90 results about "Seismic facies" patented technology

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 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.

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 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 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.

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.

This invention belongs to the field of reservoir prediction technology, specifically seismic inversion, and particularly relates to a method and apparatus for establishing an initial facies-controlled model in facies-controlled inversion. Based on sedimentary facies, this invention establishes a forward model and performs forward analysis according to the lithological combination characteristics and variation patterns of different facies zones. It then selects seismic attributes or attribute combinations that can distinguish different facies zones to establish seismic identification models for different sedimentary facies. These identification models are used to classify seismic facies, and the seismic facies control the establishment of the subsequent initial model, thereby making the initial model more reasonable and more sedimentary indicative.

The application provides a detection signal high-frequency continuation method and related equipment, constructs a local dip angle estimation network to process target deep seismic data to obtain local dip angle field data, captures signal edge time delay characteristics through a dynamic spatial attention network, constructs a seismic facies hidden space representation model to extract geological ages corresponding to seismic facies hidden space representation results; the geological ages and the seismic profile data are input into an inversion physical model for processing to obtain elastic parameter data, and the elastic parameter data, well logging data and well side seismic data are input into a forward physical model for reconstruction to obtain time-varying wavelet spectrum characteristics to construct a high-frequency continuation inversion target function, complete inversion solving, obtain a high-resolution reflection coefficient sequence, and after convolution of the high-resolution reflection coefficient sequence and the time-varying wavelet spectrum characteristics, obtain deep seismic data after high-frequency continuation; and the technical difficulties of high-fidelity, high-robustness band widening and resolution improvement of deep seismic data are solved.

The application provides a deep-sea T-phase calculation method and system based on a seafloor seismic source mechanism, and the method comprises the following steps: determining calculation physical quantities in an elastic multilayer ocean crust medium and a fluid deep-sea sound channel for a deep-sea seafloor seismic T-phase propagation process, and establishing an acoustic wave-seismic wave combined elliptical differential equation in a two-dimensional cylindrical coordinate system; the elliptical differential equation is converted into an acoustic wave-seismic wave combined parabolic differential equation, a horizontal recursive equation in a root form of the acoustic wave-seismic wave combined parabolic differential equation is given, an initial field horizontal recursive equation in the root form suitable for a seafloor seismic event is established, the horizontal recursive equation in the root form is converted into an N p step loop recursive equation, numerical discrete processing is performed on operator action in the loop recursive equation, a representation form of calculation quantities of virtual points in a discrete process is given, and a seismic wave field in an ocean crust medium and an acoustic wave field in a deep-sea sound channel are obtained according to the loop recursive equation.

The present disclosure provides a multi-wave and multi-domain adaptive seismic horizon auto-tracking method and apparatus. The multi-wave and multi-domain adaptive seismic horizon auto-tracking method comprises: importing seismic data, and setting a seed point on the seismic data; determining a seismic relative resolution according to a seismic waveform of a seismic trace where the seed point is located; determining horizon tracking parameters according to the seismic relative resolution; determining a horizon point according to the horizon tracking parameters and the seismic trace where the seed point is located; replacing the seed point with the horizon point, and performing corresponding iterative calculation to obtain a plurality of horizon points in one-to-one correspondence with the seismic traces in the seismic data; and combining horizon values corresponding to the plurality of horizon points into seismic horizon data and exporting the seismic horizon data.

The invention discloses a natural gas hydrate identification method and device based on multi-attribute discrimination, electronic equipment and a storage medium. The method comprises the following steps: setting a natural gas hydrate discrimination area based on seabed depth; calculating a negative amplitude envelope of the pre-stack depth migration data volume in the natural gas hydrate discrimination region, and further calculating an amplitude envelope median, so as to determine a natural gas hydrate amplitude anomaly discrimination operator by using a natural gas hydrate BSR discrimination threshold value; natural gas hydrate seismic facies are extracted based on the operator, and then a prior velocity model is obtained, so that a full-waveform inversion target functional is constructed; and iteratively solving the target functional through an adjoint state method to obtain a full-waveform inversion velocity modeling result so as to discriminate the natural gas hydrate. According to the invention, natural gas hydrate identification is carried out through combination of the seismic data amplitude difference attribute and the high-precision full-waveform inversion speed attribute, the natural gas hydrate identification precision is effectively improved, and the hydrate and underlying free gas are accurately identified.

The application provides a seismic facies analysis method and system based on an improved self-organizing neural network, which comprises the following steps: S1, initializing the self-organizing neural network, wherein the self-organizing neural network is a two-layer self-organizing neural network with K neurons, and K is the number of seismic facies types; S2, calculating the similarity between input data and each neuron one by one and determining the winning neuron according to the similarity; S3, updating the related parameters of the neuron; and S4, every M time steps, adding or inserting a new neuron according to a first preset rule, and deleting or discarding a neuron according to a second preset rule. According to the lateral variability of the seismic signal in a certain target layer, the improved self-organizing neural network is used to classify the seismic trace shape, and the classification result forms discrete'seismic facies'. Further, the rock physical parameters or the plane distribution rule of the sedimentary facies are described by using the seismic data.

