311 results about "Reservoir evaluation" patented technology

The invention relates to the technical field of reservoir evaluation, in particular to a deep learning-based reservoir evaluation model construction method and system. The method comprises the following steps: acquiring logging data and core analysis data of a target work area, and respectively carrying out abnormal value processing and missing value filling to obtain complete logging data and complete core data; calculating reservoir pore structure parameters based on the complete core data; and inverting the macroscopic porosity of the reservoir based on the complete logging data. According to the method, the accuracy, the stability and the robustness of reservoir evaluation are remarkably improved by fusing the core data and the logging data and combining the pore structure parameters, the macroscopic porosity and the geological attention mechanism, the problems of data missing and small samples are solved, and the interpretability and the geological consistency of the model are enhanced.

The invention provides a pore-fracture-based shale reservoir classification evaluation method and system, and relates to the technical field of oil and gas geology, and the method comprises the steps: S1, scanning an electron microscope image of a shale reservoir to be classified and evaluated, and recognizing different types of reservoir spaces of the shale reservoir; s2, reservoir space parameters of the shale reservoir are extracted according to the recognition result of the reservoir space; s3, calculating evaluation parameters of the shale reservoir based on the reservoir space parameters and a pre-obtained view field area of the shale reservoir; s4, inputting the evaluation parameters and the pre-calculated weight value of each evaluation parameter into a pre-constructed classification evaluation model, and calculating to obtain an evaluation coefficient of the shale reservoir; and S5, based on the evaluation coefficient, performing classification evaluation on the shale reservoir. According to the method, quantitative evaluation can be carried out on the shale reservoir on the microcosmic aspect, and compared with a traditional shale reservoir evaluation method, the shale reservoir evaluation is more visual and accurate on the basis of the microcosmic characteristics of the pore fractures.

The invention belongs to the technical field of evaluation, development and research of oil and gas reservoirs, and discloses a T2 cutoff value calculation method based on a saturated nuclear magnetic resonance experiment. The method comprises the following steps: acquiring a saturated / bound water nuclear magnetic resonance T2 spectrum curve of a compact sandstone sample; determining a T2 cut-off value based on a saturated / bound water nuclear magnetic resonance accumulative porosity curve, and recording the T2 cut-off value as T2-Cuff; acquiring a logT2-logphi intersection curve of the saturated nuclear magnetic resonance T2 spectrum, and recording a T2 value corresponding to an inflection point of the curve as T2-Log; and determining a mathematical relationship between the T2-Log and the T2-Cuff, and establishing a T2 cut-off value calculation model based on the saturated nuclear magnetic resonance experiment. According to the method, the T2 cutoff value can be calculated only by performing the nuclear magnetic resonance experiment on the saturated water sample and performing mathematical treatment on the T2 spectrum, and the method has the advantages of being economical, simple, efficient and high in practicability.

The invention provides a digital rock core three-dimensional reconstruction method containing pore and mineral distribution, and relates to the field of oil-gas exploration and development. The three-dimensional reconstruction method of the digital rock core containing pore and mineral distribution comprises the following steps: (1) marking positioning information on a sample; (2) acquiring pore three-dimensional information of the rock core sample; (3) obtaining a mineral analysis cross section; (4) carrying out QEMSCAN quantitative analysis; and (5) establishing a three-dimensional mineral model. According to the method, a multi-element three-dimensional mineral model of pore and mineral distribution is established, so that reservoir characterization and rock parameter simulation are better carried out, and a basis is provided for later reservoir evaluation and favorable exploration area selection.

