389 results about "Seismic attribute" patented technology

The invention discloses an offshore oil and gas resource efficient evaluation method based on a well-free / few-well condition, and the method comprises the steps: S10, carrying out the collection of seabed multi-field coupling data, and obtaining an original data flow; s20, performing multi-source data intelligent fusion processing: performing data space-time alignment, performing electromagnetic seismic joint inversion by using a joint objective function, performing leakage path analysis, and extracting reservoir physical property parameters and a leakage network; s30, dynamic geological knowledge graph construction: constructing a graph neural network and establishing a cross-regional reservoir parameter prediction model through transfer learning to obtain a dynamic geological model and transfer learning parameters; s40, virtual well intelligent generation: guiding virtual well generation to obtain a virtual well data set; and S50, reservoir modeling evaluation: training reservoir modeling together with the virtual well data and the real seismic attributes to obtain resource probability distribution and sweet spot division. According to the method, the dependence on dense drilling is reduced, the defect of multiplicity of solutions of geophysical data is overcome, and the technical bottleneck of modeling under the condition of less wells / no wells is broken through.

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 discloses a geological modeling method for fusing remote sensing and seismic data based on a multi-mode auto-encoder, and the method comprises the steps: collecting a remote sensing image covering a target area and seismic exploration data, and converting the attribute of the seismic exploration data into a resolution consistent with the remote sensing image; respectively extracting remote sensing image features and seismic attribute features by using a parallel double-branch coding structure of a multi-mode encoder; a fusion module is arranged in the middle layer of the remote sensing image encoder and the seismic attribute encoder, fusion is carried out in the fusion module, remote sensing images and seismic data are reconstructed by using a decoder based on fusion features, and difference data between reconstructed data and the remote sensing images and the seismic data are calculated based on a combined loss function introducing regular terms. Training a multi-modal encoder by using the difference data to obtain an encoding feature and a difference heat map; and on the basis of the coding features and the difference heat map, key geological structure region identification and visual display are carried out, and key geological structures comprise faults and lithology.

The invention discloses a weak signal compensation method based on seismic attribute analysis, which belongs to the field of seismic data processing, and comprises the following steps: processing post-stack data through a radial prediction filtering technology to obtain data with a high signal-to-noise ratio, gridding two-dimensional seismic data, and interpolating to form a three-dimensional data volume; then carrying out attribute analysis according to the signal-to-noise ratio and the continuity factor of the seismic data, scanning grid data of an inclination angle in a given range on the basis of the three-dimensional grid data, solving the signal-to-noise ratio and the continuity factor of the data, and selecting and extracting required superposition data according to attributes; and finally, correlation analysis is carried out through the CMP gather and extracted target superposition data, and weighted compensation of signals is carried out, so that effective seismic weak reflection signals are compensated, deep imaging quality is improved, and effective compensation of deep weak signals of seismic data is realized.

The invention discloses a method for predicting a favorable area of a thin-layer stacked sand body, and relates to the technical field of exploration and development of oil and gas reservoirs, and the method comprises the steps: generating a synthetic seismic record through well-seismic combination by using interval transit time and a density curve, aligning the synthetic seismic record with an actual seismic record, and building a three-dimensional frame model of a target layer of a research area; performing abnormal value elimination and standardization processing on the logging curve, making a statistical histogram, comparing peak separation degrees of sandstone and mudstone, and selecting the most sensitive curve to distinguish the sandstone and mudstone; and realizing well-side seismic trace waveform dynamic clustering analysis through singular value decomposition, and determining the number of effective samples. Based on the result of waveform indication simulation, the method is combined with the optimized seismic attribute to predict the thin-layer stacked sand body together, a systematic method is provided, the multiplicity of solutions of predicting the thin-layer stacked favorable sand body through the seismic attribute and the uncertainty of depicting the sand body by the sedimentary facies in the area with few wells can be reduced, and the prediction accuracy of the thin-layer stacked favorable sand body is improved. Therefore, the precision and reliability of thin-layer stacked sand body prediction are improved.

