143 results about "Seismic facies" patented technology

The invention discloses a deep learning model for picking up a seismic phase from a seismic signal with a low signal-to-noise ratio. The deep learning model comprises a feature extraction trunk, a bidirectional time sequence-channel attention module (BTCA) and a multi-scale dilated convolutional layer module (DSCN). According to the feature extraction trunk, a cascaded LiteMobileBlock module is used, shallow high-resolution features are extracted from an original waveform step by step, and an initial feature map is generated; the bidirectional time sequence-channel attention module integrates the time sequence modeling capability of the bidirectional LSTM and the spectrum sensing capability (emphasizing importance of different sensors / directions) of a channel attention mechanism, and outputs fusion features; and the multi-scale cavity convolution layer module utilizes parallel cavity convolution modules with different expansion coefficients to synchronously capture a local mutation and global oscillation mode of a waveform to generate multi-scale enhancement features, further enhance the capture capability of the model on long-range time dependence in seismic signals through LSTM, and output time sequence enhancement features. According to the model, a multi-scale cavity convolution module, a bidirectional time-frequency attention mechanism module and an LSTM enhanced sequence modeling module are fused, so that the feature extraction and time sequence modeling capability of a seismic signal with a low signal-to-noise ratio is improved, and robust pickup of a seismic phase is realized.

Systems and methods are provided for subsurface characterization from seismic data. The system can receive a plurality of candidate velocity and anisotropic parameter models and seismic gather data. A subset of the plurality of candidate velocity and anisotropic parameter models can be selected to form a training data set. The system can generate a joint probability functions of depth differences and seismic semblances based on the training data set and generate a likelihood function based on the joint probability function. A Bayesian model can be defined using the likelihood function and the prior probability functions. The system can draw a plurality of samples from a posterior distribution of the Bayesian model using Markov Chain Monte Carlo sampling methods and calculate depth uncertainty values using the plurality of samples.

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 invention relates to a road cavity detection and identification method and system based on multichannel optical fiber sensing. The method comprises the following steps: acquiring and storing Rayleigh scattering light time domain signals which are acquired by a DAS underground sensing system at different positions and different time periods of a detection road section and contain active knocking signals; determining a frequency range of the active knocking signal according to a frequency spectrum obtained after the Rayleigh scattering light time domain signal is subjected to fast Fourier transform processing; carrying out filtering processing on the Rayleigh scattering light time domain signal by adopting a finite impulse response band-pass filter to obtain a filtered signal; the filtered signal is input into a PhaseNetDAS model for seismic phase pickup, and an active source seismic phase is obtained; associating the active source seismic facies with the active source event to obtain a seismic facies report including event latitude and longitude, time and seismic facies arrival time; and inverting the velocity structure of the underground medium by using the seismic phase report by using a tomography method, and determining a road cavity detection and recognition result, thereby improving the accuracy of road cavity detection.

The present application relates to the technical field of oil reservoir exploration, and particularly relates to a fracture-cave seismic facies identification method and device, which comprises the following steps: obtaining each reservoir section range of a fracture-cave reservoir; obtaining the seismic reflection energy intensity of each reservoir section of the fracture-cave reservoir; obtaining the seismic waveform characteristics of each reservoir section, wherein the seismic waveform characteristics are any one of the following: a first type of seismic waveform with both wave peaks and wave troughs, and a second type of seismic waveform with only wave peaks or only wave troughs; determining the seismic facies category of each reservoir section of the fracture-cave reservoir based on the seismic waveform characteristics and the seismic reflection energy intensity, thereby providing an important basis for the exploration and development of the carbonate rock fracture-cave oil reservoir through effective identification of the seismic facies.

The invention relates to the technical field of seismic facies intelligent identification models, in particular to a seismic facies intelligent identification model construction method and system and a storage medium. The method comprises the following steps: acquiring three-dimensional seismic data and seismic facies label data corresponding to the three-dimensional seismic data, and performing data preprocessing to generate a standard training data set; determining network structure parameters and generating a network model initial architecture based on the structural features of the standard training data set; according to the initial architecture of the network model and the standard training data set, carrying out network parameter training and generating multiple groups of candidate model parameters; through a preset model verification evaluation process, screening out an optimal model parameter from the multiple groups of candidate model parameters, and constructing a seismic facies intelligent identification model; according to the method, the seismic facies intelligent identification model is constructed, so that standardization and automation of the whole process from data preparation to model optimization are realized, and the accuracy and reliability of seismic facies identification are remarkably improved.

