30 results about "Petrophysical analysis" patented technology

The invention discloses a method for constructing a three-dimensional digital core model of irregular concave-convex particles, and the method comprises the following steps: S1, generating regular small balls with the same radius distribution based on the radius distribution and shape characteristics of actual rock particles; s2, constructing a single particle with an irregular concave-convex shape based on a single regular small ball; s3, by setting modeling parameters of different regular small balls, on the basis of a construction method of a single particle with an irregular concave-convex shape, constructing a plurality of irregular concave-convex particles by the regular small balls with different sizes; and S4, simulating deposition and compaction diagenesis processes of a plurality of irregular concave-convex particles in a three-dimensional space to obtain digital core models with different pore structures. The modeling principle and process have the advantages of being easy to understand, controllable in concave-convex shape, diverse in result and the like, and a model foundation is laid for oil and gas reservoir rock physical analysis and modeling work.

The invention provides a method for predicting the filling probability of a fracture-vuggy carbonate reservoir, a product and application, and belongs to the technical field of geological information processing. The method comprises the following steps that well logging interpretation evaluation and rock physical analysis of a target work area are carried out through drilled data of the target work area; establishing fracture-cavity filling geologic model seismic forward modeling of the target work area; establishing a pre-stack iteration low-frequency model of the target work area, and performing pre-stack simultaneous inversion; and on the basis of an inversion result, fracture-cavity filling probability prediction of the target work area is carried out. According to the method, the filling property of the fracture-vuggy carbonate reservoir in the complex karst area is summarized and predicted, the lithofacies probability and the filling general situation of the fracture-vuggy reservoir can be obtained, the pre-stack inversion wave impedance and Vp / Vs are combined, the filling property of the fracture-vuggy reservoir can be predicted, and the prediction precision of a large-scale effective reservoir is improved.

The application discloses a kind of three-dimensional seismic forward modeling methods and systems for carbonate karst cave reservoir, belong to the field of oil and gas exploration seismic forward modeling.The method of fusing the three-dimensional P-wave velocity body of the carved-out karst cave reservoir quantitatively into seismic interval velocity establishes three-dimensional seismic forward modeling P-wave velocity model for karst cave reservoir, and the method of carrying out seismic forward modeling based on the model;In the process of three-dimensional seismic forward modeling for carbonate karst cave reservoir, the seismic inversion P-wave impedance body after carving by well logging petrophysical analysis is introduced into the process of establishing P-wave velocity model for the first time, so that the established karst model is more in line with geological understanding and actual situation, and the established seismic forward modeling P-wave velocity model is more accurate.The three-dimensional seismic forward modeling P-wave velocity model of carbonate karst cave reservoir obtained by the application can more truly represent the shape and size of actual carbonate karst cave reservoir compared with the model made by other traditional methods.

Disclosed are methods, systems, and computer programs for generating petrophysical analysis data. The methods include: accessing first porosity data and first well log data associated with an NMR well; randomly selecting data elements comprised in the first porosity data and the first well log data; developing, based on the randomly selected first porosity data and first well log data, a learning data structure; generating, based on the learning data structure, a machine learning model; receiving second well log data using sensor(s) deployed about a non-NMR well; adapting, based on the second well log data, variable input parameters of the machine learning model to generate second porosity data; and formatting the second porosity data to generate petrophysical analysis data. The petrophysical analysis data indicates one or more of: a petrophysical characterization of a rock type; flow dynamic data of a carbonate reservoir; well test or perforation zone data; and pore data.

The invention discloses a longitudinal and transverse wave VSP-based segmented amplitude attenuation factor calculation method and system and a medium, and relates to the technical field of geophysical exploration and rock physics analys.The method is improved on the basis of a traditional amplitude attenuation factor calculation method, a segmented geologic model of a target area is constructed based on a strong reflection interface, longitudinal wave and transverse wave zero offset VSP data is utilized, and the seismic data of the target area is calculated. The geological amplitude attenuation factor of each layer section of the layered medium is obtained through longitudinal and transverse wave joint inversion and segmented quantification, accurate compensation of stratum amplitude attenuation under strong reflection shielding of a coal seam, a volcanic rock layer and the like is achieved, and more reliable parameters are provided for solving the problems of data distortion and the like caused by shielding effects below strong reflection in ground seismic data processing. The obtained amplitude attenuation factor can be directly used for guiding fidelity processing and high-resolution imaging of ground seismic data, and signal recovery and imaging quality of deep and hidden reservoirs are remarkably improved, so that a more reliable geophysical basis is provided for subsequent fine description of the reservoirs and fluid detection.

