123 results about "Seismic velocity" patented technology

The invention provides a rapid Marchenko imaging method for undulating surface conditions, and the method comprises the following steps: S1, inputting an earthquake shot record of a fixed surface and a depth domain undulating migration surface, and correcting the earthquake shot record to the migration surface through a static correction method; s2, inputting a depth domain seismic velocity model, and constructing an initial downlink focusing function from an underground target area imaging point to an undulating surface migration surface through a ray tracing algorithm; and S3, taking the shot record on the fluctuating offset surface and the initial downlink focusing function as input, and calculating uplink and downlink Green functions from the underground imaging point to the ground through an iterative method. And S4, applying an energy normalization cross-correlation imaging condition to the reconstructed uplink and downlink Green functions to obtain a Marchenko imaging result. According to the Marchenko imaging method, the problems that conventional Marchenko imaging is low in calculation efficiency and undulating surface conditions cannot be processed are solved, and the Marchenko imaging quality under the undulating surface conditions is ensured while the calculation efficiency is improved.

The invention discloses a three-dimensional intelligent seismic velocity modeling method based on a two-dimensional self-attention U-shaped network and low-rank adaptation, and belongs to the technical field of seismic services of geophysical exploration. The problems that in the prior art, a traditional three-dimensional intelligent seismic velocity modeling method is low in efficiency and insufficient in model generalization ability are solved. The method comprises the following steps: generating a three-dimensional hybrid geological velocity model, obtaining multi-shot seismic record data based on a wave equation, obtaining a three-dimensional initial velocity model through Gaussian filtering, and obtaining an imaging result by adopting reverse time migration; carrying out dimension reduction processing on the three-dimensional data, and constructing a multi-dimensional pre-training sample set; carrying out pre-training by utilizing the training set to obtain an optimized LAUNet framework; and performing fine tuning by adopting a dual-stage collaborative optimization mechanism, and obtaining a fine-tuned LAUNet framework through a LoRA * method. The method effectively improves the model inversion efficiency, and can be applied to the establishment of a three-dimensional intelligent seismic velocity model.

Systems and methods for predicting seismic velocities for a subsurface formation include obtaining seismic data from a subsurface formation. The seismic data includes an amount of information about the seismic formation. A reduced seismic dataset is generated by applying an unsupervised machine learning model to cluster the seismic data. The reduced seismic dataset has less data than the seismic data, and the reduced seismic dataset includes the amount of information about the subsurface formation. Inversion models and forward modeling data are generated by performing a full waveform inversion for the reduced seismic dataset. The inversion models specify velocities in the subsurface formation and the forward modeling data specify response values of the inversion models. A supervised machine learning model is trained using the inversion models and the forward modeling data; and seismic velocities are predicted by providing the seismic data as input to the trained supervised machine learning model.

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 invention discloses a seismic-electromagnetic joint inversion method based on bidirectional cross gradient constraint. The method comprises the following steps: firstly, establishing three physical field initial models; taking a seismic grid as a main grid, and mapping respective models of a direct current method and a transient electromagnetic method to the main grid through interpolation; constructing a joint inversion objective function containing data fitting terms of the three physical fields, model constraint terms and cross gradient constraint terms between every two of the three physical fields; decomposing the joint inversion objective function into three inversion objective functions which are coupled with one another, and respectively carrying out iterative updating on the seismic velocity model, the direct current method resistivity model and the transient electromagnetic method resistivity model by adopting a collaborative alternative iteration strategy; each iteration update of any model is constrained by the cross gradient exported by the last iteration result of the other two models, so that the structural consistency between the models is forcibly realized; and after completion, outputting a geologic structure model fusing the three-field information, thereby effectively improving the detection precision and interpretation reliability of the geologic structure under the complex geological condition.

The invention provides a seismic velocity model acquisition method and device, electronic equipment and a storage medium, and relates to the technical field of seismic data processing. The method comprises the steps of obtaining a spatial position, a shot point coordinate, an initial velocity model and an initial imaging result, and inputting the spatial position, the shot point coordinate, the initial velocity model and the initial imaging result into a trained velocity model neural network to obtain a target velocity model. According to the method, the space structure information in the imaging result can be extracted by using the trained speed model neural network, and the tomography speed model is obtained according to the space structure information, so that the obtained speed model is higher in resolution and more accurate in structure.