Seismic facies modeling of an area of study at an oil and gas exploration site includes obtaining a seismic dataset. A set of unsupervised machine learning (USML) models processes a test dataset of the seismic dataset. Respective USML models of the set are configured with different cluster numbers. A USML model and corresponding elbow point cluster number is selected from the set of USML models. The selected USML model, configured with the elbow point cluster number, processes the seismic dataset to obtain clusters of the data points. Cluster profiles based on seismic cell attributes of the data points of each cluster are generated. Seismic facies labels are assigned to the clusters based on corresponding cluster profiles. The clusters are sampled to a three-dimensional (3D) grid representation of the area of study to obtain a seismic facies model displayed in a visualization tool of a seismic modeling platform.

The invention provides a fault control fracture-cavity modeling method utilizing geological trend factor constraints, and relates to the technical field of oil-gas exploration, and the method comprises the steps: calculating seismic coherent body attributes based on post-stack seismic data, obtaining the overall space distribution characteristics of geological structure anomalies and the like, and depicting a strike-slip fracture zone boundary model U1 (x, y, z); calculating the spatial development intensity I1 (x, y, z) of the crack and the hole body based on the distance influence factors from the fracture zone; the longitudinal development strength I2 (x, y, z) of the crack and the hole body is restrained by using the buried depth trend of the stratum, and the comprehensive spatial distribution trend strength Itotal of the crack and the hole body is constructed. The space trend constraint and the stochastic simulation technology are combined, and a space distribution model of the cracks and the hole bodies is established. The model established by the method is more in line with actual space distribution characteristics of cracks and holes in a work area. The method is applied to carrying out numerical simulation research, and is helpful for finding more effective means for processing the fault control fracture-cavity body.

The invention relates to the technical field of geological exploration, in particular to an on-site exploration stratum logging method and system. The method comprises the following steps: identifying a layered boundary in a rock core image by using a deep learning image segmentation model; extracting a stratum interface in the logging curve through wavelet transform and multi-scale analysis; based on a multi-source feature fusion strategy of an attention mechanism and an auto-encoder, the stratigraphic boundary, the stratigraphic interface and the seismic facies feature vectors are combined to analyze comprehensive features of the stratigraphic boundary; according to a stratum attribute graph construction strategy of the graph neural network, associating a stratum reflection layer and the lithology classification feature vector in the radar image to obtain stratum attribute graph features; and constructing a comprehensive decision-making model of a random forest classifier and a rule engine, and generating a stratum catalog report by combining the landform boundary, the stratum boundary comprehensive features and the stratum attribute graph features. According to the method, the accuracy, objectivity and efficiency of the catalog result are improved by cooperatively processing the multi-source data.

The application discloses an unsupervised seismic facies analysis method based on a lognormal mixture variational autoencoder, and comprises the following steps: obtaining prestack seismic data, performing data preprocessing, making a data set, constructing a deep clustering model of the LMVAE, iteratively training the data set by using the deep clustering model of the LMVAE, completing unsupervised prestack seismic data reflection mode analysis, predicting a seismic facies category, and generating a prestack seismic facies map. The method models the lognormal mixture probability of the seismic data in the feature, solves the limitation of the asymmetric data in the deep feature space distribution in the seismic reflection mode analysis, and simultaneously, for the purpose of simplifying the model solution, an inference model using a reparameterization skill for direct optimization is constructed, the inference difficulty problem of the deep generation model under a complex latent structure is overcome, the accuracy of the seismic facies map is improved, and thus strong technical support is provided for the prestack seismic data reflection mode analysis.

The invention discloses a reservoir parameter prediction method and device, electronic equipment and a medium. The method comprises the following steps: determining seismic attribute parameters capable of reflecting reservoir parameter essential characteristics, and predicting a seismic facies through a self-organizing competitive neural network algorithm; adding the seismic facies as transverse constraints into the low-frequency model, and constructing a phase-controlled low-frequency model; and carrying out reservoir parameter inversion through a self-adaptive pre-stack mixed domain inversion method. According to the method, low-frequency information containing seismic facies constraints is added in low-frequency model construction, in addition, in the inversion process, through self-adaption of prior information of different distributions, and meanwhile, mixed domain constraints are added in a likelihood function, the inversion result resolution and transverse continuity are improved, the reservoir description precision is greatly improved, and the method is suitable for large-scale popularization and application. And good support is provided for high-quality reservoir prediction.