The invention discloses a thin mutual reservoir sand-to-ground ratio calculation and correction method, electronic equipment and a storage medium. The method comprises the steps that sensitive parameters of a thin sand body reservoir are determined through reservoir rock physical analysis; high-resolution reservoir inversion is carried out, and reservoir sensitive parameter layer section attributes needing to be subjected to sand-to-ground ratio calculation are extracted; fine small layer division comparison is carried out, and the true thickness of each set of thin sand body corresponding to the actual drilling well is determined; performing rendezvous analysis on the real drilling well thin sand body true thickness and the reservoir sensitive parameters, and determining a value domain critical mean value of multiple sets of thin sand body reservoirs in the interval; calculating the sand-to-ground ratio of the thin mutual reservoir by utilizing the reservoir sensitive parameter layer section attributes and the critical mean value of the multiple sets of thin sand body reservoir value domains; calculating the actual drilling sand-to-ground ratio corresponding to the actual drilling well; and correcting the sand-to-ground ratio of the thin mutual reservoir by using the actual drilling sand-to-ground ratio to obtain a final corrected sand-to-ground ratio contour map of the thin mutual reservoir. According to the method, the precision of calculating and correcting the sand-to-ground ratio of the thin mutual reservoir can be improved, and reliable technical support is provided for guiding exploration and development of oil and gas reservoirs in the thin mutual reservoir area.

The invention relates to the technical field of rock physical analysis in oil-gas exploration and development, and particularly provides a shale full-size pore structure characterization method which comprises the following steps: acquiring high-pressure mercury injection experimental data and nuclear magnetic resonance experimental data of a shale sample, dividing a pore system of the shale sample into a small pore section and a large pore section by an inflection point of fractal dimension, respectively fitting the pore throat radius of the small pore section and the pore throat radius of the large pore section with the transverse relaxation time to obtain two groups of conversion coefficients, respectively inverting the pore diameter distribution curve of the small pore section and the pore diameter distribution curve of the large pore section by utilizing the two groups of conversion coefficients, and carrying out segmented integration according to the intersection point of the two curves to characterize the full pore diameter distribution of the shale sample. According to the method, a fractal inflection point division pore system is introduced, deep structure information is excavated by utilizing a fractal theory, segmentation fitting is carried out, and the technical problems that a traditional HPMI-NMR conjoint analysis method is insufficient in precision under the conditions of large displacement pressure, low mercury injection saturation and strong heterogeneity are solved.

Disclosed are methods, systems, and computer programs for generating petrophysical analysis data. The methods include: accessing first porosity data and first well log data associated with an NMR well; randomly selecting data elements comprised in the first porosity data and the first well log data; developing, based on the randomly selected first porosity data and first well log data, a learning data structure; generating, based on the learning data structure, a machine learning model; receiving second well log data using sensor(s) deployed about a non-NMR well; adapting, based on the second well log data, variable input parameters of the machine learning model to generate second porosity data; and formatting the second porosity data to generate petrophysical analysis data. The petrophysical analysis data indicates one or more of: a petrophysical characterization of a rock type; flow dynamic data of a carbonate reservoir; well test or perforation zone data; and pore data.

A drilling process method and system for drilling a well are described. The method includes performing a drilling operation through a downhole formation, the drilling operation generating drilling cuttings. A single drilling cutting sample is obtained at a location on the surface. An x-ray diffraction (XRD) and / or x-ray fluorescence (XRF) analysis is performed on the single drilling cutting sample. Elemental information and mineral information of the single drilling cutting sample is obtained from the XRF analysis about the downhole formation. At least one petrophysical property of the downhole formation is determined from the obtained information.

The present invention proposes a method, system, and apparatus for calculating an unconventional reservoir brittleness indicator curve. The method comprises: determining the shale content based on a well logging data curve and reservoir rock physical parameter information; and determining the brittleness indicator curve based on the shale content and well logging porosity. The method, system, and apparatus for calculating an unconventional reservoir brittleness indicator curve of the present invention, based on conventional P-wave logging, density logging, and porosity logging information, integrates rock physical analysis to construct a brittleness indicator curve calculation formula, and implements brittleness indicator parameter calculation and evaluation under conventional logging conditions. This method does not rely on S-wave logging information and is not affected by human factors. It is a highly applicable and accurate brittleness index extraction method. Conducting shale gas reservoir brittleness evaluation based on this method will effectively improve the accuracy of shale gas reservoir fracturing evaluation and reduce the risks of exploration and development.

