover paleo-climate and paleo-hydrological conditions; s3, combining total organic carbon TOC, organic matter maturity Ro, terrestrial input Al and Ti content and nanopore development degree, and establishing an organic matter-rich high-quality lithofacies evaluation standard; and S4, according to an evaluation standard, well drilling, well logging and seismic data are fused to predict the oil and gas sweet spot area. Through the four steps of lithofacies division, paleoenvironment recovery, evaluation standard establishment and dessert area prediction, high-precision recovery of the marine facies compact limestone sedimentary environment is realized, the dessert area prediction success rate is improved to 78%, and efficient technical support is provided for oil-gas exploration.