The application discloses a kind of seismic facies drive deep carbonate high-precision sedimentary microfacies recovery methods, comprising: basic data collection;Well-seismic calibration;Establish regional isochronous sequence stratigraphic framework;Logging interpretation;Sequence framework within sedimentary facies division: seismic facies division;Establish seismic facies-sedimentary facies conversion relationship;Isochronous sequence stratigraphic framework within division strata slice;Optimization seismic attribute;Establish single factor sedimentary index-seismic attribute relationship;Attribute volume isochronous strata slice;Seismic facies-sedimentary facies under the constraint of high-precision isochronous sedimentary microfacies drawing.The application can accurately predict sequence within sedimentary facies belt boundary by establishing sedimentation-seismic correlation under the condition of less drilling, outcrop;Further optimization can reflect the seismic attribute of sedimentary characteristics, calculate isochronous strata slice;Isochronous sedimentary facies map is obtained by attribute-sedimentary correlation conversion.In practical application, good effect is obtained, and the coincidence rate with actual drilling is high.

The invention discloses a shale-oil-rock facies combined seismic facies inversion method based on deep learning, and the method comprises the steps: carrying out the preprocessing of a pre-stack angle gather and logging data, and carrying out the seismic facies division of seismic data through employing a K-Means clustering algorithm; establishing a three-dimensional convolution deep learning model based on a space-time attention mechanism, introducing a position coding theory in natural language processing to convert a seismic facies classification result into a time sequence code, and introducing the time sequence code into the three-dimensional convolution deep learning model to form a seismic facies control deep learning model; and training the seismic phased deep learning model by adopting a semi-supervised learning method to obtain inversion longitudinal and transverse wave velocity and density result transverse resolution. Transverse resolution of longitudinal and transverse wave velocity and density results of phase-controlled deep learning inversion is superior to that of a non-phase-controlled inversion result, and the thickness of an inverted thin layer is far smaller than one fourth of the wavelength of seismic waves.

The invention provides a supervised seismic facies division method and device based on a support vector machine algorithm. The method comprises the steps of forming a basic sample according to historical seismic data and seismic facies data corresponding to the historical seismic data; determining the correlation degree between each attribute and the seismic facies by combining an attribute analysis result obtained by analyzing the historical seismic data with a preset seismic grid and the seismic facies data, and selecting a training sample from the basic samples based on the correlation degree; inputting the to-be-measured data into a seismic facies prediction model obtained by training the three-dimensional seismic recognition model by using the training sample; and the seismic facies prediction model carries out constraint and classification processing on seismic attribute features of the to-be-measured seismic facies data to obtain a classification prediction result of the seismic facies. According to the method, the training sample is obtained through supervised learning analysis of the historical seismic data, so that the training sample is better matched with the known target single well data, then the seismic attribute features of the seismic facies data to be detected are constrained and classified by using the seismic facies prediction model, and the precision of seismic facies identification and prediction is improved.

The invention provides a seismic coherent body calculation method and device, electronic equipment and a computer readable storage medium. The method comprises the following steps: sequentially extracting three-dimensional sampling data volumes from three-dimensional post-stack seismic data volumes of a to-be-processed region by taking a single data sampling point as a center according to a preset calculation window; determining a local event slope at the central position of each three-dimensional sampling data volume by using a direct search method; and according to the local event slope at the central position of each three-dimensional sampling data volume and a coherence calculation formula based on a variational principle, determining a coherence value at the central position of each three-dimensional sampling data volume, and obtaining a seismic coherence body result of the to-be-processed region. Based on the variational principle, the adaptive standard trace is introduced to calculate the seismic coherent body, so that the influence of the transverse change of the amplitude of seismic data on the calculation of the coherent body can be eliminated, and the resolution capability of discontinuous bodies such as faults and unconformity surfaces can be remarkably improved.

The invention belongs to the technical field of oil-gas exploration and development, and discloses a method for predicting the thickness of dark mudstone in a thin-well low-exploration basin, which comprises the following steps of: 1, acquiring a seismic horizon velocity of a hydrocarbon source rock stratum section and a thickness model of the hydrocarbon source rock stratum section; secondly, the pure mudstone speed and the pure sandstone speed of the hydrocarbon source rock stratum section are obtained; and 3, substituting the seismic horizon velocity of the hydrocarbon source rock stratum section, the pure mudstone velocity of the hydrocarbon source rock stratum section and the pure sandstone velocity into a calculation formula to calculate the percentage content of the mudstone. And 4, acquiring a seismic facies type plane distribution diagram of the hydrocarbon source rock stratum section, calibrating dark mudstone, and determining a dark mudstone distribution range. And a fifth step of constructing a dark mudstone thickness model within the dark mudstone distribution range. According to the method, the interference of the non-hydrocarbon source rock on the thickness of the dark mudstone is eliminated, and accurate thickness data is provided for calculation of the resource quantity. And the thickness data is calculated in a unit division mode, so that the calculation efficiency and accuracy are improved.