The invention discloses a reservoir identification method fusing seismic attribute inversion and ancient karst form modeling, and relates to the technical field of reservoir identification and modeling, and the method comprises the steps: carrying out the multi-modal fusion processing of a standardized image data set through an image sensor, obtaining the multi-modal fusion image data, executing the high-precision texture sampling and space alignment processing, and obtaining a multi-modal image data set; generating a cross-modal image fusion matrix; inputting the cross-modal image fusion matrix into an image topological graph structure construction process, establishing an image block node relationship and a spatial connection edge weight, and generating an ancient karst topological graph; and coupling the karst earthquake joint matching graph with the comprehensive reservoir attribute data, establishing an effective reservoir evaluation model, and generating a multi-stage reservoir prediction layer set through weighted fusion and multi-parameter spatial analysis. By constructing an ancient karst topological graph and a karst earthquake joint matching graph, spatial modeling of a complex karst structure and cross-modal correlation analysis of earthquake abnormal attributes are realized.

The invention provides a method for characterizing the heterogeneity of a millimeter-level shale oil and gas reservoir. The method mainly solves the problem that the reliability and accuracy of a reservoir evaluation result of an existing volume sampling method are low. The method is characterized by comprising the following steps that S1, shale millimeter-level mineral components are recognized and drilled; s2, based on the obtained powdery sample, oil-containing and non-oil-containing tests are carried out; determining occurrence pore size distribution to carry out reservoir characterization through comparison before and after pumping; s3, on the basis of the obtained powdery sample, carrying out oiliness characterization on different lamina / layered components; and S4, carrying out millimeter-level shale oil reservoir heterogeneity characterization on the basis of the reservoir property and oiliness results of the different strata / layered components. According to the method provided by the invention, the identification precision of the shale formation / stratified structure can be enhanced, millimeter-level fine representation of lithology of different formation / strata is realized, and favorable support is provided for shale oil enrichment mechanism, sweet spot prediction and the like.

The invention relates to a method, a device and equipment for analyzing in-situ formation fracture porosity of mixed rock. Comprising the steps of obtaining original data of a plurality of layers in a target block; taking out a first rock core sample from each layer of the target block, and performing experimental analysis on the first rock core sample of each layer to determine a target layer; taking out a second core sample from the target horizon, and performing a triaxial compression test on the second core sample to obtain a stress index; according to the original data and the stress index, the stratum fracture porosity of the target layer is calculated, the stratum fracture porosity is used for guiding workers to evaluate the real underground in-situ fracture porosity of the reservoir, and therefore an oil and gas reservoir exploitation plan of the target layer is made. The stratum fracture porosity calculated by the method represents the fracture porosity of the target horizon in the target block and represents in-situ characteristics in the stratum, so that the accuracy of reservoir evaluation is improved. A worker can formulate an oil and gas reservoir exploitation plan according to the formation fracture porosity, and the oil and gas reservoir yield is improved.

The invention discloses a compact glutenite reservoir evaluation method based on a lithofacies-pore throat structure and application. The method comprises the following steps: step 1, establishing a lithofacies division scheme in a compact glutenite reservoir; 2, determining the reservoir performance of different lithofacies in the compact glutenite reservoir; 3, determining reservoir space types and features of different lithofacies in the compact glutenite reservoir; 4, analyzing pore throat structure characteristics of different lithofacies in the compact glutenite reservoir to obtain multi-type pore throat structure parameters; 5, establishing a reservoir performance and pore throat structure parameter database of different lithofacies in the compact glutenite reservoir; step 6, carrying out compact glutenite reservoir clustering by utilizing a Ward system clustering method; and step 7, in combination with productivity conditions and physical characteristics of different lithofacies in the compact glutenite reservoir, proofreading and perfecting a reservoir clustering result, and finally establishing a compact glutenite reservoir classification evaluation scheme. The invention further discloses application of the method in oil exploitation.

