1583 results about "Logging" 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 relates to the technical field of data, and discloses a logging data intelligent processing method. The method comprises the following steps: acquiring data through a multi-source logging device and preprocessing the data to form a result set; identifying and classifying stratum features according to the result set; analyzing oil and gas parameters based on the stratigraphic map and the result set; identifying risks by using the oil-gas evaluation table and the result set and establishing an early warning mechanism; the reservoir physical property is evaluated according to the oil and gas evaluation table and the early warning mechanism; and integrating the stratigraphic map, the oil and gas evaluation table and the reservoir report to reconstruct the geological structure prediction enrichment area. According to the method, full-process intelligent processing from acquisition and preprocessing of multi-source heterogeneous logging data to stratum identification, oil and gas evaluation, drilling risk early warning, reservoir assessment and oil and gas reservoir description is realized, and the problems of data space-time mismatching, parameter isolated analysis, high evaluation subjectivity, early warning lag and the like in a traditional method are solved; and the efficiency, accuracy and systematicness of logging data processing are improved.

The invention discloses a high-precision logging lithology intelligent identification method based on DenseNet-Transformer depth fusion. The method comprises the following steps: step 1, carrying out data preprocessing; and step 2, constructing a deep learning model in which a DenseNet mechanism and a Transform mechanism are fused. According to the method disclosed by the invention, efficient feature multiplexing is realized by using the dense connection characteristic of the DenseNet through a deep learning technology, and the global feature modeling capability is enhanced in combination with the Transform network, so that the lithology identification accuracy and generalization capability are remarkably improved. The method is suitable for intelligent analysis of complex logging data, especially shows excellent performance in the aspects of high-dimensional data processing and fine feature extraction, greatly improves the recognition difficulty of a traditional method in a complex stratum structure, and provides important technical support and efficient decision basis for the fields of oil-gas exploration, geological survey and the like.

The invention relates to the technical field of nuclear power internal disaster analysis, and discloses a nuclear power plant internal flooding experiment verification method, device, equipment and medium, and the method comprises the steps: carrying out the modeling analysis of a target plant prototype, and determining a plurality of model parameter values of an experiment scaling model; constructing an experiment scaling model according to the plurality of model parameter values, and determining experiment parameters of an internal water logging experiment; performing an internal water logging experiment on the experimental scale model by using the experimental parameters to obtain an internal water logging experiment result of the experimental scale model; and obtaining an internal water logging simulation result of the numerical model, comparing the internal water logging simulation result with an internal water logging experiment result, and verifying the accuracy of the internal water logging simulation result according to a comparison result. According to the method, the experiment scale model of the nuclear power plant prototype is constructed, the flooding spreading conditions under different working conditions are simulated and compared with the flooding simulation result, the accuracy of flooding simulation calculation is verified, and more accurate guidance is provided for follow-up nuclear power project internal flooding analysis.

The invention discloses a logging lithofacies intelligent identification method of an unbalanced sample, and a related method and device. The method comprises the following steps: establishing a lithofacies sample data set according to pre-obtained logging attributes corresponding to various rock phases of an actual well in a research area, and sampling to obtain a plurality of first sample subsets; for each first sample subset, calculating a mean value of each logging attribute corresponding to all the sample data of each type of lithofacies to obtain each attribute feature of each type of lithofacies, further determining the weight of each attribute feature, and further calculating a weighted feature parameter of each type of lithofacies to obtain a second sample subset; inputting each second sample subset into a base classifier trained based on the resampled lithofacies sample data set for identification, and determining a logging lithofacies identification probability result in each base classifier; and determining lithofacies types of different depths of the actual well in the research area according to the predetermined combined weight of each base classifier and the corresponding logging lithofacies identification probability result.

The invention discloses a dual-model-driven intelligent reservoir logging evaluation method, which comprises the following steps of: establishing a lithology identification logging data set based on a rock slice identification result and conventional logging data, and constructing a data model-driven lithology identification pre-training model; establishing a porosity prediction data set of different lithology based on a core porosity analysis result, and constructing a dual-model driven porosity prediction model of a data model and a knowledge model under the guidance of a lithology classification result; establishing a permeability prediction data set of different lithology based on a permeability analysis result, and constructing a dual-model driven permeability prediction model of a data model and a knowledge model under the guidance of a lithology classification result; on the basis of different lithologic saturation parameters measured in a laboratory, the water saturation of different lithologies is calculated in combination with physical property prediction results driven by double models of different lithologies and conventional logging data. The method can effectively solve the well logging evaluation problem faced by a complex reservoir.

