264 results about "Seismic attribute" patented technology

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 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 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 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.

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 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.

The invention relates to a thin interbed clay mineral content prediction method based on an RBF neural network, and the method mainly improves the longitudinal resolution through the inversion of the RBF neural network on the basis of multiple attributes, and compared with the conventional research, the method directly uses the seismic attributes, such as amplitude, as the input to learn a target curve, and improves the prediction efficiency. According to the method, the seismic frequency division reconstruction data body and the inversion body are selected as input, so that the longitudinal resolution and the well coincidence rate are greatly improved; in addition, the inversion body is used as input, so that the problem that neural network inversion geological laws are poor in previous research is solved, and an important basis can be provided for reservoir assessment and development; examples prove that the method disclosed by the invention is higher in precision of inverting the clay mineral content compared with a simulation method directly using waveform indication, and is suitable for reservoir parameter prediction of a thin interbed.

This invention provides a method for characterizing sedimentary microfacies distribution based on comprehensive geological and seismic analysis. By studying the sedimentary environment and surrounding tectonic background of the entire block, this invention calculates the cumulative percentage of channel and shoal body types based on sedimentary microfacies and grain size analysis. Based on a comprehensive analysis of the lithofacies of drill cores and corresponding well logging curves and seismic response characteristics, it establishes a comprehensive response model of the main sedimentary facies-well logging facies of the target stratigraphic group in the study area. Furthermore, based on the fusion of seismic attributes, it characterizes sand bodies in seismic profiles. By integrating the sand body characterization results, the comprehensive response model of sedimentary facies-well logging facies, and the sedimentary microfacies types, a method for characterizing sedimentary microfacies distribution in tight sandstone reservoirs using comprehensive seismic and geological analysis is established.

This invention relates to the field of geophysical exploration and oil and gas geological analysis technology, and discloses a method for predicting fractured reservoirs in carbonate buried hills based on geological guidance and multi-scale fusion. The method includes: constructing a three-dimensional geological guidance constraint body using paleogeography and fault systems as geological priors; characterizing the fracture response mechanism using Gabor wavelet frequency division and structural tensor; constructing sensitive attributes and predicting the fracture body using a multi-branch convolutional neural network that fuses multi-source seismic attributes; and calculating the fracture complexity index based on the predicted fracture body to classify and zone the buried hill fractures. This invention introduces geological guidance constraints to improve the geological consistency of the prediction results; utilizes multi-scale Gabor frequency division and structural tensor analysis to achieve multi-scale fracture characterization; enhances model interpretability and prediction accuracy by fusing multi-source attributes through a multi-branch convolutional neural network; and achieves quantitative reservoir classification through the fracture complexity index, effectively guiding well location deployment and drilling decisions.

The application discloses a low-angle fault automatic identification method based on seismic data driving, which comprises the following steps: obtaining original seismic data and carrying out fracture enhancement processing; extracting fracture attributes of different fracture scales based on the fracture enhancement processed seismic data, and carrying out grid segmentation on the fracture attributes to form a fracture spatial breakpoint data body; recombining and arranging the fracture spatial breakpoint data body, and setting a fracture seed point of a low-angle fracture surface position; guiding seismic attributes based on the fracture seed point, re-searching a low-angle fracture surface, and forming new low-angle fracture seismic attributes. The low-angle fault automatic identification method based on seismic data driving can greatly improve the prediction accuracy of fractures.

The invention discloses a deep learning fracture prediction method based on interpretability, and the method comprises the following steps: S1, obtaining the seismic attribute and fracture density of a research region, carrying out the normalization processing of the seismic attribute, and carrying out the grid processing and smoothing processing of the fracture density; s2, analyzing the correlation between the seismic attribute and the fracture density, and obtaining a preferred seismic attribute; s3, performing fracture cause seismic attribute analysis on the optimal seismic attributes to obtain seismic attributes after dimension reduction; s4, establishing a deep learning neural network, and training to obtain a trained deep learning neural network; and S5, carrying out fracture prediction on the target stratum, and carrying out interpretability analysis by adopting a local interpretability analysis method to obtain reservoir fracture causes. According to the method, three-dimensional space accurate prediction of the fracture density can be realized, a master control mechanism of fracture development can be disclosed through quantitative evaluation of cause attributes, and the geological credibility of a prediction result is effectively improved.