The invention discloses a reservoir parameter prediction method and device based on logging information and a storage medium, and relates to the technical field of petroleum earth exploration, and the key points of the technical scheme are that the method comprises the following steps: obtaining existing reservoir parameters and logging curve data of a work area; constructing a first sample set according to the existing reservoir parameters and the logging curve data, and performing normalization processing on the first sample set; constructing a convolutional neural network model, and training the convolutional neural network model to obtain an optimal intelligent model; constructing a second sample set for the unknown well, and predicting reservoir parameters of the unknown well through the intelligent model based on the second sample set; and performing interpolation and reverse normalization processing on the unknown well reservoir parameters, and outputting the processed unknown well reservoir parameters. By means of the prediction method, logging longitudinal correlation information can be reserved, the calculation error evaluation problem caused by the fact that the core reservoir parameters do not correspond to the logging curve depth is solved, and therefore the calculation requirement of the reservoir parameters in logging reservoir evaluation is met.

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 belongs to the technical field of oil and gas reservoir evaluation, development and research, and discloses a compact sandstone permeability calculation method based on a saturated nuclear magnetic resonance experiment, which comprises the following steps: carrying out a permeability determination experiment on a compact sandstone sample to obtain a sample permeability value, and recording the sample permeability value as KEm; carrying out a saturated nuclear magnetic resonance experiment on the compact sandstone sample to obtain a nuclear magnetic resonance T2 spectrum curve; logarithms of the nuclear magnetic resonance T2 spectrum and the porosity component # imgabs0 # are taken, a # imgabs1 # intersection curve is constructed, and inflection points of the curve are marked as T2-1 and T2-2 respectively; taking T2-1 and T2-2 as independent variables and KEm as a dependent variable, and establishing a compact sandstone permeability calculation model based on a saturated nuclear magnetic resonance experiment; recording a permeability value calculated by the permeability calculation model as KCal, establishing a KCal-KEm linear relation, and judging the effectiveness of the model according to the correlation coefficient of the KCal and the KEm. According to the method, accurate calculation of the permeability of the tight sandstone reservoir can be realized only by carrying out a saturated nuclear magnetic resonance experiment, and the method has the advantages of being economical, simple, efficient and high in practicability.

The invention discloses a rock permeability testing system in a high-temperature and high-pressure environment and a using method thereof, and belongs to the technical field of geothermal development or coal in-situ gasification mining. The test system is composed of a stress loading system, a test kettle assembly and a magnetic particle imaging system. When in use, the stress loading system and the test kettle assembly provide conditions such as high temperature, high pressure, seepage fluid and the like for a test piece; and detecting the spatial distribution state of the nano magnetic induction particles added in the seepage fluid through a magnetic particle imaging system to observe the pore and crack distribution characteristics in the test piece or detecting the nano magnetic induction particles added in the pollutant solution to observe the pollutant migration condition. The test system is scientific in design, simple in structure and easy to operate and maintain, can monitor pore fracture distribution and pollutant migration conditions in the test piece in real time under high-temperature and high-pressure conditions, provides accurate dynamic observation data, and provides important technical support for reservoir assessment and optimization in a complex environment.

The invention discloses a salt lake carbonate rock multi-parameter quantitative classification and reservoir evaluation method, and relates to the technical field of oil-gas exploration. The method comprises the following steps: selecting a key well in an exploration block, collecting a plurality of salt lake carbonate rock samples, preparing an analysis sample under an anhydrous condition, quantitatively measuring a salinity parameter, a mixing index and a diagenesis strength parameter by utilizing the analysis sample, and determining a salinity code, a mixing code and a diagenesis phase code. And naming the salt lake carbonate rock based on the salinity code, the mixed product code and the lithogenous phase code, directly evaluating the salt lake carbonate rock reservoir according to a naming result, and performing single well comprehensive evaluation on the salt lake carbonate rock reservoir. According to the method, the influence of salinity, mixed accumulation and diagenesis strength on formation and evolution of the salt lake carbonate rocks is fully considered, accurate division of the types of the salt lake carbonate rocks is achieved, the reservoir naming result is directly associated with reservoir evaluation, and technical support is provided for standardized evaluation of the salt lake carbonate reservoir.