The invention relates to a response characteristic calibration method, device and equipment of logging equipment and a storage medium. The method comprises the following steps: carrying out two-parameter response modeling on key parameters of logging equipment, and constructing a pressure-fluidity coupling response model; data acquisition parameters and signal filtering parameters of logging equipment are configured in a partitioned manner, and a multi-layer response parameter set is established. And processing the logging pressure and fluidity data sequence to obtain a logging data deviation characteristic spectrum and a response anomaly type matrix. And performing multi-target parameter optimization calculation on different layers of response parameter sets to generate a layered calibration parameter sequence. The layered calibration parameter sequence is analyzed, response benchmark analysis, response gain analysis and response compensation analysis are achieved, and a multi-layer response calibration instruction is obtained. And the calibration instruction is subjected to a comparison test under three working conditions, and a pressure-fluidity two-parameter calibration curve is generated. According to the method, accurate calibration of the logging equipment under various working conditions is realized, and the reliability of a calibration result is ensured.

The invention discloses a saw chain guide plate lubricating system which comprises an integrally-formed guide plate body and is characterized in that a driven supporting structure is arranged at one end of the guide plate body, a rotating structure is installed on the driven supporting structure, and a transmission wheel is installed on the rotating structure; a saw chain guide groove is formed in the side face of the guide plate body. The guide plate body and the driven supporting structure are provided with an oil conveying channel, one end of the oil conveying channel is connected with an oil supply unit of the lumbering equipment, and the other end of the oil conveying channel makes contact with the rotating structure. Through the arrangement of the oil conveying channel, the service life of the driven end rotating structure is prolonged, the part, making contact with a tree, of the saw chain can be lubricated, and meanwhile the utilization rate of lubricating oil can be increased.

The invention relates to the technical field of geological reservoirs, in particular to a reservoir microcosmic connectivity characterization method based on pore throat boundary intelligent identification, which comprises the following steps of: performing threshold segmentation processing on a rock casting body slice by using image analysis software, and depicting partition images of pore throats and skeleton particles; based on the partition image, combining image analysis software to measure and extract the diameter, perimeter and area of the pore throat, and calculating quantitative parameters of the pore throat structure; constructing a one-dimensional Gaussian distribution model of the nuclear magnetic logging of the reservoir; based on a one-dimensional Gaussian distribution model, establishing a Gaussian collaborative factor for pore structure evaluation; based on the quantitative parameters of the pore throat structure and the established Gaussian collaborative factors, establishing a multi-factor evaluation mathematical method to establish a multi-element evaluation model of the pore structure; and performing multivariate nonlinear fitting on the porous structure multivariate evaluation model, the nuclear magnetic movable fluid saturation and the reservoir pressure measurement fluidity, and constructing a quantitative characterization model for characterizing the reservoir microscopic connectivity. According to the method, the microscopic connectivity of the reservoir can be accurately, rapidly and quantitatively evaluated.

The invention is suitable for the technical field of mechanical equipment monitoring and control, and provides a mechanical state real-time monitoring system based on the Internet of Things, and the system comprises a data obtaining module which is used for obtaining a real-time monitoring result when a target feller performs felling operation on a target tree according to a preset cutting pressure parameter; real-time vibration data, real-time pressure feedback data and real-time temperature change data of the target feller saw blade within a preset time range are collected; according to the invention, multi-dimensional real-time monitoring of the state of the feller saw blade and dynamic adjustment of the cutting parameters are realized through the Internet of Things technology, and the stability and the intelligent level of equipment operation are effectively improved. By integrating three key parameters of vibration, pressure and temperature, the system provides more accurate saw blade aging evaluation, and solves the problem that a traditional single-parameter monitoring method is easily influenced by the environment and is not comprehensive enough.