Embodiments of the present application disclose a well-seismic joint reservoir prediction method, device and electronic equipment. The well-seismic joint reservoir prediction method comprises: selecting seismic attributes and performing standardization processing on the selected seismic attributes; obtaining a seismic attribute variogram plan based on the standardization-processed seismic attributes; establishing a relationship between the standardization-processed seismic attributes and reservoir parameters based on the seismic attribute variogram plan; and predicting the reservoir based on the relationship. The selected seismic attributes are used to obtain a seismic attribute variogram plan, thereby establishing a relationship between the standardization-processed seismic attributes and reservoir parameters, and the prediction of the reservoir is realized based on the established relationship between the standardization-processed seismic attributes and reservoir parameters. The seismic attributes are integrated into the prediction of the reservoir parameters, thereby improving the accuracy of the prediction results.

The invention discloses a paleo-gradient constraint reservoir characterization attribute for deep fan delta sedimentary facies characterization. The paleo-gradient constraint reservoir characterization attribute comprises the following steps: 1) carrying out multiple seismic attributes of a target stratum and carrying out normalization processing; 2) seismic attribute optimization is carried out by using the correlation with the sandstone thickness; 3) fitting a multiple linear regression relationship between the sandstone thickness and the optimized seismic attributes; 4) drawing a whole-area sandstone thickness prediction contour map; 5) solving an ancient slope on the ancient landform map; 6) drawing a whole-region ancient slope contour map; 7) acquiring a relation inflection point between the sandstone thickness and the paleo-slope in the research area; 8) predicting sandstone thickness and paleo-gradient by using seismic attributes to establish paleo-gradient constrained reservoir characterization attributes; and 9) calculating all-region paleo-gradient constraint reservoir representation attributes, and drawing a contour map as a basis for depicting the deep fan delta sedimentary facies. The method solves the problem that seismic attribute representation sedimentary facies lacks environmental information and the problem of multiplicity of solutions of deep fan delta seismic reflection representation sedimentary facies.

The application discloses a sparse well area hydrocarbon source rock TOC prediction method and system, an electronic device and a medium, relates to the technical field of hydrocarbon source rock carbon content prediction, and comprises the following steps: establishing a virtual well in a well-free area; determining the seismic attribute of the virtual well based on a seismic attribute extraction method; determining the lithology of the virtual well based on the seismic attribute of the virtual well and a sandstone and mudstone lithology prediction model, and extracting the mudstone section of the virtual well; determining the longitudinal continuous TOC prediction value of the hydrocarbon source rock in the mudstone section of the virtual well based on a single-well longitudinal continuous TOC prediction model; and combining the longitudinal continuous TOC prediction value and the measured TOC value of the hydrocarbon source rock in the mudstone section of the virtual well to construct a three-dimensional TOC attribute body of the target sparse well area based on a preset geological condition constraint, so as to predict the TOC of the hydrocarbon source rock in the target sparse well area. The application realizes higher-precision quantitative evaluation and prediction of the TOC of the hydrocarbon source rock in the sparse well area.

The application discloses a method and device for identifying gas-bearing reservoirs, and the method comprises the following steps: establishing a reservoir longitudinal identification criterion according to well logging data and seismic data of a study area; extracting a plurality of seismic attributes from each seismic survey line in two-dimensional seismic data of the study area; determining optimal seismic attributes from the plurality of seismic attributes through comparative analysis; and determining a favorable reservoir distribution area according to the reservoir longitudinal identification criterion and the optimal seismic attributes.

The invention discloses a fractured-vuggy reservoir comprehensive prediction method and device based on diffracted wave seismic attributes, electronic equipment and a storage medium. The method comprises the following steps: carrying out diffracted wave separation on a first full-wave-field three-dimensional seismic data volume to obtain a first diffracted wave three-dimensional seismic data volume; performing inclination angle guided filtering on the two three-dimensional seismic data volumes to obtain a second full-wave-field three-dimensional seismic data volume and a second diffracted wave three-dimensional seismic data volume, extracting full-wave-field coherent attributes, and solving a full-wave-field amplitude envelope and a diffracted wave amplitude envelope; carrying out ant tracking on the two amplitude envelopes to obtain a full-wave-field ant body and a diffracted wave ant body; and based on the full-wave-field coherent attributes, the two amplitude envelopes and the two ant bodies, fracture-cavity identification and fracture comprehensive prediction are carried out. According to the method, fracture-cavity identification and fracture crack comprehensive prediction are carried out by using the diffracted wave amplitude envelope body and the diffracted wave ant body, and the prediction precision of the carbonate rock fracture-cavity reservoir is improved.

The invention discloses a seismic data removal processing method, system and device and a medium, and the method comprises the following steps: S1, analyzing and processing seismic data, and generating a minimum offset seismic attribute; s2, determining a'full coverage 'boundary and a'primary coverage' boundary by using the minimum offset seismic attribute, giving a corresponding water bottom removal time, and calculating an unknown water bottom removal time by using an isoline interpolation method; s3, smoothing the calculated water bottom removal time; and S4, applying a removal time table: writing all the water bottom removal time into a seismic trace header in a trace-by-trace matching mode, and carrying out zero filling processing on the amplitude value of the part above the water bottom removal time. The method is high in adaptability to irregular boundary three-dimensional seismic data, and the purposes of shortening the data processing period, reducing the labor intensity of processing personnel and improving the project operation efficiency can be achieved.