The invention discloses a metamorphic rock surface porosity analysis and evaluation method, device, equipment and medium, and relates to the technical field of petroleum logging reservoir evaluation, and the method comprises the steps: preprocessing a rock debris sample, scanning the preprocessed rock debris sample, and obtaining a rock debris sample image; performing identification detection on mineral and background color edges in the rock debris sample image and rock debris gap image segmentation to obtain rock debris particles, and determining the total area of the particles; performing threshold truncation on the rock debris particles by adopting a threshold segmentation method, calculating pore areas in the rock debris particles, identifying cracks in the rock debris particles by adopting a maximum likelihood method, calculating crack areas in the rock debris particles, and calculating pore and crack areas by utilizing the pore areas and the crack areas; and calculating the surface porosity based on the total particle area and the pore and crack area, and analyzing and evaluating the imaging logging crack interpretation result and the surface porosity to obtain an analysis and evaluation result. The metamorphic rock surface porosity analysis and evaluation accuracy and efficiency can be improved, and metamorphic rock surface porosity analysis and evaluation complexity can be reduced.

The invention relates to the field of oil-gas exploration, and discloses a reservoir evaluation parameter determination method and device, a storage medium and electronic equipment. The method comprises the steps that according to the while-drilling gamma of the horizontal well, the logging element content and a preset logging curve, a passing trajectory tracking operation is executed, so that a passing horizon and a horizontal well stratum model are determined; determining a horizontal well reference curve according to a preset logging curve, the element curve and the horizontal well stratum model; according to the reference curve of the horizontal well, the preferable actual measurement well logging curve of the pilot hole well corresponding to the horizontal well passing horizon and the element logging data of the horizontal well, the actual measurement well logging curve of the horizontal well and the logging element content of the horizontal well are corrected, and the corrected well logging curve and the corrected logging element content are obtained; generating an evaluation parameter determination model according to the corrected logging curve and logging element content and preset modeling data; and determining evaluation parameters of the horizontal well reservoir according to the evaluation parameter determination model. And the accuracy of determining the reservoir evaluation parameters is higher.

The invention discloses a tight sandstone reservoir evaluation method and system based on a knowledge graph and a storage medium, relates to the technical field of geological exploration and development, and aims to solve the problems of data splitting and model staticizing existing in an existing evaluation method. Comprising the steps that S100, tight sandstone reservoir evaluation data are collected, an ontology knowledge base is constructed, and a tight sandstone reservoir domain ontology layer is constructed based on knowledge and the incidence relation between the knowledge; step S200, a tight sandstone reservoir evaluation model is constructed, the evaluation model comprises a knowledge extraction module and a knowledge reasoning module, and the knowledge extraction module is used for performing triple extraction on the entity relationship marked by the tight sandstone reservoir sequence, and constructing a tight sandstone reservoir knowledge graph combined with the entity relationship; the knowledge reasoning module is used for sensing a convolutional network based on a structure embedded by a rule, and is used for carrying out collaborative reasoning of multiple tasks; and S300, based on the tight sandstone reservoir evaluation model, performing multi-task collaborative reasoning of the tight sandstone reservoir.

The invention discloses a multivariable inversion method for measuring the neutron porosity of a buried hill reservoir, and belongs to the technical field of nuclear logging. The multivariable inversion method specifically comprises the following steps: constructing a neutron logging instrument array detector, measuring the buried hill reservoir, calculating a counting ratio R, and defining R1 and R2; constructing a multivariable inversion model, and performing inversion to obtain Por, Por1, Por2, cal and OFFSET; and the relative eccentricity OFFSETRE is defined, iteration inversion is conducted on the OFFSETRE through a dichotomy method, and the well diameter, the relative eccentricity and the porosity are output to serve as inversion results. The method can effectively reduce the negative influence of dual environment effects generated by well diameter fluctuation and instrument eccentric distance change on a logging result, remarkably alleviates the complexity and difficulty of environment correction, greatly improves the accuracy and reliability of neutron porosity measurement of the buried hill reservoir, and has a great significance in improving the oil-gas exploration and development efficiency. And the reservoir evaluation precision is optimized, and the method has extremely important practical significance and application value.

