135 results about "Seismic interpretation" patented technology

The invention discloses a fault identification method combining enhanced convolution and multi-scale attention, and relates to the technical field of seismic interpretation and reservoir prediction. The method comprises the following steps: firstly, on the basis of seismic data high-resolution enhancement processing, generating a contrast-invariant four-channel image representation by using a local intensity sequence transformation filter; then, constructing an enhanced convolution encoder in combination with differential convolution to extract fault edge information, and meanwhile, constructing a convolution Mama attention module by fusing multi-scale separable convolution and a Mama framework to extract space and global information; and finally, constructing a feature fusion module to fuse the shallow features of the jump connection with the deep features of the decoder, and outputting a fault recognition result through full connection layer mapping. Compared with the prior art, the method combines the advantages of the local intensity sequence transformation, the attention mechanism, the convolutional network and the Mama architecture, can more effectively learn the fault feature information in the seismic data, and improves the fault recognition precision.

A method is described for 3D seismic interpretation using a pre-trained 3D transformer that is fine-tuned for desired types of interpretation. It may be used with field seismic or synthetic seismic. The method is executed by a computer system.

The invention relates to a fault stability evaluation method considering a pressure-stress coupling effect. The method comprises the following steps: calculating a pressure-stress coupling ratio according to target layer rock mechanical parameters or field actual measurement pressure and ground stress data; fault three-dimensional structure modeling is carried out based on the seismic interpretation result, and fault occurrence distribution is simulated and calculated; analyzing and calculating the initial stress of the section and the stress after the coupling action by combining the three-way principal stress, wherein the stress comprises shear stress and effective normal stress; a quadratic equation of unary about fault instability critical pressure is constructed, and fault instability risk types are judged in combination with the actual pressure limit of oil field production; and calculating the critical pressure of fault instability under different risk types. According to the method, the influence of the pressure-stress coupling effect on the fault stability can be considered, so that the fault stability evaluation result is more practical.

Storing and retrieving subsurface rock physical property models for well drilling by generating estimated subsurface rock physical property models based on a respective set of seismic data, determining a stratigraphic classification for each model based on seismic interpretation data; storing the models associated classification in a database, determining characteristics of the seismic data used to generate the models, selecting from the database a subset of the models based on a similarity of the stratigraphic classification, determining measures of similarity between the characteristics of the seismic data used to generate each of subset of the models and a new set of seismic data and retrieving an estimated subsurface rock physical property model from the subset models based on the similarity measures.

The present application belongs to the technical field of geophysical well logging data processing, and particularly relates to a multi-scale dual-branch neural network well logging curve reconstruction method based on seismic constraint, which comprises reading in well-seismic and interpreted horizon data, target interval sublayering and facies belt division based on three-dimensional seismic interpretation, pyramid multi-scale training set sample construction by fusing multi-curve and well flanking seismic trace information, residual-Transformer dual-branch neural network training model establishment taking a composite loss function as the core, three-stage progressive training strategy and reconstruction of target well missing section and result evaluation. The method still maintains high stability and robustness under the condition of complex heterogeneous reservoirs; on the basis of seismic data constraint, geological sublayering and facies belt division and other interpretation information are fused, so that the reconstruction result is more in line with geological laws; and the obtained reconstructed curve provides reliable support for re-interpretation of old data and full use of incomplete well logging data.

The invention discloses a seismic image super-resolution reconstruction method based on a double-branch multiple attention network, and belongs to the technical field of seismic exploration, and the method comprises the steps: constructing a data sample library for model training; adopting a super-resolution backbone network and a gradient branch auxiliary network to construct a double-branch multiple attention network model; and training the double-branch multiple attention network model by adopting the data sample library and the mixed loss function to obtain a network model for realizing seismic image super-resolution reconstruction and high-frequency texture information reconstruction, and performing reconstruction based on the trained network model to obtain a seismic image subjected to super-resolution reconstruction. According to the method, a gradient branch is introduced as an auxiliary network, so that the super-resolution reconstruction effect is enhanced. Meanwhile, a mixed loss function is adopted to train the network, and a loss function for constraining high-frequency information is particularly introduced, so that the common structural deformation problem in a super-resolution task is effectively solved, the generation of artifacts is prevented, and a new technical approach is provided for high-precision seismic interpretation.

