1221 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 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 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 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 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 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.

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.

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.

A system and process for subsea fiberoptic logging of a well has a subsea structure having a central bore, an upper valve positioned in the central bore, a lower valve positioned on the central bore below the upper valve, and a subsea probe releasably connected to an upper connector of the subsea structure. The subsea structure has at least one channel adapted to connect to a downline. The upper valve is movable between an open position and a closed position. The lower valve is movable between an open position at a lower position. The channel opens to the central bore in an area between the upper valve and a lower valve. The subsea probe has a fiberoptic line connected thereto. The fiberoptic line is adapted to extend to a portion of the central bore of the subsea structure and into the well so as to sense parameters within the well.

A method for evaluating shale compressibility logging based on game theory, comprising: step 1 of preparing rock core data, conducting experiments, and obtaining rock mechanical parameters and experimental rock physical parameters; step 2 of establishing a relationship between the rock mechanical parameters and the experimental rock physical parameters; step 3 of establishing a relationship between the rock mechanical parameters and the experimental rock physical parameters to predict the formation pore pressure based on the well logging rock physical parameters; step 4 of establishing a relationship between the laboratory and field logging rock physical parameters; step 5 of constructing a geostress calculation model for the research block formation and further obtaining related parameters; step 6 of obtaining the main controlling factors of shale compressibility geomechanics by normalizing the oil recovery intensity; step 7 of obtaining the main expression parameters and ranking of the relatively independent shale compressibility; and step 8 of determining the shale compressibility index. [Effects] A method for evaluating shale compressibility has been established with high accuracy.

The invention discloses a method and system for evaluating rock drillability in real time, and the method comprises the steps: determining a well section with logging data and element logging data at the same time in a target area, and obtaining a rock drillability grade value through the logging data; selecting element types conforming to geological characteristics of a current target area from the element logging data to form optimal element combination data of a corresponding stratum matched with the optimal element types, and based on the optimal element combination data, performing data fitting on a preset multiple regression model in combination with rock drillability levels to obtain a drillability level calculation model; the current drillability grade value calculation model is corrected by utilizing a rock core taken from a target stratum to obtain an experimental rock drillability grade value; and optimal element combination data of a target stratum is obtained in real time in the drilling process, and a dynamic rock drillability grade value is obtained by using the corrected drillability grade value calculation model, so that a rock drillability real-time evaluation result is obtained. According to the invention, real-time evaluation of the drillability of the rock is realized.

The invention discloses a while-drilling barrel rock debris and gas logging sample collecting device. The while-drilling barrel rock debris and gas logging sample collecting device comprises a liquid-sand separation system, a rock debris collecting system and a gas logging sample collecting system. The liquid-sand separation system is used for separating while-drilling well drilling fluid into drilling fluid liquid and rock debris and respectively conveying the drilling fluid liquid and the rock debris to the gas logging sample collection system and the rock debris collection system, and rock debris samples are collected through the rock debris collection system; the gas logging sample collection system comprises a quantitative metering mechanism and a degassing mechanism, and the quantitative metering mechanism quantitatively conveys the drilling fluid to the degassing mechanism; the degassing mechanism comprises a heating mechanism and a vacuum stirring degassing mechanism, and the drilling fluid is degassed through the heating mechanism and the vacuum stirring degassing mechanism to obtain the gas logging sample; the problems that an electric degasser cannot conduct quantitative degassing, oil gas in the C6-C8 range in mud cannot be removed, and the timeliness of manual rock debris collection is poor are effectively solved.

The invention relates to a buried hill reservoir permeability evaluation method and system based on Stoneley wave time difference intelligent prediction, processing equipment and a storage medium. The method comprises the following steps: preprocessing core physical property, conventional logging and electric imaging logging data in a target area; array acoustic logging data are processed, and longitudinal wave, transverse wave and stoneley wave time difference of each well in the target area is extracted; establishing an intelligent stoneley wave time difference prediction model, and predicting the stoneley wave time difference of the interval with poor stoneley wave data quality of each well; determining the effective fracture development position and the fracture width of each well; carrying out a crack plate model experiment, and calculating the total permeability of the stratum at the effective crack development position according to the effective crack development position and the crack width of each well; inverting the total permeability of the buried hill reservoir; on the basis of the plunger sample core permeability and the total stratum permeability calculated at the effective fracture development position, permeability inversion results of the effective fracture non-development position and the effective fracture development position of each well are calibrated, and the method can be widely applied to the field of oil and gas exploration and development.