The present application discloses a method for analyzing free gas saturation and gas occurrence ratio based on pore space, and relates to the technical field of coal rock development. The method comprises: conducting coal rock pore-related experiments to obtain the total pore space, which includes pore adsorption space and pore free space; establishing a free gas saturation calculation model; based on the pore adsorption space and pore free space, in combination with the free gas saturation calculation model, establishing an adsorbed gas and free gas quantity calculation model; based on the adsorbed gas and free gas quantity calculation model, establishing an adsorbed gas and free gas content relative ratio calculation model, and determining the relative proportion of adsorbed gas and free gas content. The present application can more comprehensively grasp the occurrence form of gas in coal rock, the content distribution characteristics and the relationship between the gas and the reservoir pore structure, reveal the migration mechanism and process of gas in the reservoir, and provide a solid theoretical support for subsequent reservoir evaluation.

The invention relates to a lithofacies intelligent pattern classification method based on a conditional diffusion probability model, and belongs to the technical field of deep learning, pattern recognition and big data processing. Comprising the following steps: step (1), collecting and preprocessing big data; step (2), constructing a conditional diffusion probability model; step (3), model training and parameter optimization; step (4), generating a category balance sample; (5) enhancing data quality evaluation; and (6) training and verifying the pattern classification model. According to the method, shale lithofacies data enhancement and intelligent mode classification based on the conditional diffusion probability model and the deep learning technology are realized, the lithofacies identification problem under the condition of training set category imbalance is effectively solved, and the minority class lithofacies identification precision and the overall classification performance of the model are remarkably improved; and a reliable technical method is provided for marine shale oil and gas reservoir evaluation and sweet spot prediction.

The invention discloses a shale reservoir classification evaluation method based on machine learning and optimal fractal attributes, and relates to the technical field of shale reservoir evaluation. Comprising the following steps: calculating fractal attribute indexes of a T2 spectrum, obtaining a new clustering label, obtaining optimal fractal attribute values, obtaining a final classification label, applying a final classification basis to all optimal fractal attribute values of a target well, and obtaining a new classification result; multi-fractal calculation is carried out on T2 spectrum original data obtained through nuclear magnetic logging, effective porosity data obtained through combination of corresponding well section closed coring data is taken as the basis, after effective porosity of different degrees is classified, systematic machine learning training is carried out on fractal attributes corresponding to each category, and fractal fractal data are obtained. And a set of classification interval of fractal attribute values mainly based on the fractal attribute with the maximum influence degree is obtained and is applied to all fractal attribute values of the target interval, so that the shale reservoir can be accurately classified and evaluated.

The invention discloses a logging depth correction method and system and a computer readable storage medium, and the method comprises the following steps: determining a reference well and at least one correction well needing depth correction in a plurality of wells under the same oil field pressure system; establishing a reference well pressure trend model according to the measurement data of the reference well; establishing a corrected well pressure trend model according to the measured data of the corrected well; according to the reference well pressure trend model and the correction well pressure trend model, a depth correction value is obtained through analysis; and correcting the measured depth value of the correction well according to the depth correction value. According to the method, the precision of depth measurement can be improved, the precision of oil and gas reservoir evaluation is further improved, and the method is high in operability.

The invention provides a shale reservoir grading evaluation method. The shale reservoir grading evaluation method comprises the following steps: acquiring petrology, geochemistry and pore structure parameters of different shale reservoirs by utilizing at least two experimental test methods; calculating a multi-scale fractal parameter of the pore structure of the shale reservoir; according to the obtained petrology, geochemistry, pore structure and multi-scale fractal parameters, reservoir evaluation is conducted, and shale reservoir grading is completed. According to the method, shale reservoir petrology, geochemistry and pore structure parameters are obtained by comprehensively utilizing multiple means, pore structure multi-scale fractal parameters are calculated, reservoir characteristics are evaluated by combining multiple types of parameters, and reservoir grading evaluation is completed; the problem that a single test method or a single calculation method cannot systematically analyze the pore structure of the reservoir, divide the type of the reservoir and carry out reservoir evaluation is avoided, and the accuracy, effectiveness and comprehensiveness of grading evaluation of the shale reservoir are improved.