A method for the time-depth conversion of a concealed structure in a foreland thrust belt with a low exploration degree. The method comprises: obtaining velocity variation characteristics of a study area; using seismically interpreted horizons and fault data in a time domain to establish a geological structure model; by means of a stratigraphically controlled and fault-block-division-based velocity assignment method, converting the geological structure model into an interval velocity model; establishing virtual well control points in the study area, and extracting from the interval velocity model an interval velocity curve at each virtual well control point (S400); converting the interval velocity curve at each virtual well control point into an average velocity curve (S500); using a stratigraphically controlled and fault-block-division-based virtual well control point spatial interpolation method to construct a three-dimensional average velocity volume; and performing time-depth conversion on target horizons. Further provided is an apparatus for the time-depth conversion of a concealed structure in a foreland thrust belt with a low exploration degree. By means of an obtained depth-domain structural map, the identification of a concealed structure in a foreland thrust belt with a low exploration degree can be realized, thereby providing reliable depth data for the detailed description of traps.

A method includes receiving seismic data of a subsurface. The method also includes color processing the seismic data to produce color-processed seismic data. The method also includes performing a seismic interpretation on the color-processed seismic data to identify regional geologic features. The method also includes performing first mapping along the regional geologic features to identify local geologic features. The method also includes extracting reservoir bodies from the local geologic features based upon the first mapping. The method also includes performing second mapping along the local geologic features to embed the local geological features into the regional geologic features. The method also includes delineating boundaries of reservoirs or seals in the subsurface based upon the local geologic features that are embedded into the regional geologic features. The method also includes de-risking a fluid prospect in a fluid reservoir based upon the boundaries of the reservoirs or the seals.

The invention discloses a novel method for establishing a high-precision three-dimensional velocity field in a horizontal well deployment area, and the method comprises the steps: firstly determining two or more oil layer group-level seismic standard layers near a target layer, then building a vertical reasonable constraint frame model, calibrating a velocity spectrum through a fitting relation, and then building an initial three-dimensional velocity model. And performing geological mode constrained filtering processing on the velocity curve of each well to obtain a smooth velocity curve capable of reflecting reservoir change, training the model by adopting a multi-layer feedforward neural network intelligent algorithm, and applying a model training result to the initial three-dimensional velocity model to obtain a high-precision three-dimensional velocity field. The time-depth relationship, the three-dimensional velocity spectrum, the seismic interpretation result and other information of the well are fully applied, and through pseudo well velocity analysis, velocity curve filtering, artificial intelligence algorithm fusion and the like, the error between a depth domain result and the well is reduced, so that the accuracy of horizontal well region velocity establishment is improved.

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

The invention provides a time-depth conversion method and device for a hidden structure of a low-exploration-degree foreland thrust zone, and belongs to the technical field of geological exploration. The method comprises the steps of obtaining speed change characteristics of a research area; establishing a geologic structure model by using the seismic interpretation horizon and fault data of the time domain; converting the geologic structure model into an interval velocity model through an interval control breaking block velocity assignment method; establishing virtual well control points in the research area, and extracting an interval velocity curve at each virtual well control point from the interval velocity model; converting the interval velocity curve at the virtual well control point into an average velocity curve; constructing a three-dimensional average velocity body by using a layer control breaking block virtual well control point spatial interpolation method; and performing time-depth conversion on the target layer. The variable-speed mapping is performed based on the time-depth conversion method realized by the invention, and through the obtained depth domain structure map, the identification of the hidden structure of the low-exploration-degree foreland thrust zone can be realized, and reliable depth data is provided for fine description of entrapment.

A method for estimating higher-order dependencies from input data includes receiving input data. The method also includes generating multi-tapered spectral estimates based upon input data. The method also includes determining dependencies based at least partially upon the multi-tapered spectral estimates. The method also includes building or updating a deep learning foundation model to employ the dependencies.

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

The invention provides a well control constrained seismic data frequency expanding method based on broadband wavelets, and the method comprises the steps: S1, determining the feasibility of frequency expanding through the analysis of an amplitude spectrum and a signal-to-noise ratio spectrum of original seismic data in combination with the drilling condition of an actual work area; s2, performing high-frequency seismic forward modeling on the geologic model of the multi-well target sand body to determine a wavelet dominant frequency capable of identifying the target sand body; and obtaining three-dimensional matching factor data according to the wavelet dominant frequency and completing data frequency expansion processing with the constructed broadband wavelet. The spatial matching factor library can be obtained by fully utilizing broadband information of well data in an actual work area and existing mature geological knowledge, wavelet side lobes are further suppressed through amplitude envelope, broadband wavelets are constructed, dominant frequency improvement and frequency band expansion of seismic data are achieved, and the requirements of seismic fine interpretation are met.