The invention relates to the technical field of geophysical exploration data processing, and discloses a multi-element prospecting data cleaning and feature extraction system based on machine learning, and the system comprises a structure field generation unit which is used for constructing a multi-scale structure tensor field according to three-dimensional seismic data and extracting a structure vector representing a geological structure; the self-adaptive cleaning unit is used for carrying out abnormal value judgment and interpolation on non-seismic data by using the structural vector as prior guide information; the coupling feature generation unit is used for generating single-scale and cross-scale coupling features capable of deeply quantizing the relationship between the attribute change and the structure by calculating the projection of the non-seismic data gradient on different-scale structure vectors; and the feature output unit is used for combining the cleaned data, coupling the features and the original seismic attributes and constructing a high-dimensional final feature vector. According to the method, input with more geological significance is provided for machine learning, so that the precision and reliability of prospecting prediction are remarkably improved.

The invention discloses a remaining oil distribution prediction method and system, a control device and a storage medium, and the method comprises the steps: obtaining a multi-scale fusion feature vector according to wide-area apparent resistivity data, seismic attribute data, optical fiber time sequence signal data, nano tracer tracking data and pore network point cloud data, inputting a physical constraint neural network to obtain a residual oil saturation initial value and a pressure gradient initial value of each region of the target layer, wherein a loss function comprises data fitting item loss, Darcy law physical constraint loss and mass conservation constraint loss; the real-time water content and the bottomhole flowing pressure of the production well serve as observation data, and a static parameter correction value, a remaining oil saturation correction value and a pressure gradient correction value are obtained through correction based on an ensemble Kalman filtering algorithm; the static parameter correction value and the pressure gradient correction value are fed back to a physical constraint neural network for loss function updating; and determining a remaining oil distribution result of the target layer. According to the invention, the accuracy and timeliness of residual oil distribution prediction can be improved.

The invention provides a fracture-vug reservoir inter-well three-dimensional communication path multi-target search method and system, and the method comprises the steps: building a three-dimensional space model of a fracture-vug reservoir based on seismic multi-attribute data, mapping a carbonate reservoir to a three-dimensional space, and carrying out the grid division; according to the coherent amplitude data and the well distribution condition of each well pair in the search space, adaptively optimizing a coherent amplitude threshold and a seismic attribute fusion weight by adopting a multi-objective optimization algorithm by taking the communication length and the volume as optimization objectives, and searching to obtain grouped inter-well communication paths meeting set requirements corresponding to the current well pair; according to the method, an evaluation function improved heuristic search algorithm is designed to search a single path in a search space, and a self-adaptive crossover and mutation probability improved multi-objective algorithm is proposed to optimize threshold parameters to obtain a plurality of inter-well communication paths conforming to a real seismic structure, so that the problems of limited path analysis and poor reliability in the prior art can be solved; and efficient and comprehensive search of inter-well communication paths is realized.

The invention discloses an underground crack prediction method and device based on multi-scale information fusion. The method comprises the following steps: acquiring a construction pattern of a research area; obtaining a fracture analysis result of the research area, and obtaining fracture development characteristics and a fracture density curve of each single well; performing model forward modeling based on the seismic data of the research area to determine a fracture grading standard; based on the post-stack seismic data of the research area, obtaining large-scale and medium-scale fractures according to seismic attributes; according to the post-stack seismic data, obtaining a small-scale crack based on a preset crack prediction model; based on the pre-stack seismic data of the research area and the fracture density curve of each single well, obtaining a micro-scale fracture according to a pre-stack azimuth anisotropic fracture prediction technology; and taking the large and medium-scale fractures, the small-scale fractures and the micro-scale fractures as input variables, and fusing the input variables by adopting a fuzzy neural network to obtain a fracture prediction data volume. A complex network and a multi-information fusion method are combined with a big data technology to carry out high-precision crack identification so as to realize multi-scale characterization of shale cracks.

The proposed technique introduces embodiments of a computer-implemented method for interpreting image delineations as vector objects and topological extraction from segmentation by visual computational methods applied to sections (slices) of seismic volumes, in order to aid geological interpretation and sampling of parameters originating from the fracture network and its topology. Embodiments of a developed method integrates a software / application that allows the loading and generation of statistical data related to the fracture network while maintaining georeferencing and scale of the two-dimensional input data. In addition, a fracture segmentation method is shown that uses pyramid image smoothing (decomposition into hierarchical levels of resolution) in order to reduce the amount of details and aid the identification of main faults or fractures. The segmentation after this smoothing is based on adaptive thresholding segmentation.