The invention discloses a method for testing lost gas in shale gas content based on a depletion experiment device, and relates to the technical field of oil-gas field development, the depletion experiment device comprises a rock core holder, a heating jacket, a confining pressure booster pump, a gas inlet assembly, a gas outlet assembly, a vacuumizing device, a gas reference container and an arc radiation trough plate, the loss gas in the shale gas content is tested based on the depletion experiment device, the characteristic that the horizontal permeability of the shale is far greater than the vertical permeability is fully utilized, the testing device completely conforming to the real environment is designed, on one hand, the shale core can be really saturated by methane, the time required for saturating the shale core is greatly shortened, and on the other hand, the loss gas in the shale gas content is greatly reduced; on the other hand, the lost gas part in the shale gas content can be directly tested, the testing efficiency is improved, the accuracy of the testing result is also improved, and therefore powerful evidence support is provided for shale gas reservoir evaluation and reserve calculation.

This application relates to the field of data technology and discloses an intelligent processing method for mud logging data. The method includes: collecting data through multi-source mud logging equipment and preprocessing to form a result set; identifying and classifying formation characteristics according to the result set; analyzing oil and gas parameters based on the formation atlas and the result set; establishing an early warning mechanism by identifying risks using the oil and gas evaluation table and the result set; evaluating reservoir physical properties according to the oil and gas evaluation table and the early warning mechanism; integrating the formation atlas, the oil and gas evaluation table and the reservoir report to reconstruct the geological structure and predict the enrichment area. This application realizes the full-process intelligent processing from the acquisition and preprocessing of multi-source heterogeneous mud logging data to formation identification, oil and gas evaluation, drilling risk early warning, reservoir evaluation and oil and gas reservoir description, overcomes problems such as data spatio-temporal mismatch, isolated parameter analysis, strong subjectivity in evaluation, and lagging early warning in traditional methods, and improves the efficiency, accuracy and systematicness of mud logging data processing.

The invention provides a reservoir sedimentary microfacies depicting method based on artificial intelligence image recognition, and belongs to the technical field of oil and gas reservoir evaluation, and the method comprises the following steps: collecting and sorting different types of sedimentary microfacies mode patterns, and constructing a sedimentary microfacies mode pattern library; carrying out image preprocessing operation; after preprocessing, key feature parameters are extracted from the image; training the features by using a deep learning algorithm to obtain an identification rule and a decision rule, and constructing a sedimentary microfacies diagram identification model; evaluating and verifying the trained model, and adjusting and optimizing the model according to feedback; and inputting limited constraint data into a sedimentary microfacies diagram recognition model, and optimizing and matching a microfacies mode through a related matching algorithm and a correction rule, so as to calculate and draw a microfacies plane diagram and a profile diagram which conform to a sedimentary mode. According to the method, the reservoir sedimentary microfacies can be automatically, efficiently and accurately identified and depicted, the working efficiency is greatly improved, the accuracy is improved, and the labor cost is reduced.