The application provides a relative velocity analysis well-free constraint relative wave impedance inversion method, which comprises the following steps: step 1, velocity analysis is carried out by using seismic data to establish the time-depth relationship of a study area; step 2, seismic facies analysis is carried out on the seismic data to determine the geological model of the study area; step 3, velocity correlation analysis is carried out to determine the velocity migration amount; step 4, the velocity curve of a seismic trace is extracted; step 5, relative velocity normalization analysis of different lithologies at the same depth point is carried out; step 6, relative velocity wave impedance inversion processing is carried out; and step 7, lithology and reservoir identification, tracking and description are carried out. The relative velocity analysis well-free constraint relative wave impedance inversion method breaks through the limitation of conventional inversion which can only be used for single target layer and time domain wave impedance inversion, eliminates the influence of different velocities in time domain and depth domain of multiple target layers, and provides relative wave impedance inversion data for lithology or reservoir prediction under complex conditions in a well-free exploration area.

This invention discloses a reservoir prediction method and system based on multi-source information fusion, belonging to the field of reservoir prediction technology. The method includes: high-fidelity signal processing of seismic data combined with seismic geological conditions of the target area; fine well-seismic calibration using well logging, seismic, and geological information combined with domain knowledge; seismic facies identification using a deep embedded self-organizing map network; multi-scale fault detection using cepstral analysis-enhanced coherent ant body attributes; wave impedance and porosity inversion using seismic waveform indication inversion; reservoir gas-bearing capacity detection by combining data-driven seismic texture analysis, depth domain dispersion analysis, and deep learning of seismic data; and prediction of favorable reservoir distribution by adaptively weighted fusion of the results of seismic facies identification, fault detection, wave impedance and porosity inversion, and gas-bearing capacity detection. The advantage of this invention is that it can achieve accurate and reliable prediction of deep and complex oil and gas reservoirs by integrating multi-source, multi-dimensional, and multi-attribute data.

The invention discloses a riverway boundary identification method based on a geological mode and seismic response characteristics, and the method comprises the steps: building a riverway sand body boundary geological model, building a seismic section, seismic attribute characteristics, a geological forward modeling model and a forward modeling result which are matched with the model, and deepening the recognition of the deposition characteristics of different types of sand bodies and the seismic corresponding characteristics; the river channel sand body boundary analysis disclosed by the invention accords with geological mode cognition and is also matched with earthquake corresponding characteristics, so that the identification method disclosed by the invention has a relatively high professional identification degree, the analysis and recognition of the river channel boundary are closer to underground reality, the identification precision of the river channel sand body in a development area can be effectively improved, and the development area is more accurate. And the problems of high multiplicity of solutions and high characterization difficulty of the inter-well sand body are effectively solved.

The application discloses a kind of robust prestack seismic inversion methods and devices based on coherence constraint algorithm, belong to unconventional oil and gas exploration and development field, comprising: through the extraction of seismic coherence attribute of centrifugal window of space transformation;Based on the approximate formula of azimuthal anisotropy reflection coefficient, the forward operator of fracture reservoir is deduced and constructed, and the positive problem of prestack seismic inversion prediction is clear;Based on the prestack seismic inversion prediction positive problem, under the Bayesian framework, the coherence attribute is added to the objective function of inverse problem as the prior information of seismic inversion prediction, and stable prestack and poststack joint seismic inversion prediction is carried out.The application not only considers the influence of large-scale fracture in post-stack, but also considers the azimuthal anisotropy characteristics caused by small-scale fracture, which can greatly improve the stability of anisotropy parameter inversion of fracture reservoir, and has better predictability for small-scale fracture associated with large-scale fracture.

This specification relates to the field of oil and gas exploration technology, specifically disclosing a method and apparatus for determining the effective reservoir distribution of granite weathering crust. The method includes: analyzing seismic facies types and distribution characteristics based on three-dimensional seismic data of the target granite weathering crust reservoir; extracting reservoir attribute characteristics of the target granite weathering crust reservoir along the layer using maximum amplitude attributes to obtain seismic sensitivity attribute distribution characteristics; delineating the vertical structure of the target granite weathering crust reservoir using well logging data; calibrating the vertical structure with the seismic facies types of the target granite weathering crust reservoir to establish a correspondence between seismic facies types and vertical structure; and determining the effective reservoir distribution characteristics of the target granite weathering crust reservoir based on the seismic facies distribution characteristics, seismic sensitivity attribute distribution characteristics, and the correspondence between seismic facies types and vertical structure. This method can accurately determine the effective reservoir distribution characteristics.