The application provides a method, product and application for predicting filling probability of a fracture-vug type carbonate reservoir, and belongs to the technical field of geological information processing. The method comprises the following steps: using drilled well data of a target work area to carry out well logging interpretation evaluation and rock physical analysis of the target work area; establishing a fracture-vug filling geological model seismic forward modeling of the target work area; establishing a prestack iterative low-frequency model of the target work area, and performing prestack simultaneous inversion; and based on the inversion result, predicting the fracture-vug filling probability of the target work area. The application can summarize and predict the filling of a fracture-vug type carbonate reservoir in a complex karst area, can obtain lithofacies probability and filling profile of the fracture-vug reservoir, and can predict the filling of the fracture-vug type reservoir in combination with prestack inversion wave impedance and Vp / Vs, so that the prediction accuracy of the scale of effective reservoirs is improved.

The application provides a method for identifying and quantitatively describing a deep-water lithologic trap, and the method comprises the following steps: (1) establishing a deep-water lithologic trap development model through structural-sedimentary slope folding and deep-water deposition analysis; (2) determining a dominant superimposition angle and obtaining reservoir identification data through elastic impedance petrophysical analysis; (3) obtaining a pseudo-elastic impedance body and identifying sandstone reservoir distribution through the reservoir identification data in step (2); (4) three-dimensionally identifying and searching for a lithologic trap target by using a relative geologic age body method and a seismic geomorphology method; and (5) quantitatively describing a lithologic trap boundary through a pinch-out model forward. The method can be used for identifying and quantitatively describing a lithologic trap in a deep-water sand-rich deposition environment, a P-wave impedance and conventional full superimposition data being difficult to accurately identify a reservoir and a sandstone-mudstone boundary being fuzzy.

The invention relates to the field of coal bed gas exploitation and geological exploration, and discloses a macroscopic coal and rock type prediction method, device, equipment and medium, which can perform rock physical analysis on a first coal bed in a first stratum of a research area to determine a plurality of sub-coal beds in the first coal bed and macroscopic coal and rock type sensitive parameter groups of the plurality of sub-coal beds. And for any sub-coal seam, sampling in the sub-coal seam to obtain a plurality of rock samples, respectively detecting parameter values of the macroscopic coal rock type sensitive parameter group of each rock sample, and counting a sensitive parameter value distribution range of each sub-coal seam. And based on the sensitive parameter value distribution range of each sub-coal seam, predicting macroscopic coal rock type distribution of a second coal seam in a second stratum of the research area. According to the method, the macroscopic coal rock type of the coal seam can be predicted based on the macroscopic coal rock type sensitive parameter group of the coal seam, the macroscopic coal rock type distribution of the coal seam is obtained, the macroscopic coal rock type prediction of the coal seam is realized, and the prediction modes of the macroscopic coal rock type of the coal seam are diversified.

The application discloses a method for constructing a three-dimensional digital core model of irregular concave-convex particles, and comprises the following steps: S1, generating regular small balls with the same radius distribution based on the actual rock particle radius distribution and shape characteristics; S2, constructing a single particle with irregular concave-convex shape based on a single regular small ball; S3, constructing several regular small balls with different sizes into several irregular concave-convex particles by setting the modeling parameters of different regular small balls based on the construction method of the single particle with irregular concave-convex shape; and S4, simulating the deposition and compaction of the several irregular concave-convex particles in a three-dimensional space to obtain a digital core model with different pore structures. The modeling principle and process of the application have the advantages of easy understanding, controllable concave-convex shape, various results and the like, and lay a model foundation for carrying out oil and gas reservoir petrophysical analysis and modeling work.

The invention relates to a method, a device, a medium and equipment for predicting an offshore fan delta reservoir affected by unstable strong reflection, and the method comprises the steps: carrying out the physical analysis of rock in a work area, and carrying out the well-seismic calibration; on the basis of the rock physical analysis and well seismic calibration, establishing a sequence stratigraphic framework and extracting a sedimentary ancient landform; on the basis of the establishment of the sequence stratigraphic framework and the extraction of the sedimentary ancient landform, carrying out self-phased pre-stack simultaneous inversion and inversion sensitive attribute extraction; and on the basis of self-phased pre-stack simultaneous inversion and inversion sensitive attribute extraction, plane compensation factor construction and attribute optimization processing are carried out.