The invention discloses a drilling-measurement constrained three-dimensional geothermal geologic attribute modeling and quantification method, and relates to the technical field of data analysis and processing, and the method comprises the following steps: 1, constructing a drilling-measurement constrained three-dimensional geothermal geologic voxel basic model, and forming a three-dimensional geothermal geologic voxel basic model covering a modeling range; 2, constructing an implicit signed distance field on the three-dimensional geothermal geologic voxel basic model to express fracture geometry, generating a unified implicit geothermal geologic voxel field, and redividing voxel categories according to the unified implicit geothermal geologic voxel field; and step 3, executing moving cube surface reconstruction and prior sensitivity analysis on the unified implicit geothermal geologic voxel field to give a space confidence body and a volume interval, and dividing the space confidence body and calculating the volume interval according to the voxel occurrence frequency. Evaluation of the geothermal body is changed from traditional single volume estimation to interval and confidence expression, and a reliable basis is provided for well position deployment, risk management and development planning.

The invention discloses a saturation multi-probe instrument nuclear calculation and processing automation method, a saturation multi-probe instrument nuclear calculation and processing automation system, a saturation multi-probe instrument nuclear calculation and processing automation terminal and a saturation multi-probe instrument nuclear calculation and processing automation medium, and belongs to the technical field of nuclear logging in oil-gas exploration. Data generated by the model is extracted, converted and integrated into a simulation spectrum database, and saturation parameters of the simulation data are automatically generated into the simulation spectrum database; meanwhile, saturation actual measurement logging data are preprocessed, saturation parameter results are automatically calculated, the water-containing saturation, the gas-containing saturation and the oil-containing saturation under each depth are solved, and the whole system basically achieves the automatic processing flow; according to the method, the dependence on manual processing is reduced, the consumption of human resources is reduced, the influence of human subjective judgment on results is reduced, the consistency of the results in the oil-gas exploration field is improved, important saturation parameters for oil-gas exploration are generated in batches, and the intelligent level in the oil-gas field is improved.

The invention belongs to the field of petroleum and natural gas engineering, particularly relates to a method and a system for converting XRF logging elements and mineral content, and aims to solve the problems of long time for judging the mineral content by XRD and low cost performance. The method comprises the steps that XRF logging element and XRD mineral content detection is conducted on a coring sample according to the well depth, and XRF logging element detection data and XRD mineral content detection data are obtained; classifying minerals corresponding to the coring samples to obtain mineral types, extracting elements of the mineral types, and screening the elements to obtain characteristic elements; performing linear regression analysis on the XRF logging element detection data corresponding to the characteristic elements and the XRD mineral content detection data corresponding to the characteristic elements to obtain mineral content models of different mineral types; and substituting XRF logging element data of any well depth into the model to obtain the mineral type content of the well depth. According to the method, the mineral type content can be judged according to the XRF logging elements.

The invention provides an electric logging data normalization processing method and equipment based on X-ray diffraction (XRD) mineral constraint, and belongs to the field of petroleum logging engineering.The method comprises the steps that test data of mineral composition parameters and cable logging data of rock physical property parameters are obtained, and the mineral composition parameters at least comprise rock mineral composition obtained through X-ray diffraction testing; based on the test data of the mineral composition parameters, determining calculated values of the rock physical property parameters by utilizing a volume summation method; judging whether the calculated value of the rock physical property parameter is completely matched with the cable logging data of the rock physical property parameter or not; if the judgment result is incomplete matching, performing normalization processing on the cable logging data of the rock physical property parameters based on the calculated values of the rock physical property parameters; otherwise, the normalization processing is not carried out. Through the method provided by the invention, all system errors of the cable test data can be reduced, so that the cable logging information can more accurately reflect the physical properties of underground rocks.

The invention discloses an intelligent natural gas productivity prediction method and device for a logging small layer joint test, and the method comprises the steps: dividing a plurality of target small layers of a target gas well reservoir according to the obtained various logging curve data of a target gas well, constructing corresponding productivity prediction machine learning models, and carrying out the prediction of the productivity of the target gas well. Predicting single-layer daily gas yield and single-layer daily water yield; for the plurality of target small layers, determining an interference coefficient representing interlayer interference of the plurality of target small layers; according to the interference coefficient and the single-layer daily gas production rate and the single-layer daily water production rate of each target small layer, obtaining a combined test productivity prediction result of the logging small layer after the interference loss is deducted; and / or, a deep learning algorithm is adopted to establish a well logging small layer combined test productivity prediction model, and a combined test productivity prediction result corresponding to the target small layer combined test is output. The method is used for improving the productivity prediction efficiency and accuracy of the natural gas combined test well.