The invention belongs to the technical field of geophysical logging data processing, and particularly relates to a multi-scale double-branch neural network logging curve reconstruction method based on seismic constraints. Comprising the steps of reading in well seismic and interpreting horizon data, performing target layer section layering and phase belt division based on three-dimensional seismic interpretation, fusing multiple curves and well-side seismic channel information to construct a pyramid multi-scale training set sample, establishing a residual-Transformer double-branch neural network training model by taking a composite loss function as a core, and constructing a multi-scale training set sample based on the residual-Transformer double-branch neural network training model. And a three-stage progressive training strategy is adopted, and target well missing section reconstruction and result evaluation are carried out. According to the method, high stability and robustness are still kept under the condition of a complex heterogeneous reservoir; interpretation information such as geological layering and phase belt division is fused on the basis of seismic data constraints, so that a reconstruction result better conforms to geological laws; and the obtained reconstruction curve provides reliable support for re-explanation of old data and full utilization of incomplete logging data.

The invention provides a seismic data bidirectional frequency band expansion method based on carrier modulation, which relates to the field of seismic exploration, and comprises the following steps: processing original seismic data through time window segmentation, calculating an autocorrelation function and carrying out time domain continuation; solving a prediction coefficient based on entropy maximization constraint to generate an amplitude spectrum; calculating a boundary attenuation rate through boundary frequency point positioning; constructing an attenuation compensation function to generate a frequency domain carrier function; applying sparse constraint to solve a weighting factor and iteratively calculating a modulation amplitude spectrum; and finally, carrying out combined inverse transformation with an original phase spectrum to obtain expanded data. According to the method, the resolution and the signal-to-noise ratio of the seismic data are effectively improved, and the seismic interpretation precision is improved.

The invention belongs to the technical field of geologic structure interpretation and modeling, and particularly relates to a multi-data fusion seismic interpretation and geologic modeling method for a complex structural zone. The invention discloses a multi-data fusion seismic interpretation and geologic modeling method for a complex tectonic zone. The method comprises the following steps: observing and judging formation and fault development characteristics in a work area through an earth surface outcrop fault; interpreting a stratigraphic dip angle by adopting imaging logging to identify a fault; using 3D-VSP data to enhance seismic imaging and identify faults; a low-angle fault is recognized through conventional logging interpretation; identifying a velocity abrupt change interface by adopting a seismic velocity spectrum to determine a fault; through fault comprehensive judgment identified by different data and methods, the fault position is determined, and a geological structure mode of a research work area is constructed. According to the method, through fusion interpretation, fault position identification, seismic interpretation and geologic structure modeling of various data such as outcrop, logging data, seismic data and velocity field, the accuracy of geologic structure modeling can be improved, the success rate of oil-gas exploration is improved, and meanwhile, the cost is saved.

A system and method in accordance with the present disclosure include a workflow applied to identify areas of confidence in seismic interpretations that meets a pre-selected threshold by utilizing machine-learning (ML) techniques. The ML techniques enable one or more filters to be applied, based on a range of confidence values, to areas of seismic interpretation both in the vicinity of the faults but also away from fault locations. The contribution of the various inputs is evaluated and weighted by the ML model, which reduces the time to prepare input seismic interpretation data and to obtain results from executing the model.

The present application relates to the technical field of rock lithology exploration, and more particularly to a dolomite lithology prediction method, system and device; comprising the following steps: S1, obtaining drilled well data and seismic data of a region to be identified; S2, performing standardization processing; S3, performing well control feature frequency extraction; S4, performing fine production of a synthetic record for the region to be identified; S5, determining a seismic interpretation mode scheme for the region to be identified; S6, determining dolomite attributes; S7, obtaining a first content of dolomite in the region to be identified; S8, determining a second content of dolomite in the region to be identified; S9, determining whether the dolomite attributes determined in the step S7 are qualified according to the first content of dolomite and the second content of dolomite; and quantitatively predicting the content of dolomite in a carbonate rock formation.