The invention relates to an evaluation and optimization method for continental shale oil dessert intervals, which comprises the following steps: calculating an oil production evaluation coefficient of each sample according to the organic carbon content and petroleum potential parameters; calculating a reservoir evaluation coefficient of each sample according to the shale porosity; calculating an oiliness evaluation coefficient of each sample according to the free oil quantity; calculating a compressibility evaluation coefficient of each sample according to the quartz content, the carbonate mineral content and the clay mineral content; calculating the shale oil dessert layer segment coefficient of each sample according to the oil-generating property evaluation coefficient of each sample, the storage property evaluation coefficient of each sample, the oiliness evaluation coefficient of each sample and the compressibility evaluation coefficient of each sample, and performing data distribution proportion analysis on the shale oil dessert layer segment coefficients of all the samples in the plurality of samples to obtain the shale oil dessert layer segment coefficient of each sample. And determining an evaluation grading standard to complete well drilling shale oil dessert interval evaluation optimization, so that technical support can be provided for realizing high yield and stable yield of a shale oil well.

The invention provides a rock sample pore wettability evaluation method, device and system and electronic equipment, relates to the technical field of reservoir evaluation, and aims to solve the problem that the wettability of different pores of rock cannot be represented. The evaluation method comprises the following steps: acquiring a first three-dimensional grayscale image of a rock sample in a normal state, a third three-dimensional grayscale image of a wet-phase fluid filled with a tracer agent in the rock sample, and a fourth three-dimensional grayscale image of the wet-phase fluid and a non-wet-phase fluid filled with the tracer agent in the rock sample; and determining the pore wettability of the rock sample according to the first three-dimensional grayscale image, the third three-dimensional grayscale image and the fourth three-dimensional grayscale image. According to the evaluation method provided by the invention, the high-resolution three-dimensional imaging technology is combined with the distribution of the interface form of multiphase fluid seepage in a stable state, so that the wetting state in each rock sample pore can be quickly evaluated one by one, and the evaluation method has important significance on exploring the influence of different wetting conditions on fluid properties.

The invention relates to the technical field of reservoir assessment, and discloses a gas storage geologic body stability intelligent prediction method considering configuration heterogeneity, and the method comprises the following steps: 1, reservoir configuration unit division and feature extraction; 2, carrying out coupling analysis on geomechanical parameters and a crustal stress field; step 3, carrying out correlation modeling on configuration heterogeneity and mechanical characteristics; and 4, training and optimizing the stability intelligent prediction model. According to the method, deep coupling characterization of geologic configuration heterogeneity and mechanical response is achieved, and the physical basis and precision of stability prediction are remarkably improved.

The invention provides a sandstone reservoir oil-water layer logging identification method and system, and belongs to the field of reservoir evaluation. According to the method, an identification plate is obtained according to oil testing and production testing data and gas logging data, and then the fluid type of a reservoir to be identified is identified through the identification plate. According to the method, a conventional gas logging technology is adopted, new technical means and measurement time do not need to be added, the fluid type of the sandstone reservoir can be simply, conveniently and rapidly recognized based on deep excavation of gas logging data, the recognition coincidence rate is increased, a basis is provided for exploration and development decision and benefit development, and the method has the advantages of being economical, real-time, accurate and the like and has good application prospects. The popularization and application prospects are wide.

The invention relates to the technical field of reservoir evaluation in petroleum geological exploration sedimentary reservoirs, in particular to a method for calculating shale movable oil saturation based on a nuclear magnetism-centrifugation principle, which comprises the following steps: processing to obtain a sample after oil washing; based on the underground real gas-oil ratio, crude oil and natural gas are used for sample preparation, and prepared crude oil is obtained; pressurizing and saturating the prepared crude oil to obtain an oil-saturated rock core sample; calculating the total pore volume V of the rock core under the real underground temperature and pressure condition; obtaining the total volume V1 of the oil-saturated rock core sample at normal temperature and normal pressure, and calculating the oil saturation S1 of the light hydrocarbon lost part at room temperature; the ground movable fluid saturation S2 is obtained through calculation, and finally the real movable oil saturation S of the shale is obtained through calculation. By means of the method, the gas-oil ratio closer to the real geological condition is recovered, the viscosity of crude oil can be reduced, the viscosity is closer to the real condition, the experimental error is reduced to the maximum extent, and the calculation result is more accurate.