The invention discloses a multi-component digital rock core reconstruction method, device, equipment and medium, and relates to the technical field of reservoir logging identification, the Wasserstein distance is introduced, the Wasserstein distance between a generated sample and real sample distribution is calculated, and the Wasserstein distance is minimized layer by layer in training, so that the problem of non-convergence in the training process is solved, and the accuracy of the multi-component digital rock core reconstruction is improved. The training stability and the quality of generated samples are improved; a priori knowledge decision is embedded into a discriminator to perform regularization constraint on mineral distribution, porosity and crack characteristics, so that the judgment capability of the discriminator on the authenticity of a generated sample and coincidence domain characteristics is improved, and the reconstruction accuracy of a multi-component structure is improved; meanwhile, a gradient penalty mechanism is introduced, the stability of model training and the distribution consistency of generated samples are improved, finally, the trained network model is used for reconstructing a multi-component digital core, and the complex pore structure and the multi-component structure of the reservoir are clearly displayed.

The invention provides a method and system for identifying an omissive hydrocarbon reservoir of an old oil field based on rock debris and optical fiber monitoring, and the method comprises the steps: carrying out the multi-mode digital analysis of the stock rock debris of a target well of the old oil field, and obtaining the rock physical characteristic parameters of the rock debris; the method comprises the following steps: obtaining stratum dynamic response parameters through a distributed optical fiber sensing system arranged in a sleeve of a target well of an old oil field in advance; historical logging data of the target well of the old oil field are called, and standardization processing is conducted on the historical logging data to obtain a historical logging parameter set; static errors of a historical logging parameter set are corrected through rock physical characteristic parameters, and real-time dynamic constraints are provided through stratum dynamic response parameters; according to the type characteristics of the reservoir where the target well of the old oil field is located, the corresponding multi-parameter fusion identification method is adopted to obtain the position of the missing oil and gas reservoir, and the accuracy and reliability of missing oil and gas reservoir identification are improved.

The present description relates to the field of oil and gas field exploration and development, and in particular, to a shale oil sweet spot logging evaluation method and apparatus based on source-reservoir coupling characteristics. The method comprises: on the basis of the effective porosity and effective thickness of a shale oil reservoir in a research area, determining reservoir quality, wherein the effective porosity is greater than a preset porosity lower threshold; on the basis of effective points of kerogen total organic carbon content in the research area, the effective thickness of an interval corresponding to each effective point, and the kerogen total organic carbon content of each effective point, evaluating the quality of source rock intervals; on the basis of an estimated longitudinal distance range of the positions where the effective points of kerogen total organic carbon content are located, determining a coupling relationship between the reservoir quality and the quality of the source rock intervals; and on the basis of the coupling relationship, evaluating a shale oil sweet spot within the estimated longitudinal distance range.

The invention discloses a deep niobium phosphate ore positioning physical and chemical exploration method based on multi-method cooperation, and belongs to the technical field of geological mineral exploration. According to the method, a three-stage exploration system of region optimization, target region delineation and fine verification is constructed, multi-parameter cooperation of a magnetic method, an electromagnetic method, an induced polarization method, a three-component magnetic logging method and a geochemical method is combined, and each step is constrained by a basic rock-niobium phosphorite metallogenic model. An ore prospecting mark is determined through regional geological background analysis and metallogenic model construction, a metallogenic prospective region is delineated through regional optimization, a specific target region is delineated and locked, engineering verification is carried out through fine verification, and finally, a result is integrated to estimate the resource quantity. According to the method, the multiplicity of solutions of a single method is effectively reduced, the positioning precision of the deep niobium phosphorite is improved, the exploration efficiency and the target area ore finding rate are improved, and the method is suitable for accurate exploration of the deep niobium phosphorite.