This invention relates to a method, apparatus, migration method, device, and medium for establishing an anisotropic parameter field. The method includes: obtaining the thickness of the target logging layer at each well point based on well logging layer information; obtaining the thickness of the target seismic interpretation layer corresponding to the well point based on seismic interpretation layer information; calculating the thickness factor of the well point at the target logging layer based on the thickness of the target seismic interpretation layer and the thickness of the target logging layer; and correcting the target logging layer based on the thickness factor of each well point and the thickness of the target seismic interpretation layer to obtain the corrected target logging layer, thereby establishing an anisotropic parameter field. In this embodiment, by constraining the logging layers between well points under the lateral trend of the seismic interpretation layer, the difference between the established anisotropic parameter field and the anisotropic parameter field of the actual subsurface medium is reduced.

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.

The invention belongs to the technical field of earthquake comprehensive interpretation research, and discloses a lithofacies identification method based on difference analysis, which comprises the following steps: firstly, calculating to obtain difference data of a longitudinal wave velocity logging curve, difference data of a transverse wave velocity logging curve and difference data of a density logging curve; performing clustering analysis on the difference data of the longitudinal wave velocity logging curve, the difference data of the transverse wave velocity logging curve and the difference data of the density logging curve by using a Gaussian Mixture method according to the drilling lithofacies type to obtain logging lithofacies data; analyzing the lithofacies depth trend of the well logging according to the lithofacies data of the well logging, and predicting the lithofacies space distribution of the well logging by taking the lithofacies depth trend as a constraint and utilizing a Bayesian method; the invention further provides a lithofacies recognition device and equipment based on difference analysis and a storage medium. The lithofacies can be accurately identified, the identification working efficiency is improved, and the method has a good application effect and a good popularization prospect.

The present application belongs to the technical field of oil exploration, and discloses a method for evaluating the development degree of sand-mudstone reservoir-cap assemblage, which specifically comprises the following steps: conducting regional data analysis for a study area, and mastering the core / cuttings data, logging data and oil testing and production testing data of drilled wells, as well as the seismic data and seismic interpretation horizon data of the whole work area; conducting pre-stack deterministic inversion based on Zeoppritz equation to obtain the data body of the ratio of P-wave velocity to S-wave velocity in the work area; extracting the numerical value of the ratio of P-wave velocity to S-wave velocity along the sandstone layer interpreted by the seismic data to obtain the plan view of the sandstone reservoir; extracting the numerical value of the ratio of P-wave velocity to S-wave velocity along the mudstone layer interpreted by the seismic data to obtain the plan view of the mudstone cap rock; quantifying the relative development degree of the reservoir-cap assemblage by a development degree index calculation formula, constructing the plan view of the development degree index of the sand-mudstone reservoir-cap assemblage, and quantitatively dividing the development degree of the reservoir-cap assemblage. The present application can clearly define the development range of high-quality reservoir-cap assemblage, delineate the favorable exploration area, and further guide the well site deployment of exploration wells.

Systems and methods include a computer-implemented method for presenting an interpretation result of synchronized seismic data and fracturing treatment time. A standard format seismic data set of sensor readings obtained from three-component sensors is generated. Coordinates and recording times corresponding to the sensor readings are added to the standard format seismic data set. Synchronized seismic data is generated from the standard format seismic data set by synchronizing the seismic recording times with the fracturing treatment time. Quality-controlled synchronized seismic data is generated by removing dead traces and abnormal data samples from the synchronized seismic data. A time-frequency analysis is performed on the quality-controlled synchronized seismic data, including performing a short-time Fourier transform to analyze changes in Fourier spectra over time. Based on the time-frequency analysis, a resonance frequency is extracted from each spectrum at different time samples. An interpretation result based on the resonance frequency is presented to a user.

The invention provides a fracture prediction method and device for a shale reservoir. The method comprises the following steps: acquiring a seismic interpretation result of a target shale reservoir; applying a geochemical test analysis method and the seismic interpretation result to determine a key fracture forming period corresponding to the target shale reservoir, and tectonic deformation historical information and rock mechanical parameters of the key fracture forming period; according to the geologic model and rock mechanical parameters of the target shale reservoir in the key fracture forming period, constructing a geomechanical model of the key fracture forming period; and according to the geomechanical model of the key fracture forming period, tectonic deformation historical information and a preset superposition calculation model, fracture parameters of the target shale reservoir in the key fracture forming period are obtained through prediction. According to the invention, the accuracy of crack prediction of the shale reservoir can be improved.