The invention relates to a reservoir porosity and permeability prediction method based on a dynamic committee integration model, and the method comprises the following steps: obtaining an original data set which comprises shale content, porosity, permeability, GR, AC, CNL, DEN, RT, RXO, SP, CALC, CALI, depth and lithologic labels; performing data enhancement on the original data set; determining main control factors influencing the porosity and the permeability; constructing a dynamic committee integration model; inputting the main control factors into a dynamic committee integration model; and utilizing the trained dynamic committee integration model to respectively predict the porosity and the permeability. According to the method, the logging data is processed by adopting a machine learning method, reservoir parameters can be efficiently and accurately predicted, and favorable support can be provided for oil-gas exploration and development; the dynamic committee integrated model constructed by the invention can dynamically adjust the weight of each model according to different geological conditions and data features, can more flexibly adapt to the geological condition of a research area compared with a single model, and improves the prediction precision and generalization of the model.

The invention relates to the technical field of oil exploration, and discloses a rock mechanical parameter logging intelligent evaluation method considering continental facies shale fabric characteristics. The method comprises the following steps: 1, dividing shale lithofacies, identifying and determining the bedding development thickness of a sample through an optical microscope, determining a granularity experiment of the sample through granularity analysis, and forming mineral composition and bedding factor lithofacies division; 2, constructing a typical logging facies based on a rock electricity response relationship, and dividing the imaging logging facies of the research area according to natural gamma curves of conventional logging and imaging logging, core deposition structure characteristics and imaging logging image characteristics; 3, testing the longitudinal and transverse wave velocities and the porosity of different shale lithofacies, and calculating the dynamic elastic modulus and the Poisson's ratio. Through fine division and description, the method for accurately evaluating the bedding development condition, the particle size characteristics and the mineral components of the shale sample is provided, the testing time is short, the cost is low, and accurate evaluation of shale can be achieved.

The invention provides a coal mine scour prevention and shock absorption method with ground horizontal wells and vertical wells arranged in a combined mode, and relates to the technical field of coal mining. By means of the coal mine anti-scour shock-absorbing method for combined arrangement of the ground horizontal well and the vertical well, the advantages of fracturing of the ground horizontal well and the vertical well can be effectively fused, and the defects that single ground vertical well or ground horizontal well arrangement is high in construction cost, limited in coverage range and limited in composite disaster control are overcome; the invention provides a coal mine anti-impact damping technical scheme which is effective and good in economical efficiency. The invention provides a determination basis for comprehensively analyzing and judging fracturing position selection and well shaft arrangement positions of a ground horizontal well and a ground vertical well by combining working face data and open hole logging data, and a method for finely judging and optimizing the fracturing position based on a logging result, and establishes a systematization and standardization basis for designing a ground fracturing overall scheme.

The invention relates to the technical field of oil well logging, in particular to a core storage short section and a drilling type well wall coring instrument. The core storage pup joint comprises a core storage pup joint base body, a core detection plectrum mechanism, a plectrum cylinder and a core storage cylinder, the plectrum cylinder is arranged on the periphery of the middle part of the core storage short section base body and is used for storing the plectrum; the core storage barrel is arranged in the core storage short section base body and used for storing the taken core; the rock core detection plectrum mechanism is arranged at the first end of the core storage short section base body and is used for counting the taken rock cores and pushing a plectrum to separate the taken rock cores. The drilling type side wall coring device needs to complete drilling coring and rock core recovery underground to guarantee completeness and storage of rock cores, so that the core storage short section is the key technology of the drilling type side wall coring device, and a structural mode that hydraulic pressure provides power to achieve rock core pushing, rock core separation and counting is provided for the drilling type side wall coring device. And the stability and the reliability of the core storage function of the drilling type side wall coring device are ensured.

The invention relates to a multi-well consistency correction method and system, and the method comprises the steps: obtaining the logging curve data of a plurality of wells; performing logging curve quality evaluation on the logging curve data to select a standard well according to a logging curve quality result; wherein the logging curve data of the standard well is first logging data; the rest logging data are second logging data; substituting the first logging data into a preset rock physical model for fitting to obtain a fitted first model; and performing correction processing based on the first logging data, the second logging data and the first model to obtain second logging correction data. Consistency correction of logging data among multiple wells is achieved, and then the accuracy and reliability of oil and gas exploration and development are improved.