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 belongs to the field of petroleum and natural gas exploration and development, and particularly discloses a multi-attribute fusion thin reservoir prediction method which comprises the following steps: S1, calibrating a well-to-seismic combined synthetic record, performing seismic structure interpretation on a near-well structure in combination with a through-well seismic section, and comparing the actual drilling depth of a seismic marker bed with the seismic structure interpretation horizon depth to obtain a well-to-seismic combined synthetic record; determining a well trajectory position; s2, seismic attributes reflecting reservoir features are extracted through seismic data; s3, compressing the seismic attribute space extracted in the step S2 by using a principal component analysis technology, and preferably selecting effective seismic attributes; s4, performing further optimization on the effective seismic attributes, and performing reservoir distribution prediction according to the optimized effective seismic attributes; and S5, analyzing whether a prediction result accords with geological expectation or not. The drilling efficiency, the well bore quality and the exploration benefits are improved, and the oil-gas exploration process is accelerated. The method is suitable for reservoir distribution prediction in seismic exploration.

The present invention provides a geophysical identification method for mid-deep undercompacted low-velocity mudstone. By statistically analyzing data, representative P-wave velocity, S-wave velocity, and density of low-velocity mudstone and gas-bearing sandstone in well logging data are obtained. Combining geological information, a theoretical model of the superimposed relationship between low-velocity mudstone and gas-bearing sandstone is designed, and convolution forward modeling and prestack forward modeling are carried out on it. The post-stack and prestack seismic attributes of the model are calculated respectively. Then, sensitive attributes are optimized from these attributes, and the obtained sensitive attributes are used as learning samples, and the superimposed relationship is identified by probability self-organizing neural network clustering. The present invention adds prestack attributes to the optimization of sensitive attributes, making the fuzzy self-organizing neural network more accurate in identifying the superimposed relationship between gas-bearing sandstone and low-velocity mudstone, effectively distinguishing low-velocity mudstone from gas-bearing sandstone, and providing support for subsequent effective reservoir prediction.

The embodiment of the invention provides a reservoir heterogeneous region identification method and device, a storage medium and a processor, and belongs to the field of data processing. The method comprises the following steps: calculating a statistical metric of each seismic attribute pre-selected in a reservoir seismic horizon; and drawing the reservoir according to the statistical metric of each seismic attribute to identify the heterogeneous region of the reservoir. According to the method, the limitation of the prior art is solved, and meanwhile, the innovation capability is provided for reservoir characterization.

The invention relates to the technical field of energy storage, and provides a compressed air energy storage salt cavern volume estimation method and system based on multi-seismic attribute fusion. The method comprises the following steps: S1, data acquisition: acquiring three-dimensional seismic data, logging data and actually measured salt well volume data; s2, extracting data characteristic parameters; s3, constructing and training a salt cavern volume estimation model: establishing the salt cavern volume estimation model by adopting a Gaussian process regression algorithm, wherein an algorithm kernel function is a linear combination of a plurality of seismic attribute kernel functions; performing data training on the estimation model to obtain optimization parameters; and S4, estimating the volume of the to-be-measured salt cavern: inputting the processed seismic attribute features into the trained salt cavern volume estimation model to obtain an estimated value of the volume of the to-be-measured salt cavern. By establishing the mapping relation between various seismic attributes and the salt cavern volume, a quick estimation method is provided, the calculation speed is high, and the calculation precision is high; not only is a salt cavern volume prediction value given, but also probability distribution can be given, and a decision maker is helped to better consider risk factors.

The invention relates to the technical field of oil and gas geophysical exploration, in particular to a dolomite reservoir fracture-cavity system prediction method and device, equipment and a medium. The method comprises the steps that large-scale fractures of a reservoir in a research area are described by extracting seismic attributes, small-scale fractures of the reservoir in the research area are described by solving attenuation gradient attributes of different azimuth angles, and hole bodies of the reservoir in the research area are described by conducting pre-stack total porosity inversion. Therefore, large-scale and small-scale crack and hole body depicting results are obtained; and the dolomite reservoir fracture-cavity system prediction result in the research area is obtained through fusion. The problems that when a dolomite fracture-cavity system is depicted through logging data or seismic data in the prior art, fracture-cavity development characteristics cannot be described and predicted on the whole, fine depiction of fracture-cavity body seismic response outer contours cannot be achieved, and the accuracy of hole recognition and fracture depiction is relatively low are solved.