The present application provides a kind of carbonate rock thin reservoir interwell connectivity evaluation method, comprising the following steps: (1) collecting the regional geological background of the purpose layer of the study area in multiple wells, drilling and logging data, seismic data, production dynamic data, and establishing database;(2) determine the sedimentary facies type, determine the reservoir characteristics;(3) the logging data of different wells is normalized, and the logging sequence is combined to form the logging facies, and the logging facies characteristics of the corresponding reservoir section between adjacent wells are compared;(4) obtain the pressure coefficient of adjacent wells at the same altitude depth, and compare the pressure coefficient of the corresponding reservoir section between adjacent wells;(5) according to the seismic phase profile and seismic phase plane of the well, the phase change of the corresponding reservoir section between adjacent wells is compared;(6) according to the comparison result, judge whether the thin reservoir is connected or not.The beneficial effects of the present application: can carry out static productivity prediction in newly drilled carbonate reservoir, and then provide reference for well completion test.

The invention discloses a thin layer prediction method based on quantitative phase belt constraint. The method specifically comprises the following steps of S1, collecting seismic data and performing preprocessing; s2, seismic facies analysis attributes are extracted and selected from the seismic data; s3, performing phase belt division on the seismic facies; s4, performing qualitative prediction on the reservoir thickness, and counting the prediction coincidence rate of each phase belt; s5, in the same seismic facies belt, under the well drilling horizon constraint, performing horizon fine interpretation by combining well logging, well drilling and seismic data; and S6, in the same seismic facies belt, modeling is carried out under interpretation horizon constraint, inversion is carried out under model constraint, and the facies belt seismic qualitative prediction reservoir coincidence rate is used as the seismic weight to predict the thin layer. According to the thin layer prediction method based on the quantitative phase belt constraint, the seismic facies reservoir prediction coincidence rate is used as the seismic weight, meanwhile, fine inversion is carried out by phase belts, and accurate seismic prediction of the 3-5m thin reservoir is achieved.

The present application relates to a kind of low-order fault seismic interpretation method.Seismic interpretation method includes: obtaining the time-depth conversion relationship of target area;According to the time-depth conversion relationship, the seismic profile of target area is converted;According to more than three small breakpoint information or two more fault trace lines on the seismic profile not on a straight line, low-order fault is constrained seismic interpretation;The mode of small breakpoint information or fault trace line of target area is B mode or A+B mode, A mode is: if there is well drilling small breakpoint in target area, well drilling small breakpoint is corresponded with seismic phase one by one;B mode is: in seismic interpretation software, change the polarity of sandstone to corresponding negative polarity, and reduce the intensity of seismic polarity, obtain small breakpoint information or fault trace line.The present application only needs to find out small breakpoint or fault trace line by changing software setting in interpretation process, and low-order fault can be accurately interpreted, which is simple and reliable.

The present disclosure belongs to the field of geomagnetic station earthquake anomaly extraction, and is an earthquake magnetic field anomaly extraction method, system and electronic equipment, comprising: constructing a three-dimensional non-negative tensor data body; adopting a spatial weighted non-negative tensor decomposition method to decompose the three-dimensional non-negative tensor data body, extracting R characteristic components, each characteristic component containing a frequency factor matrix, a time factor matrix and a station contribution factor matrix; calculating the proportion of the total energy of a target frequency band in the frequency factor matrix of each characteristic component in the total energy in the entire frequency range, and selecting the characteristic component with the largest proportion as the earthquake-related characteristic component; and based on the time factor matrix of the earthquake-related characteristic component, adopting an over-limit threshold method to extract an earthquake anomaly point. The present disclosure can retain and utilize all measured data to study earthquakes, and effectively detect earthquake anomalies by obtaining more relevant components of earthquake activity.