The application discloses a kind of multi-region reservoir potential effective quantification evaluation methods, it includes the following steps: step 1, on the basis of interpretive achievement data, respectively using DSG Seismic Attribute Generation module and OpenGS Openinversion module of module calculation relative amplitude data body and relative wave impedance data body, and carry out construction smoothing;Step 2, in combination with well data, main fracture characteristics and reservoir attribute, in the region of reservoir target obvious and well data, reservoir and T74 layer relative amplitude and relative wave impedance inversion ratio plane calculation and point position statistics are carried out;Step 3, using EXCEL to calculate the ratio between reservoir and T74 layer, and draw the ratio trend chart between the two layers in the study area.The application can intuitively and effectively find out the distribution of the study area reservoir, which solves the problem of previous reservoirs being limited to qualitative analysis and cannot be quantified, breaks the status quo of previous qualitative analysis of reservoir evaluation parameters, and is simple to operate and has strong popularization.

Certain aspects and features relate to a system for trajectory planning and control for new wellbores. Data can be received for multiple existing wells associated with a subterranean reservoir and used to train a deep neural network model to make accurate well property projections at any other location in the reservoir. A model of features for specific well locations based on seismic attributes of the well location can be automatically generated, and the model can be used in drilling trajectory optimization. In some examples, the system builds a deep neural network (DNN) model based on the statistical features, and trains the DNN model using Bayesian optimization to produce an optimized DNN model. The optimized model can be used to provide drilling parameters to produce an optimized trajectory for a new well.

The application discloses a multi-azimuth post-stack seismic attribute interpretation method, device, medium and equipment, and the method comprises the following steps: acquiring a target layer seismic interpretation horizon and a plurality of azimuth seismic attribute data bodies; setting a window used for calculating eigenvalues; selecting an eigenvalue; for the selected eigenvalue, in the window, based on the plurality of azimuth seismic attribute data bodies, attribute optimal eigenvalues of each position point in the plurality of azimuth seismic attribute data body spaces are obtained; based on the attribute optimal eigenvalues of each position point, an eigenvalue data body is obtained; and an attribute plan view is extracted in the eigenvalue data body along the target layer seismic interpretation horizon. The application acquires the most representative attribute advantage eigenvalues in different azimuths, and constructs a new data body from the eigenvalues, so that the purpose of highlighting the geological abnormal response characteristics and reducing the dimension interpretation is achieved, and the efficiency and precision of the post-stack multi-azimuth seismic interpretation are effectively improved.

This application discloses a well-seismic analysis system, method, and equipment based on a tight sandstone geomechanical coupled lithofacies system, relating to the field of tight sandstone oil and gas extraction analysis. The system employs a four-element composite triangular diagram method in its classification model construction module to construct a geomechanical coupled lithofacies classification model for tight sandstone reservoirs based on information data. The integration module performs geomechanical coupled lithofacies classification and integrates multi-type lithofacies prediction results at the single-well level based on element difference data. The analysis and determination module, based on a well-seismic collaborative three-dimensional model of the tight sandstone reservoir's geomechanical coupled lithofacies, performs feature extraction, lithofacies classification feedback adjustment, and seismic attribute mapping of the geomechanical coupled lithofacies to determine the distribution information data of the tight sandstone geomechanical coupled lithofacies. This application can determine the heterogeneous distribution law of rock mechanics and geostress characteristics in tight sandstone reservoirs, providing a basis for improving the extraction efficiency of tight sandstone oil and gas.

The invention belongs to the technical field of oil-gas exploration and development, and provides a deep carbonate reservoir azimuth pre-stack depth domain earthquake prediction method and device, and the method comprises the steps: employing OVT domain pre-stack pre-stack depth domain trace gather data, combining with the stress distribution characteristics of a target region, generating sector azimuth pre-stack depth domain trace gather data, retaining azimuth anisotropy information, and obtaining a deep carbonate reservoir azimuth pre-stack depth domain earthquake prediction result. And optimal orientation sensitive attribute pairs and multi-transformation coefficient solving are carried out, the porosity of the deep carbonate reservoir is predicted, and deep carbonate reservoir prediction is completed. The method mainly comprises the following steps: generating a sector azimuth pre-stack depth migration gather; performing sector azimuth pre-stack depth domain gather seismic attribute processing; preferably selecting sensitive seismic attribute pairs; solving multiple equation coefficients; and calculating a reservoir porosity three-dimensional data volume, and completing earthquake prediction of a favorable reservoir. The method not only solves the problem of resolution reduction caused by high-frequency information loss of a time domain, but also facilitates characterization of anisotropy information of the reservoir, can predict the anisotropy thin reservoir, improves thin layer identification precision, and effectively guides well location deployment.