The present invention discloses a method and system for identifying low-velocity mudstone based on attribute clustering. It includes: by analyzing the logging data and seismic data of exploration wells in the work area, dividing the low-velocity mudstone into bright spot type and non-bright spot type, and analyzing their characteristics. Establishing a three-layer medium model and conducting seismic forward modeling to obtain the seismic response characteristics of different models. Then, extracting and optimizing seismic attributes from the forward modeling results. Using the CLARA clustering algorithm and the PAM method to conduct clustering analysis on the attributes to identify the types of low-velocity mudstone and gas-bearing sandstone. The present invention improves the identification accuracy of low-velocity mudstone and gas-bearing sandstone, and provides technical support for exploration and well location deployment.

The invention discloses a physical property parameter inversion method combining domain knowledge and a deep network, and relates to the technical field of reservoir prediction. The method comprises the following steps: performing fine well-seismic calibration by using logging, earthquake, geology and other information in combination with seismic geological conditions of a target area; on the basis of model seismic response and actual reservoir earthquake and well data analysis, multiple seismic attributes are extracted, and attribute combinations which are sensitive to lithological characters and physical properties and are not highly correlated with each other are screened out; fusing multiple networks and a progressive hierarchical extraction strategy to construct a multi-task deep network inversion model, and performing model training by taking a rock physical parameter statistical relationship and low-frequency information as constraints; and performing reservoir physical property inversion by using the trained inversion model, and performing constraint improvement on an inversion result in combination with actual exploration data and prior information. Compared with the prior art, the method can solve the problems of lack of data and poor robustness and interpretability to a certain extent, and improves the physical property inversion precision and reliability.

A computer implemented method provides for detecting a geological object in a geological formation by processing a seismic 3D image, said seismic 3D image comprising a plurality of pixels representing seismic measurements performed on the geological formation. The method includes computing, based on the seismic 3D image, a geological-time (GT) isochronous surface of the geological formation, wherein the GT isochronous surface corresponds to the coordinates of pixels of the seismic 3D image which represent portions of the geological formation considered to have a same geological age, computing a seismic attribute 2D image representing at least one seismic attribute of the geological formation on the GT isochronous surface, and detecting, by image segmentation, pixels of the seismic attribute 2D image which represent the geological object.

A quantitative coal-seam coal-quality prediction method and system based on a seismic AVO attribute. A physical model for self-consistent rock in coal rock is established, which model is based on the coal quality characteristics of a coal seam, and on the basis of the model, an elasticity parameter and seismic AVO attribute of the coal seam are calculated; mathematical relational expressions between the seismic AVO attribute and coal rock components, and rationality evaluation indicators of the mathematical relational expressions are constructed; and a method for using the seismic AVO attribute to perform quantitative prediction on coal rock components of an underground in-situ coal seam is further established. In the method, a physical model for rock is used to perform simulation analysis, such that a certain level of universality is achieved, and the method is easily popularized, and fills in the blanks in the prediction of the coal quality occurrence of the underground coal seam.

The invention discloses a geophysical method for predicting coal rock thickness, particularly relates to the technical field of coalbed methane exploration, and has the core innovation that a wedge-shaped geologic model containing nine kinds of roof and floor lithology combinations is constructed, and seismic attributes sensitive to a thin coal seam are optimized through principal component analysis; converting the complex relationship between the seismic attributes and the coal thickness into a linear separable problem by using the specific high-dimensional nonlinear mapping capability of the radial basis function neural network; a three-level closed-loop mechanism of attribute optimization-network training-dynamic verification is established, and model self-optimization is realized through spider diagram analysis, over-fitting monitoring and new well triggering iteration. According to the method, the tuning distortion problem of a traditional linear model in thin coal seam prediction is solved, the reliability of well-free area prediction is remarkably improved, and a key technical support is provided for coal seam gas dessert identification and development decision making.