The invention relates to the technical field of oil-gas exploration, in particular to a lithologic trap evaluation method, device and equipment, a medium and a computer program.The method comprises the steps that under the condition that a sedimentary facies type is a single-well sedimentary facies, a seismic facies plane distribution diagram corresponding to a target area is obtained; based on the first corresponding relation and the seismic facies plane distribution diagram, obtaining a reservoir porosity plane distribution diagram; based on the second corresponding relation and the seismic facies plane distribution diagram, a packing layer porosity plane distribution diagram is obtained; and determining an evaluation result of the lithologic trap based on the seismic facies plane distribution, the reservoir porosity plane distribution diagram and the packing layer porosity plane distribution diagram. According to the method, when the distribution of the reservoir and the packing layer is predicted, the wave impedance corresponding to the porosity is considered, quantitative prediction is achieved, the evaluation result of the lithologic trap is obtained based on the distribution conditions of the seismic facies, the reservoir and the packing layer, the accuracy of the evaluation result of the lithologic trap is improved, and then the exploration success rate of the lithologic trap is improved.

The invention provides a seismic phase estimation method based on wavelet time-frequency analysis, and the method comprises the following steps: calibrating a well-to-seismic synthesis record, and determining a marker bed of a well point and a wavelet initial phase; adopting wavelet transform and Hilbert transform to calculate a frequency spectrum data volume when passing through a well point; obtaining a horizon corresponding to a marker layer of the time-frequency spectrum data body by utilizing an automatic horizon interpretation technology, and estimating a well point wavelet phase; calculating an amplitude slice corresponding to the position of the marker bed, and then estimating a well point wavelet phase according to the initial phase and the amplitude change of the slice; comprehensively determining a wavelet phase corresponding to a typical well, and then selecting a well-point-free area according to a line number interval to estimate a marker bed wavelet phase. The seismic phase estimation method based on wavelet time-frequency analysis has the advantages of being high in precision, weak in multiplicity of solutions, not limited by well pattern density, still high in reliability even under the condition that well data is rare, and the like.

This application discloses a seismic time series signal detection method and related apparatus based on an attention mechanism. The method includes monitoring a seismic time series signal with three components; inputting the seismic time series signal into a pre-trained detection network model; and determining the seismic phase, P-wave first arrival time, and S-wave first arrival time of the seismic time series signal through the detection network model. The downsampling module in the detection network model of this application includes a sequence attention subunit for extracting sequence features. The sequence attention subunit can efficiently extract sequence signal features, reduce the computational complexity of the detection model, and improve the detection speed of seismic signals.

The invention relates to the field of prediction of lacustrine facies high-quality hydrocarbon source rocks in an offshore less-well / no-well area, and discloses a lacustrine facies high-quality hydrocarbon source rock small sample machine learning quantitative prediction method and system based on geological condition constraints. The fault activity rate of the depression in the hydrocarbon source rock development period is larger than an activity rate threshold value, and the stratum thickness is larger than a thickness threshold value; for depression of the selected hydrocarbon source rock, determining a high-quality hydrocarbon source rock development layer section according to geological conditions of high-quality hydrocarbon source rock development through paleotemperature, nutrition degree and water reducibility analysis; determining seismic facies characteristics of the high-quality hydrocarbon source rocks according to well seismic analysis, and depicting a seismic facies distribution range of the high-quality hydrocarbon source rocks on the basis of seismic sequence analysis; and based on a small sample machine learning method, establishing a quantitative relationship between the seismic facies of the lacustrine facies high-quality hydrocarbon source rocks and the TOC of the lacustrine facies high-quality hydrocarbon source rocks, and carrying out high-quality hydrocarbon source rock distribution prediction on a target region so as to improve the quantitative prediction precision of the lacustrine facies high-quality hydrocarbon source rocks in the less-well / no-well region on the sea.

The invention provides a recognition method and device for a hillock beach zone in a paleocarbonate rock platform, and the method comprises the steps: building a thickness-trough number-cloud-ash ratio relation model according to the stratum thickness, trough number and cloud-ash ratio determined by first logging data in a target work area; based on a thickness-trough number-cloud-to-ash ratio relation model, determining a cloud-to-ash ratio corresponding to the seismic data according to the stratum thickness and the trough number determined by the seismic data in the target work area, and further generating a cloud-to-ash ratio plane distribution diagram of the target work area; determining a cloud-to-ash ratio change interface according to the cloud-to-ash ratio plane distribution diagram; according to the seismic data, determining seismic facies characteristics corresponding to the cloud-to-ash ratio change interface; and according to the seismic facies characteristics, identifying the paleocarbonate rock abutment inner dune beach zone. The method uses the seismic data to quickly identify the mound beach zone, improves the identification accuracy and efficiency, and is significantly faster than artificial seismic facies interpretation. The method has a remarkable effect on researching the distribution rule of the hillocks and beaches in the platform and searching large-scale reservoirs.