The invention relates to the technical field of logging evaluation of a shale gas reservoir geologic model, and discloses a logging evaluation method suitable for the shale gas reservoir geologic model, which comprises the following steps: collecting and arranging logging data, including natural gamma, resistivity, interval transit time, density and neutron logging curves, based on geological data and logging data of adjacent wells, an initial geological model of the shale gas reservoir is established, a logging curve is preprocessed, including depth correction, environment correction, data smoothing, utilization of response characteristics of the logging curve, identification of cracks and stratification in the shale gas reservoir, and utilization of the logging data to calculate physical property parameters of the shale gas reservoir. According to the logging evaluation method, a plurality of reservoir parameters including the organic carbon content, the thermal maturity, the porosity, the permeability, the water saturation, the fracture development degree and the like can be obtained, and the comprehensive analysis of the parameters can more comprehensively reflect the reservoir characteristics, so that the accuracy of a geologic model is improved.

The invention discloses a reservoir fluid identification method, device and equipment and a medium, and relates to the technical field of oil and gas field exploration. The invention provides a multi-level nested reservoir fluid identification method based on geological mode constraint for double-medium tight sandstone. For different reservoir types, a preset algorithm is utilized to fuse the comprehensive logging parameters and the proposed fluid sensitive factors suitable for the double-medium tight sandstone, and fluid discrimination parameters suitable for different reservoir types are constructed. On the basis, reservoir types are divided by comprehensively considering the coupling action result of the matrix and the tectonic fracture, the difference of fluid characteristics of different reservoir types is determined, and based on the classification constraint of a geological mode, the different reservoir types are divided, and the fluid type of the tight sandstone reservoir is identified by using an integrated identification model. Logging curve data for fluid identification are easy to obtain, the fluid identification precision is high, the identification efficiency is high, and the method can be widely applied to reservoir fluid identification of double-medium tight sandstone.

The invention belongs to the technical field of carbonate rock oil-gas exploration, and particularly discloses a complex carbonate rock logging lithology identification method based on a diffusion model, and the method comprises the following steps: determining the lithology types of a plurality of observation wells based on the on-site rock core observation and slice analysis, and synchronously obtaining the logging data of the corresponding observation wells, constructing a lithology training data set in combination with a depth corresponding relationship between the lithology category and the logging data; a diffusion model is adopted to generate and supplement lithology categories with insufficient samples; logging data is adopted as an input feature, the lithology category is adopted as an output label, and a convolutional neural network fused with a Bayesian optimization algorithm and a Transformer hybrid model are utilized to train a lithology identification model; and inputting to-be-identified logging data into the trained lithology identification model, and outputting a lithology identification result. According to the method, high-precision lithology identification can be realized under the conditions of deep layers and complex stratums.

The invention relates to the technical field of sensor detection, in particular to a comprehensive detection system and method for a petroleum logging sensor. According to the method, the temperature and pressure data in the well logging shaft are obtained, and the temperature change rate and the pressure change rate are calculated, so that the influence of the environment on the measurement data can be accurately evaluated, the compensation of the temperature-pressure coupling influence is further realized, and the measurement precision is remarkably improved. On the basis, the amplitude change and the phase deviation of the measurement signal are further combined, the interference characteristics of the electromagnetic environment on the measurement signal are extracted, and signal optimization is carried out by calculating interference influence parameters, so that the reliability and the anti-interference capability of the measurement data are enhanced. Meanwhile, the influence of load change on the sensor in the logging process is analyzed, the deviation condition of a pressure measurement value is identified, and the measurement error is corrected by using an influence coefficient, so that the measurement data is more stable. Multi-cycle measurement data are compared and analyzed, and the long-term drifting condition of the data can be identified.

The invention belongs to the technical field of reservoir logging identification, and discloses a method and system for quantitatively extracting gravel parameters of a conglomerate reservoir, and the method comprises the steps: carrying out the image preprocessing, image enhancement and full-hole image processing of imaging logging information, and obtaining the full-hole logging image information; identifying full-borehole logging image information, and determining a conglomerate development depth section; and performing threshold segmentation on the conglomerate development depth section by using two-dimensional wavelet transform to obtain a binary image of conglomerate features. The method provided by the invention has the advantages that 1) the conglomerate development section is automatically determined through intelligent identification of an imaging logging image mode; 2) by adopting a two-dimensional wavelet transform method, imaging logging image segmentation thresholds in different windows can be determined in a self-adaptive manner, and automatic separation of gravels and a matrix background is realized; and 3) further processing the segmented gravel features through an image analysis method to realize effective separation of the bonded gravels.