Examples of methods and systems are disclosed. The methods may include obtaining, by a geological modeling system, geological data and seismic data regarding a subsurface region of interest, generating a paleogeographic surface using the geological data and the seismic data, and selecting an adjustable geological attribute from the paleogeographic surface. The methods may also include generating, using a seismic interpretation system coupled to the geological modeling system, a surface of a seismic attribute using the seismic data, where the seismic attribute is related to the adjustable geological attribute. The methods may further include generating, by the geological modeling system, an updated paleogeographic surface, iteratively or recursively, until a stopping condition is reached, based on a difference between the paleogeographic surface and the surface of the seismic attribute, and determining a validated geological model of the subsurface region of interest based, at least in part, on the updated paleogeographic surface.

The invention relates to a method for generating a karst cave 3D printing digital model, a storage medium and equipment, and the generation method comprises the following steps: analyzing a seismic data file in an SEGY format; arranging and storing X and Y coordinates, trace gathers (CDP) and seismic attribute data of a specified main measuring line seismic interpretation profile (Inline) and a tie line seismic interpretation profile (Xline) corresponding to each seismic data point in an index mode; generating a corresponding orthogonal grid model on the basis of the seismic data points; searching and extracting a karst cave in the orthogonal grid model according to the seismic data information of the orthogonal grid model and each grid unit, then identifying all boundary surfaces participating in forming a karst cave boundary, and processing the boundary surfaces to obtain a karst cave contour represented by a triangular grid; and outputting and storing the three-dimensional contour of the karst cave oil reservoir model obtained in the step 3.4 according to an STL file format. The STL model is constructed by identifying and analyzing the seismic data in the SEGY format, and a new tool is provided for smooth development of a 3D printing physical model.

The invention belongs to the technical field of exploration and development of fault block oil and gas reservoirs, and particularly relates to a seismic prediction method and system for igneous rock intrusive fault zone sealing performance, and the method comprises the steps: determining the seismic attribute and seismic frequency attribute of a highlight body of a target interval according to petroleum seismic exploration data; determining magma invasion characteristic parameters according to the seismic attribute and the seismic frequency attribute of the highlight body, and determining fracture characteristic parameters of the fracture zone according to an eigenvalue characteristic algorithm operation result in the third-generation coherent attribute of the seismic data; determining an internal structure index of the intrusive fault zone according to the magma invasion characteristic parameters and the fracture characteristic parameters of the fault zone; and comparing the internal structure index of the intrusive fault zone with a closed lower limit threshold of the internal structure index of the known oil reservoir fault zone, and predicting the fault zone closure and the closure capability thereof according to a comparison result, thereby realizing quantitative determination of the oil and gas migration and dredging capability of the fault with magma invasion in the clastic rock stratum.

The application discloses a coalbed methane enrichment area prediction method based on multiple geological parameter constraints, determines a research area, analyzes geological features of the research area to determine lithology, physical property features and elastic parameters of rock strata, predicts geological elements of a coalbed methane enrichment area of the research area, and establishes a nonlinear mapping relationship between main geological parameters of the coalbed methane and enrichment characteristics of the coalbed methane, namely, a geological attribute discrimination mode of the coalbed methane enrichment area, by means of artificial neural network method and constraint of the geological parameters. The method for predicting the coalbed methane enrichment area under the constraint of multiple geological parameters simultaneously controls the convergence direction of the solution of the enrichment area by multiple mutually irrelevant geological parameters, reduces the prediction scale of the enrichment unit to obtain an optimal solution, avoids the uncertainty of single parameter prediction, improves the accuracy of the prediction result, and solves the technical problem that the method for predicting the coalbed gas content by single seismic attribute or parameter in the prior art has strong multiple solutions, thereby leading to inaccurate prediction results.

The invention provides an oil-gas exploration data rapid integration method and system based on bidirectional cascading, and the method comprises the steps: S1, obtaining an original exploration data flow, and carrying out the normalization processing, and forming a standardized initial data body; s2, transversely distributed seismic reflection interface feature points and longitudinally distributed logging lithology turning points are extracted, and a bidirectional cascade topology framework is constructed; s3, waveform similarity propagation of seismic attributes is executed in the transverse direction, rock physical parameter recursion of logging attributes is executed in the longitudinal direction, and an intermediate integrated data set is generated; s4, acquiring gravity and magnetic exploration data, generating a geologic structure background field, performing space registration and weight superposition, and correcting stratum boundary drift; and S5, reversely backtracking and updating the point-to-point constraint relationship in the bidirectional cascade topology skeleton, and generating high-precision oil-gas exploration integrated data after closed-loop verification. The method has the advantages that the precision of integrated data can be effectively improved, and therefore the actual requirement of high-precision oil-gas exploration is met.