The invention relates to a two-dimensional seismic attribute interpolation method based on multi-point geostatistics, and the method comprises the steps: taking a three-dimensional work area seismic facies as a multi-point geostatistics training image, taking a seismic facies on a two-dimensional survey line of a two-dimensional work area as basic data, and employing a multi-point geostatistics algorithm to establish a seismic facies covering a two-dimensional work area range; according to a multipoint geostatistics principle, the two-dimensional work area seismic facies is fused with three-dimensional work area seismic facies information; and finally obtaining a phased two-dimensional seismic attribute distribution diagram by using a seismic facies constraint interpolation algorithm in a two-dimensional work area range. The two-dimensional seismic attribute distribution map effectively fuses three-dimensional work area seismic facies information, the purpose of effective control over three-dimensional work area seismic facies relative to two-dimensional work area attribute interpolation is achieved, and analysis and prediction of reservoirs in areas without survey lines in the two-dimensional work area are achieved.

The application provides a wave equation full-wave Q tomography method, which comprises the following steps: performing FFT transformation on existing seismic records to determine the frequency of a Ricker wavelet; performing forward simulation using the frequency of the Ricker wavelet determined in step 1 to obtain seismic correlation data; converting the seismic correlation data to a local domain and performing FFT on the data in the local domain to obtain the frequency of a single seismic event; obtaining the peak frequency shift of each seismic event in the local domain according to the frequency of the single seismic event; obtaining a companion source according to the peak frequency shift; performing wave field continuation calculation according to the companion source to obtain a back-propagating wave field; obtaining a gradient using the back-propagating wave field; and performing iterative updating using a conjugate gradient method according to the gradient to finally obtain an updated Q model. The method provided by the application can be simulated to obtain a relatively accurate Q model.

The present application belongs to the field of geophysical exploration technology combined with artificial intelligence algorithm, and relates to a seismic facies identification method and system based on cross-correlation convolution network, comprising the following steps: obtaining well logging data and seismic data; inputting the well logging data into a committee mechanism model to identify well logging lithofacies of the well logging data; performing morphological processing on the seismic data; inputting the well logging lithofacies identification result and the morphologically processed seismic data into a generative adversarial network to expand a sample data set through the generative adversarial network; and inputting the original seismic data and the expanded sample data set into a PSP-Unet neural network model to generate a seismic facies identification result. The present application increases the expansion of the data set and the accuracy and convergence speed of each model involved.

The application provides a seismic facies classification method and device for seismic data, and the method comprises the following steps: determining the similarity between each data and other data in a seismic data set in a target work area according to the kernel function of each data, so as to determine the similarity between all data in the seismic data set; dividing the seismic data set in the target work area into K cluster data according to the similarity between all data, so as to generate a division result; wherein K is equal to the number of seismic facies types in the target work area; and calibrating the division result according to the petrophysical parameters and / or sedimentary facies of the target work area, so as to determine the seismic facies classification result of the seismic data set. According to the transverse variability of the seismic signal in a certain target layer, the mean shift algorithm is used to classify the seismic trace shape, the classification result forms discrete'seismic facies', and the petrophysical parameters or the sedimentary facies plane distribution rule is described by using the seismic data.

The present invention relates to a method for enhancing seismic coherence attributes, which can obtain two-dimensional scalar seismic coherence attributes; extract fracture seed points based on the spatial similarity of the two-dimensional scalar seismic coherence attributes, and construct a fracture skeleton based on the fracture seed points; calculate the inclination of the fault where each fracture seed point is located, and pick a local optimal fracture path with maximum similarity based on the inclination of the fracture seed point and the fault; extract the fault attribute value corresponding to each local optimal fracture path, and obtain a local optimal fracture path score based on the fault attribute value; integrate the local optimal fracture path score into a global optimal fracture score result, and normalize the global optimal fracture score result to obtain enhanced seismic coherence attributes. The technical solution shown in the present invention solves the problem of poor spatial continuity of some fractures in seismic coherence attributes, thereby obtaining stable enhanced seismic coherence attributes, meeting the needs of modern oil and gas exploration for fracture prediction.