The invention provides a petroleum drilling geomechanical characteristic estimation method based on deep learning, and aims to solve the problems of high measurement cost and poor real-time performance of shear wave velocity Vs in traditional geomechanical analysis by fusing logging while drilling LWD data and real-time drilling engineering parameters. According to the method, a Transformer model is adopted to carry out depth correction on logging-while-drilling data, depth offset of a drill bit and a logging sensor is eliminated, and real-time drilling parameters such as torque T, bit pressure WOB and drilling speed ROP are combined and input into a multi-layer perceptron MLP network to predict the shear wave speed Vs. And further calculating key geomechanical parameters based on the predicted shear wave velocity Vs. In practical application, the method improves the shear wave velocity prediction accuracy to 97.2%, the mean absolute error MAE of the shear modulus is reduced from 0.186 to 0.059, and the bulk modulus is reduced from 0.189 to 0.040. The method can be used for outputting stratum elastic parameters, optimizing bit pressure, pre-warning well wall instability and adjusting a well track, well drilling safety and efficiency are improved, and meanwhile dependence on an expensive well logging technology is reduced.

Systems and methods include obtaining well log data and core sample data of a subsurface formation; generating, based on the well log data and the core sample data, an unconfined compressive strength log for the subsurface formation; using an unsupervised machine learning model to form rock type clusters based on the unconfined compressive strength log and the well log data; forming a training dataset including the well log data, the training dataset labeled based on the rock type clusters; training a supervised machine learning model using the training dataset. While drilling a well in the subsurface formation, logging-while-drilling data is obtained from drilling equipment used to drill the well; and rock types in the subsurface formation are determined using the supervised machine learning model and the logging-while-drilling data.

The embodiment of the invention relates to the field of exploration or detection by applying nuclear radiation, in particular to a method for determining a uranium metallogenic area of a to-be-surveyed area, which comprises the following steps of: determining natural gamma logging data and energy spectrum logging uranium content data of the to-be-surveyed area; determining the positions of a plurality of drill holes of the to-be-surveyed area; obtaining a rock core sample of one of the determined drill holes; determining uranium contents at different depths according to the rock core sample; determining the relationship between the uranium content and the energy spectrum logging uranium content data of the drill hole and the relationship between the energy spectrum logging uranium content data and the natural gamma logging data so as to determine the relationship between the uranium content and the natural gamma logging data; and determining a natural gamma logging threshold value of the uranium metallogenic area so as to determine the uranium metallogenic area of the area to be surveyed. According to the method provided by the embodiment of the invention, the natural gamma logging threshold value of the uranium metallogenic area can be accurately determined, so that the uranium metallogenic area of the area to be surveyed can be accurately determined, and the exploration cost is greatly reduced.

The invention relates to the technical field of electrical digital data processing, in particular to a logging data intelligent analysis method and system.The method comprises the steps that porosity data in the logging process and corresponding timestamps are collected, and each data point serves as a combination of the porosity and the timestamps; calculating the abrupt change degree of each data point, and measuring the abrupt change characteristic by comparing the porosity change of the current data point and the neighborhood data point of the current data point; calculating linear strength based on the timestamps and the porosity of the data points to reflect the regularity of the data points along with time change; calculating weighted complexity by using the initial complexity, the mutation degree and the linear intensity; and finally, comparing a difference value between the weighted complexity and the initial complexity, and if the difference value exceeds a set threshold value, judging that the data point is an abnormal data point. The problem that the accuracy of a logging data analysis result is not high is solved.