This invention discloses a three-dimensional prediction method for tight sandstone permeability based on Bayesian optimized XGBoost and seismic waveform indicator inversion, belonging to the field of reservoir parameter prediction in geophysical exploration. The method first determines sensitive logging parameters through rock physical analysis and correlation optimization; then, it uses a Bayesian optimization framework to adaptively optimize the hyperparameters of the XGBoost model, establishing a high-precision prediction model. The trained model is applied to logging data from the wells to be predicted (M1 and M2), and the prediction results are consistent with the trend of the core data. The average permeability of the predicted segments are 0.025 mD and 0.076 mD, respectively, and the average permeability (MAE) is 0.001 mD and 0.019 mD, respectively, verifying the model's generalization ability. Finally, combined with seismic waveform indicator inversion technology, the model is extended to three-dimensional space to predict the permeability data volume. The overall consistency rate between the three-dimensional prediction results and the validation wells reaches 80%. This invention significantly improves the prediction accuracy and characterization capability of tight sandstone permeability from single wells to three-dimensional space.

The invention discloses a seismic lithogenous phase prediction method based on semi-supervised learning, and belongs to the technical field of seismic sedimentology. Analyzing main diagenesis types of compact sandstone at a target layer by comprehensively using analysis test data, rock physical experiment data, logging data and seismic data, establishing a diagenesis evolution sequence and a diagenesis phase division scheme, and determining rock core diagenesis phases; a logging curve sensitive to the lithogenous phase is screened out, and vertical distribution of the lithogenous phase on a single well is evaluated through logging; determining diagenetic phase sensitive seismic elastic parameters by means of rock physical analysis, correlation analysis and analysis of relation among diagenetic characteristics, mineral components and seismic elastic parameters; a semi-supervised learning neural network model based on an attention mechanism is created, a non-linear mapping relation between the rock core lithogenous phase and the sensitive seismic elastic parameters is established through semi-supervised learning, and a lithogenous phase comprehensive index data body is obtained; and carrying out phase and slice processing on the diagenesis phase comprehensive index data body, screening typical stratigraphic slices, and explaining seismic diagenesis phase space distribution.

The application provides a method, device and equipment for identifying tuff, and a storage medium, and belongs to the technical field of geophysical exploration. The method comprises the following steps: performing classification processing on logging data of a target area based on logging curve characteristics of various lithologies in volcanic rocks; generating elastic parameter curves corresponding to various lithologies based on elastic parameters of various lithologies in the classified logging data; constructing a lithology identification factor based on elastic parameters in at least two dimensions selected from the elastic parameter curves; identifying tuff from seismic data of the target area by using the lithology identification factor, and determining a distribution range of the tuff. Different lithology characteristics and distribution rules of volcanic rocks are determined by logging and seismic rock physical characteristic analysis, the influence of tuff on reservoir prediction is reduced, and the sensitivity factor for identifying tuff is constructed based on rock physical analysis, the multi-solution problem of volcanic rock prediction is reduced, and the seismic prediction accuracy of volcanic rock lithology and reservoirs can be improved.

This invention discloses a thin reservoir prediction method based on pre-stack frequency extension processing. The technical solution is based on improving the resolution of seismic data processing, conducting reservoir petrophysical analysis using measured shear wave data, constructing a petrophysical model, clarifying sensitive parameters for thin reservoir identification, and bridging the gap between well logging and seismic data. For seismic data, pre-stack seismic deterministic inversion of sand group thickness resolution and pre-stack statistical high-resolution inversion of single sand body thickness are performed to calculate sensitive parameter data volumes. Based on petrophysical quantities, the spatial characterization accuracy of thin sand bodies is effectively improved. This invention is highly operable and easy to apply, showing good application prospects for ultra-deep and ultra-thin sand bodies similar to those in the Cretaceous and Triassic strata of the Tarim Basin's Tahe Oilfield. It is also applicable to the identification of sweet spots and fine spatial characterization of ultra-deep thin reservoirs in the central and western basins, which is of great significance for improving drilling success rates and production capacity construction.