This invention relates to a method for constructing a three-dimensional formation velocity model, belonging to the field of three-dimensional seismic interpretation technology. First, this invention utilizes seismic data from the target area and the time-depth relationship of the pilot sections of vertical and horizontal wells to establish a well-seismic fusion three-dimensional velocity model. Then, based on the lithology encountered in the horizontal section, sonic logging curves, and the fitting relationship between sonic logging and lithology, the layer velocity at the horizontal section of the target layer in the target area is determined. This layer velocity is then interpolated and smoothed to obtain a horizontal section layer velocity model. Finally, this horizontal section layer velocity model is used to constrain the well-seismic fusion three-dimensional velocity model, resulting in the corresponding three-dimensional formation velocity model of this invention. This invention utilizes well logging information from the horizontal sections of horizontal wells to constrain and correct the three-dimensional velocity model, which not only improves the accuracy and stability of the lateral variations in the three-dimensional velocity model but also enhances the accuracy of low-amplitude structural interpretation.

The present application relates to the technical field of oilfield exploration and development, and particularly relates to a method for predicting the distribution range of deep-water turbidite sand bodies. The method comprises the following steps: using three-dimensional seismic data, actual drilling well data and velocity data to calculate the maximum thickness of the sedimentary sand body of the upthrown wall of the fault which provides the source for the turbidite sand body; calculating the fault throw according to the development form of the three-dimensional seismic interpretation fault in the work area, and comprehensively considering the seismic, velocity and well data; determining the dip angle of the downthrown wall stratum through the two-dimensional seismic profile perpendicular to the fault strike; based on the drilling and logging data, according to the thickness of the deep-water turbidite sand body in the higher exploration degree area of the research area and the critical value of the deep-water turbidite sand body, drawing the sand body thickness contour map of the research area, so as to determine the distribution range of the deep-water turbidite sand body in the higher exploration degree area. The method is suitable for accurately predicting the distribution range of the deep-water turbidite sand body in the faulted basin.

The invention relates to the field of seismic reservoir prediction, and particularly discloses a flat facies thin reservoir prediction method and device based on plane constraint pre-stack inversion, and the method comprises the steps: carrying out the rock physical analysis of logging information, and obtaining the pre-stack sensitive parameters of a reservoir; obtaining plane probabilities corresponding to different rocks based on logging information and geological knowledge; based on the seismic data, the logging information and the seismic interpretation horizon, pre-stack inversion is carried out with the plane probabilities corresponding to different rocks as constraints, and an inversion result is obtained; and calculating to obtain a pre-stack sensitive parameter body of the reservoir based on the inversion result and the pre-stack sensitive parameters of the reservoir. According to the method provided by the invention, geological cognition and geophysical prospecting technologies are combined, on the basis of determining reservoir sensitive pre-stack elastic parameters through logging rock physical analysis, the cognition of sedimentary facies is introduced to determine the plane probability conditions of different lithofacies, and then pre-stack geostatistical inversion of plane constraint is carried out; the multiplicity of solutions of longitudinal and transverse prediction of the beach facies carbonate reservoir is reduced, and the inversion efficiency is high.

The application discloses a method and device for identifying a seismic horizon and a storage medium, and belongs to the technical field of seismic interpretation. The method comprises the following steps: acquiring a seismic data body and a multi-target horizon identification sample set seismic data body, and using the multi-target horizon identification sample set seismic data body to generate a sample seismic profile W r The multi-target horizon identification sample set comprises artificial interpretation seismic horizons obtained after marking and processing the sample seismic profile W r , and at least four auxiliary seismic horizons related to the artificial interpretation seismic horizons; a convolutional neural network is trained according to the sample seismic profile W r and the multi-target horizon identification sample set, and a multi-target horizon identification network Net L is obtained; a to-be-identified seismic profile is substituted into the multi-target horizon identification network Net L , and a transition seismic horizon and at least four auxiliary seismic horizons related to the transition seismic horizon are obtained, and then a target seismic horizon is determined through fusion processing. The method can accurately identify the seismic horizon and improve the accuracy of seismic interpretation.

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.