An offshore oilfield segmented water injection parameter design method based on communication unit demarcation comprises the specific steps that S1, an offshore loose sandstone longitudinal multilayer oil reservoir inter-well connectivity model is constructed, an inter-well communication network is constructed, and the bottom hole pressure of each well and the water content of each sand prevention section are calculated; matching the production dynamics calculated by the model with the actual historical production dynamics by optimizing the characteristic parameters, realizing the inversion solution of the model parameters, and determining a historical fitting objective function suitable for the connected model; and S2, determining a water injection splitting coefficient, water injection efficiency and a fluid channeling index of each layer of water injection well of the oil reservoir according to the inter-well connectivity model of the offshore loose sandstone longitudinal multi-layer oil reservoir constructed in the S1. Aiming at the problem that a production well of the offshore loose sandstone reservoir is easy to generate water channeling or water logging and the like, in combination with a real-time production allocation and injection allocation production algorithm, accurate inversion of the connectivity model and real-time prediction of the production dynamics of an oil-water well are realized according to geological characteristics, development dynamics and construction of the connectivity model of the offshore loose sandstone reservoir.

The invention discloses a reservoir four-relation chart processing method, device and equipment and a medium, and relates to the field of oil well exploration, and the method comprises the steps: building a logging information parameter processing interpretation model, and determining parameters in an oil saturation model; based on the neutron-density intersection chart, calculating the distances from the logging response parameters to different lithologic curves as lithologic indication parameters; drawing a reservoir four-relation chart by taking the porosity of the stratum as an abscissa, the resistivity of the stratum as an ordinate and the lithology indication parameters as color mapping, drawing different water saturation scale marks in the reservoir four-relation chart, and in addition, taking the water saturation or the movable hydrocarbon saturation in the reservoir four-relation chart as a bubble radius; and projecting the logging response parameters of various stratum types and the explaining stratum parameter points into the reservoir four-relation chart, and establishing a logging explaining standard. According to the method, the dynamic change and internal relation among the four properties of the reservoir can be comprehensively and accurately displayed.

A method and apparatus for autonomous container management configuration changes to container clusters during runtime and autonomous configuration-based release orchestration. A release manager manages a staggered feature release that includes staggers, stagger order, and container clusters included in each stagger. A logging service manages logs generated by the container clusters and / or app containers. An update service determines container management configuration changes based on analysis of data provided by the logging service. A shared engine attempts to implement instructions provided by the release manager and the update service at different times. The release manager receives an indication of success or failure of the attempted deployment of the feature release to the current stagger. The release manager, responsive to the indication of success or failure, determines to perform one of a plurality of actions, including attempting to deploy the feature release to the next stagger, and rolling back.

The invention discloses a water surface evaporation capacity monitoring method with rainfall compensation, and the method comprises the steps: obtaining the water level balance lag time between an evaporation dish and a liquid level logging well according to a connection structure of the liquid level logging well and the evaporation dish; the water level in the liquid level logging well is collected regularly; a rain gauge is adopted to regularly monitor whether rainfall occurs or not and the corresponding rainfall capacity; correcting the current evaporation capacity calculation period by adopting the water level balance lag time to obtain a corrected evaporation capacity calculation period, and fitting to obtain a change curve of the water level of the corrected evaporation capacity calculation period along with time; the rainfall type is judged by combining a rainfall signal fed back by the rain gauge or a change curve of the water level along with time; according to different rainfall types, the rainfall capacity is estimated by combining the water level data in the liquid level logging well and the measurement data of the rain gauge, the estimated rainfall capacity is adopted to compensate the water level difference value of the evaporation capacity calculation period, and the evaporation capacity is obtained through calculation. The measuring precision of the water surface evaporation capacity can be effectively improved.

The invention discloses a thin reservoir prediction method based on pre-stack frequency extension processing, and the technical scheme is that on the basis of improving the resolution to process seismic data, reservoir rock physical analysis is carried out through actually measured transverse wave data, a rock physical model is constructed, thin reservoir identification sensitive parameters are determined, and a bridge between logging and earthquake is built; for seismic data, pre-stack seismic certainty inversion of sand group thickness resolution capability and pre-stack statistical high-resolution inversion of single sand body thickness are respectively carried out, a sensitive parameter data body is obtained through calculation, and according to a rock physical quantity plate, the spatial description precision of the thin sand body is effectively improved. The method is high in operability and easy to apply, and has good application prospects for similar ultra-deep and ultra-thin layer sand bodies such as cretaceous systems and triassic systems of tahe oil fields in the Tarim Basin; and meanwhile, the method is also suitable for sweet spot identification and reservoir space fine description of ultra-deep and thin reservoirs of basins in the middle and western parts, and is of great significance for improving the drilling success rate, productivity construction and the like.