Disclosed are methods, systems, and computer programs for generating petrophysical analysis data. The methods include: accessing first porosity data and first well log data associated with an NMR well; randomly selecting data elements comprised in the first porosity data and the first well log data; developing, based on the randomly selected first porosity data and first well log data, a learning data structure; generating, based on the learning data structure, a machine learning model; receiving second well log data using sensor(s) deployed about a non-NMR well; adapting, based on the second well log data, variable input parameters of the machine learning model to generate second porosity data; and formatting the second porosity data to generate petrophysical analysis data. The petrophysical analysis data indicates one or more of: a petrophysical characterization of a rock type; flow dynamic data of a carbonate reservoir; well test or perforation zone data; and pore data.

The invention belongs to the technical field of geophysical interpretation, and discloses a volcanic rock seismic inversion method based on lithofacies constraint, an application, a device and a medium, and the method comprises the steps: obtaining a volcanic rock top and bottom reflection interface, carrying out the multi-attribute volcanic rock lithofacies depiction, and building a lithofacies frame model according with a geological law; meanwhile, the seismic inversion process is jointly constrained on the basis of a rock physical analysis result. The device and the computer readable storage medium according to the method can be applied to obtaining the reservoir thickness plane distribution prediction map, so that the volcanic rock seismic inversion result conforms to lithofacies characteristics, the accuracy of the inversion result is higher, and the method and the device are suitable for inversion of the volcanic rock reservoir and reservoir information analysis.

This invention belongs to the field of oil and gas exploration technology, and relates to a method and system for quantitative seismic prediction of free oil porosity. In the prediction process, high-resolution seismic elastic parameter data volumes obtained through pre-stack statistical inversion are used, and a set of machine learning prediction feature values ​​for free oil porosity is constructed based on rock physical analysis, improving the prediction capability of free oil porosity in thin sweet spots. Finally, using the KRR free oil porosity seismic prediction model, the free oil porosity information in seismic elastic parameters and sensitive seismic attributes is deeply mined to achieve quantitative prediction of free oil porosity, with a prediction accuracy of 90%, which is more than 20% higher than the prediction accuracy of conventional methods.

The application provides a kind of anisotropy parameter inversion method, device, equipment and storage medium, comprising: establishing the anisotropy petrophysical model of shale of multi-mineral mixture, complex pore combination, containing anisotropic clay;Rock physical test is carried out, and petrophysical test data and rock sample anisotropy parameter are acquired;In combination with petrophysical test data, petrophysical analysis is carried out, and the petrophysical model and related parameters suitable for shale rock structure and theoretical model physical mechanism are obtained;The anisotropy petrophysical model of shale based on petrophysical analysis is used to carry out the anisotropy petrophysical model of shale based on petrophysical analysis to the pore aspect ratio and clay orientation degree inversion of logging data;The parameters obtained by inversion are substituted into the anisotropy petrophysical model of shale, and the anisotropy parameter is solved in combination with the definition of Thomsen anisotropy parameter.The inversion result in the application has reasonable theoretical support and actual petrophysical significance.

The invention discloses a sea area deep layer flat lake group sandstone reservoir-oriented gas-bearing prediction method and device, and the method comprises the steps: determining gas-bearing sensitive parameters of a reservoir in a research area through surface rock physical analysis, and determining a fluid factor expression; an elastic parameter body is obtained through pre-stack inversion, and a fluid factor expression is adopted for calculation to obtain a fluid factor data body; superposing the pre-stack gathers of the research area into a plurality of part angle superposition bodies, analyzing the frequency spectrum characteristics of a gas-bearing layer section, a water-bearing layer section and a non-reservoir layer section of a three-dimensional data body around a well of each part angle superposition body, and then performing frequency spectrum decomposition by adopting a high-precision matching pursuit algorithm; analyzing the high-frequency attenuation gradient attribute characteristics of the plurality of partial angle superposition bodies, and performing differentiation processing to obtain a final sensitive attenuation attribute body; and based on the fluid factor data volume and the sensitive attenuation attribute volume, carrying out comprehensive prediction on the gas-bearing property of the research area. According to the method, the multiplicity of solutions of gas-bearing prediction can be reduced, and the gas-bearing prediction precision is improved.