The invention provides a method for identifying shale cracks based on conventional logging information. The method comprises the steps that 1, an interval transit time logging curve and a natural gamma logging curve of a shale section are obtained; 2, discrete wavelet transform and box dimension calculation are carried out on the interval transit time curve and the natural gamma curve, and a wavelet coefficient and a box dimension are obtained; 3, solving a difference value between the wavelet coefficient of the interval transit time curve and the wavelet coefficient of the natural gamma curve to obtain a wavelet coefficient difference value, and solving a difference value between the box dimension of the interval transit time curve and the box dimension of the natural gamma curve to obtain a box dimension difference value; 4, averaging the wavelet coefficient difference value and the fractal dimension difference value, and constructing a fracture development response index; and step 5, according to the relatively high value of the fracture development response index, performing fracture identification in the shale section. According to the method for identifying the shale crack based on the conventional logging information, the shale crack development section can be accurately and reliably identified.

The application provides a method for identifying mud shale fractures based on conventional logging data, comprising the following steps: step 1, obtaining acoustic travel time logging curves and natural gamma logging curves of a mud shale section; step 2, performing discrete wavelet transform and box dimension calculation on the acoustic travel time curves and the natural gamma curves to obtain wavelet coefficients and box dimensions; step 3, calculating the difference between the wavelet coefficients of the acoustic travel time curves and the wavelet coefficients of the natural gamma curves to obtain wavelet coefficient differences, and calculating the difference between the box dimensions of the acoustic travel time curves and the box dimensions of the natural gamma curves to obtain box dimension differences; step 4, averaging the wavelet coefficient differences and the fractal dimension differences to construct a fracture development response index; and step 5, identifying fractures in the mud shale section according to the relatively high value of the fracture development response index. The method for identifying mud shale fractures based on conventional logging data can accurately and reliably identify mud shale fracture development sections.

The invention discloses a coal body structure identification method based on an improved convolutional neural network, and belongs to the technical field of geological exploration. The method comprises the following steps of: (1) screening out a logging curve sensitive to coal body structure characteristic response; (2) carrying out wavelet decomposition and noise reduction processing on the logging curve; (3) carrying out normalization processing on the logging curve; (4) reading feature vectors of samples formed by logging data according to a window, and forming training sample data by the feature vectors and coal body structure type labels obtained by coal core logging; (5) constructing a deep learning coal body structure recognition model based on the improved convolutional neural network; and (6) training the convolutional neural network model by using the training sample data, obtaining optimal network parameters through multiple times of network parameter iteration updating, returning the optimal network parameters to the prediction model, and obtaining an optimal model by properly adjusting the structure and hyper-parameters of the model. And inputting the new logging data into the prediction model to obtain a coal body structure identification result. By adopting the method, the coal body structure identification precision can be remarkably improved.

The invention relates to the field of oil and gas field exploration and development, in particular to a fracture-cavity type reservoir mechanical parameter seismic inversion method based on logging ground stress constraint. On the basis of array acoustic logging, the current crustal stress is calculated; inverting different types of seismic attributes based on the pre-stack seismic data; by optimizing the seismic attribute and the threshold value, the geometric shape carving of the fracture-cavity body is realized; based on the fracture-cavity body seismic attribute sensitivity degree, different types of fracture-cavity body seismic recognition standards are established; establishing rock mechanical parameter assignment models of different types of fractured-vuggy bodies through rock mechanical parameter inversion cycle verification; and considering stress constraint to optimize different types of fracture-vug body rock mechanical parameter assignment models, and finally realizing fracture-vug type reservoir three-dimensional rock mechanical parameter inversion. The invention provides a fracture-vug reservoir mechanical parameter seismic inversion method based on logging ground stress constraint. The method has reference value in multiple aspects of fracture-vug reservoir geomechanical modeling, ground stress field numerical simulation, engineering dessert evaluation and the like.

The invention discloses a well logging prediction method and device applied to source rock geochemical parameters. The well logging prediction method applied to the source rock geochemical parameters comprises the steps that rock debris data and well logging data are collected and obtained, the amplitude limit of corresponding lithologic well logging response is divided, and a lithologic recognition standard is established; performing single well depth homing on the organic carbon data, and adjusting the depth position of an organic carbon sampling point; selecting a logging curve sensitive to the content of the organic carbon according to the measured data of the content of the organic carbon, and establishing a calculation model of the corresponding lithology and the content of the organic carbon; establishing a pyrolysis hydrocarbon logging calculation model; and selecting a new drilling well for formula verification. According to the method, the logging curve response characteristics are utilized, corresponding standards are divided for corresponding lithology, and meanwhile, based on the established calculation model of the corresponding lithology and the organic carbon content and the pyrolytic hydrocarbon logging calculation model, the method can be simply and reliably used for geochemical parameter logging prediction of a production well which is not subjected to coring.