The invention provides a carbonate cave type reservoir prediction method and system based on data mining, and relates to the technical field of data mining, and the method comprises the steps: carrying out the feature extraction of historical logging data, and screening out key logging parameters and a seismic attribute data set; processing the logging data based on a data mining method to obtain a characteristic mode of reservoir development; in combination with the seismic attributes and the real-time data of the current well drilling, optimizing a reservoir mode by adopting a self-organizing mapping neural network and a Bayesian reasoning method, and constructing an optimized reservoir distribution prediction model; historical oil and gas yield data is utilized, a reservoir prediction rule is constructed through a grey correlation analysis and decision tree method, a high-quality reservoir area is identified, and probability prediction is carried out; and performing spatial interpolation and visualization processing by adopting a Kriging interpolation and connectivity analysis method to generate a connectivity distribution map of the reservoir and a probability density map of the oil and gas enrichment area, and effectively improving the prediction precision of the carbonate cave type reservoir.

The present invention discloses a method and apparatus for establishing an earthquake phase prediction model. The method comprises obtaining a plurality of first-category plane earthquake attribute data and a plurality of second-category plane earthquake attribute data that have been screened and gridded according to the same rule; screening unlabeled grids from each plane earthquake attribute data according to a set rule, extracting each first-category earthquake attribute value and each second-category earthquake attribute value of the labeled grids and the screened unlabeled grids; obtaining a first labeled sample set, a first unlabeled sample set, a second labeled sample set, and a second unlabeled sample set; and using each obtained sample set to train a selected classification model through a collaborative training method to obtain a classification model for earthquake phase prediction. The method can improve the prediction accuracy of earthquake phases and reduce the dependence on labeled samples.

The embodiment of the invention provides a single-well water channeling shielding type remaining oil quantitative characterization method and system, and belongs to the technical field of oil reservoir simulation. The method comprises the steps that seismic attribute information of a target oil reservoir area is collected, oil and gas filling path analysis is executed based on the seismic attribute information, and whether an oil reservoir is located on an oil and gas filling path or not is judged and researched based on an analysis result; in response to a trigger signal that an oil reservoir is located on an oil and gas filling path, respectively executing karst cave three-dimensional depiction and fracture three-dimensional depiction based on the seismic attribute information to obtain fracture-cave well space structure characteristics; based on the single well exploitation data in the target oil reservoir area, oil and gas exploitation degree analysis is executed, and oil and gas exploitation degree information is obtained; and performing residual oil quantitative characterization based on the space structure characteristics of the fracture-cave well and the oil and gas exploitation degree information, and outputting the space distribution and scale of the residual oil. According to the scheme, the residual oil quantitative characterization is executed based on the fracture-cave well space structure characteristics and the oil and gas exploitation degree information, the space distribution and scale of the residual oil can be output, and important reference is provided for follow-up oil and gas resource management and development.

The present invention belongs to the technical field of petroleum and natural gas exploration. Provided are an intelligent seismic high-frequency sequence interpretation method and apparatus using logging information. The method comprises: inputting, into a sequence stratigraphy prediction model in a current state, seismic attribute data of determined point locations, which are determined on the basis of a sequential path, along a well-tie seismic trace of a known conditioning well in a target block, so as to obtain high-frequency sequence stratigraphy data of the determined point locations; and determining whether any high-frequency sequence stratigraphy data of a point location in the target block has not been obtained, and if any high-frequency sequence stratigraphy data of a point location in the target block has not been obtained, adding the high-frequency sequence stratigraphy data of the determined point locations to sequential simulation conditioning data, using updated sequential simulation conditioning data to construct a learning sample to train the sequence stratigraphy prediction model in the current state, and then jumping to the previous step to perform high-frequency sequence stratigraphy prediction on the next determined point location determined on the basis of the sequential path. The method can be applied to onshore early-exploration blocks and offshore sparse-well blocks, etc., so as to realize high-frequency sequence interpretation of these blocks with high efficiency, low costs and high accuracy.