The invention discloses a thin beach reservoir gas-bearing prediction method and device based on optimization attributes, and the method comprises the steps: selecting a typical well in a work area, obtaining sensitive parameters representing a reservoir in a research area through the physical analysis of surface rocks, and constructing a reservoir indication factor; based on the characteristics of the sensitive parameters, selecting a corresponding inversion method to calculate a reservoir indicator factor data volume; superposing the pre-stack gathers into a plurality of partial angle superposition bodies, analyzing the frequency spectrum characteristic difference of the gas-bearing layer section and the non-gas-bearing layer section of the three-dimensional data body around the well of the partial angle superposition bodies, and then performing frequency spectrum decomposition to obtain the time-frequency spectrum of each partial angle superposition body; performing differentiation processing on the high-frequency attenuation gradient attributes of the minimum-angle superposition body and the maximum-angle superposition body to obtain a final sensitive attenuation attribute body; and by taking the reservoir indicator factor data volume as a constraint condition, optimizing the sensitivity attenuation attribute volume to form an optimized composite gas-bearing sensitive attribute volume. According to the method, the multiplicity of solutions of gas-bearing prediction can be effectively reduced, and the gas-bearing prediction precision is improved.

The invention provides a rock physical analysis method for a tight oil reservoir, and belongs to the technical field of oil-gas field development engineering, the method comprises the following steps: obtaining logging information, explaining a sweet spot attribute curve, and selecting sweet spot attributes for evaluating the tight oil reservoir; classifying the sweet spot attributes in combination with geological knowledge; explaining elastic parameters from the logging information, calculating class distances of dessert attributes under each elastic parameter by using the elastic parameters and the dessert attribute classification result, and performing summation; sorting according to the class distances, selecting elastic parameters, respectively constructing and comparing the relevancy of a linear sensitive factor, a nonlinear sensitive factor and a dessert attribute curve, and selecting a construction method of a reservoir sensitive factor; and reservoir sensitive factors are constructed and calculated, and rock physical analysis of the tight oil reservoir is completed. According to the method, tight oil reservoir rock physical analysis can be quantitatively carried out, the accuracy of rock physical analysis is improved, and a basis is provided for subsequent sweet spot prediction and oil and gas exploitation.

The invention relates to a method and system for describing the development scale of an ultra-thin coal seam, and the method comprises the steps: determining the longitudinal distribution rule of the coal seam through the paleoclimate, paleo-water depth, paleo-structure and paleo-sedimentary environment; according to the coal seam plane distribution rule, judging whether the coal seam conforming to the coal seam longitudinal distribution rule belongs to a river-marsh phase or a lake-marsh phase; according to rock physical analysis of a target layer of sea area drilling, comparison is carried out in combination with a coal seam confirmed by drilling, and a plurality of attributes with a good recognition effect on a coal-seam-containing section are selected; carrying out attribute identification on the coal-containing seam section of the river and marsh facies or the lake and marsh facies according to the attributes; inputting an attribute identification result into a neural network model, and outputting a thickness prediction map containing the coal seam section; and obtaining a thickness prediction map of a pure coal seam according to the average coal content percentage and the thickness prediction map of the coal-containing seam section, so as to describe the development scale of the ultra-thin coal seam. The method is high in operability, wide in application range, low in cost and high in precision, and has important significance for guiding oil-gas exploration for developing a coal series hydrocarbon source rock basin.

The application discloses a three-dimensional pore pressure prediction method and device based on rock physical analysis, a storage medium and a program, belongs to the technical field of oil and gas seismic exploration and drilling, and comprehensively uses logging data and three-dimensional seismic data to determine the content of each mineral component in different lithological strata by adopting a rock physical analysis method, so that a more accurate rock physical elastic modulus range is obtained, a precise data basis is provided for stratum pore pressure prediction, a high-precision pore pressure prediction data body is obtained, a reasonable drilling fluid density is designed, accidents such as blowout and lost circulation are reduced, and important data support is provided for safe and rapid drilling and completion.

The present invention provides a well logging lithofacies classification method that integrates a rock physics model and Bayesian inversion. This method includes: Step 1: Detailed analysis of local geological and physical rock characteristics to identify and classify lithologies; Step 2: Performing lithofacies petrophysical analysis to obtain a statistically representative training database; Step 3: Performing Markov Chain Monte Carlo random inversion based on Bayesian inversion; and Step 4: Synthesizing the well logging lithofacies data with actual lithofacies data. This method overcomes the traditional difficulty in solving high-dimensional integrals by using simulation to computationally solve high-dimensional integrals, reducing the difficulty and increasing the practicality of the method.