The application provides a low-frequency impedance model establishment method and device based on multi-element information control. First, local preserving projection algorithm is used for seismic attribute dimension reduction to obtain characteristic attributes, and an LSTM network is used to calculate a low-frequency impedance model M1 under a deep model framework. Then, inverse proportional weighting is used for well interpolation extrapolation to obtain a well interpolation low-frequency impedance model M2. Finally, a whale optimization algorithm is used to obtain optimal weights of the two low-frequency impedance models, and linear combination is performed to obtain a final low-frequency impedance model M_final. The establishment of the low-frequency impedance model in the application is controlled by seismic, horizon and logging multi-element information. The application has high well coincidence with the low-frequency impedance model, and has higher resolution than a conventional well interpolation low-frequency impedance model, and has high popularization and application value.

The invention provides an inter-well logging curve prediction method and system. The prediction method comprises the following steps: predicting an inter-well logging curve through a deep learning model according to seismic data and two known logging wells; wherein the seismic attributes of the seismic data are extracted; extracting seismic features according to the seismic attributes and the feature extraction backbone network; the feature extraction backbone network is constructed based on an attention mechanism; performing feature extraction on the seismic features to obtain a plurality of seismic features of different scales of the seismic section; performing longitudinal axis attention calculation and transverse axis attention calculation on the three-layer features and the four-layer features to obtain updated three-layer features and updated four-layer features; the longitudinal axis attention calculation is used for learning precision improvement and curve change from the seismic attribute to the logging longitudinal direction; the transverse axis attention calculation is used for learning the geological gradual change process of the seismic section response in the transverse direction; and predicting an inter-well logging curve according to the updated three-layer features and the updated four-layer features.

This invention discloses an attribute fusion method and system based on unsupervised hybrid multimodal and ternary adaptive transfer autoencoder, belonging to the fields of artificial intelligence and seismic exploration. The method includes: constructing an unsupervised hybrid multimodal adaptive weighted feature fusion autoencoder (UHM-AWFFA) network, where the encoder uses a multi-branch independent encoder to extract deep independent features of multiple seismic attributes and dynamically fuses them through a weighted feature fusion layer; the decoder performs hierarchical reconstruction of the fused features, simultaneously outputting the reconstruction results of each attribute and the fused attribute profile; training is performed using an interactive reconstruction loss function, and fine-tuning is performed using a ternary multimodal adaptive dynamic transfer learning (TE-MADT) strategy, including a gradient-driven adaptive thawing mechanism, a multimodal collaborative transfer strategy, and a cross-modal knowledge distillation mechanism. This invention achieves nonlinear intelligent fusion of seismic attributes, effectively uncovers deep coupling relationships between attributes, and improves the geological interpretation accuracy of the fused profile.

The application provides an inversion method for describing the connectivity of the internal reservoir of a faulted karst body. The method uses seismic attributes reflecting the outline of the faulted karst body, the holes and cracks in the faulted karst body, establishes a comprehensive low-frequency model reflecting the carbonate bedrock, the faulted karst body, and the crack and hole system in the faulted karst body, uses the model to perform post-stack constrained sparse pulse inversion, obtains a longitudinal wave impedance data body of the underground, and uses the longitudinal wave impedance data to give a longitudinal wave impedance threshold value of the reservoir, and describe the connectivity of the internal reservoir of the faulted karst body in space.

The invention discloses a low-frequency model construction method, device and equipment and a storage medium, and the method comprises the steps: determining a straight-plate low-frequency model and a seismic attribute body based on original geological data corresponding to a carbonate reservoir; performing post-stack inversion on the straight plate low-frequency model based on the original geological data, and determining a cave low-frequency model; based on the seismic attribute body, determining a fracture low-frequency model and a reservoir boundary low-frequency model; performing fusion processing on the straight plate low-frequency model, the cave low-frequency model, the crack low-frequency model and the reservoir boundary low-frequency model to obtain an original low-frequency model; and performing post-stack inversion on the original low-frequency model based on the original geological data, and determining a target low-frequency model corresponding to the carbonate reservoir. Through the technical scheme of the embodiment of the invention, automatic construction of the carbonate rock earthquake low-frequency model is realized, the carbonate rock earthquake low-frequency model can adapt to complex topographic changes of a carbonate rock reservoir, complex geologic features of the carbonate rock reservoir can be reflected, and comprehensiveness and accuracy of the low-frequency model are improved.