Methods and systems are disclosed. The methods may include obtaining vertical seismic profile (VSP) data and a seismic velocity model for a subterranean region of interest and determining, using the seismic velocity model and reverse time migration, a pre-stacked migrated seismic image based on the VSP data. The pre-stacked migrated seismic image includes seismic slices. The methods may further include, for each of the seismic slices in turn, determining a coherency map, determining a weighting map based on the coherency map, and determining a corrected seismic slice by applying the weighting map to each of the seismic slices. The methods may still further include determining a post-stacked corrected migrated seismic image based on the corrected seismic slices and determining a location of a geological feature within the subterranean region of interest based, at least in part, on the post-stacked corrected migrated seismic image.

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.

The invention discloses a method and a system for establishing a low-frequency model based on multi-attribute fusion. The method for establishing the low-frequency model based on the multi-attribute fusion comprises the following steps: establishing a work area longitudinal wave velocity compaction trend body; based on the longitudinal wave velocity compaction trend body and the seismic horizon velocity body, constructing a pre-stack parameter elastic very low frequency model; establishing a well interpolation model based on the three-dimensional constructive framework; and performing frequency domain merging on the pre-stack parameter elastic very-low-frequency model and the well interpolation model to obtain a multi-attribute fusion low-frequency model. According to the method, seismic velocity information, regional compaction trend information, logging information and geologic model information are fully fused to participate in establishment of the low-frequency model, the'bullseye 'phenomenon of a conventional well interpolation low-frequency model is improved, the overall rule is matched with the structure background, and the problems that well drilling distribution is not uniform, reservoir heterogeneity is high, and the reliability is high are solved. And the low-frequency model precision is not high due to fast transverse change.

The construction of an uphole-calibrated velocity model from uphole seismic survey data using a machine learning model. Uphole seismic survey data may be processed to obtain seismic travel times sorted in a midpoint-offset domain. The machine learning model may be trained with pairs of training data that include travel time vs offset and uphole time, travel times vs offset and uphole velocity, and travel times vs. offset and seismic velocity determined from an interval velocity interpretation of uphole times. The trained machine learning model may output calibrated pseudo uphole velocities having a vertical resolution comparable to the existing upholes.

The present invention relates to a combined first-arrival traveltime tomography method and system based on streamer and OBN data, belonging to the technical field of seismic exploration modeling. The method addresses the problems of low accuracy, insufficient data utilization, and inapplicability of existing models to complex geological structures in the prior art. The method comprises: obtaining raw streamer seismic data and raw OBN seismic data from a target work area; after preprocessing, calculating the streamer traveltime and OBN traveltime of each detector point; constructing an initial seismic velocity model for the target work area and iteratively updating it; in each iteration, obtaining streamer deviations based on the initial seismic velocity model and streamer traveltimes, and obtaining OBN deviations based on the initial seismic velocity model and OBN traveltimes; calculating a slowness correction based on the streamer deviations and OBN deviations; and updating the slowness of the initial seismic velocity model based on the slowness corrections to obtain an updated initial seismic velocity model, which serves as the initial seismic velocity model for the next iteration.

The application discloses a kind of multi-information fusion's speed modeling method and device, the method includes, after seismic migration, the imaging data A of acquisition, data phase control type mask B, seismic structure interpretation horizon and fault data C, well logging velocity curve data D and seismic velocity field data E;Edge feature of imaging data A is extracted by edge detection technique, and the structural skeleton data F of seismic image is obtained;Phase control type mask B is fused with structural skeleton data F, and phase control structure guide data G is obtained;Seismic structure interpretation data C is added in phase control structure guide data G, and space interpolation guide data H is formed;Through two-step interpolation extrapolation, the space interpolation distance weight field Q corresponding to space interpolation guide data H and well logging velocity interpolation filling model P are obtained;Classification mask M is established for well logging velocity interpolation filling model P;Two-step filling is carried out to class mask M, and multi-information fusion model R is obtained.

The present invention provides a method and apparatus for processing seismic imaging travel time data. The method comprises: establishing a seismic velocity structure model of a target work area based on seismic imaging data of the target work area; establishing an eikonal equation for the seismic imaging data; and determining the earthquake first arrival times of the seismic imaging travel time data based on the seismic velocity structure model, the eikonal equation, and pre-established upwind difference format data. The method and apparatus for processing seismic imaging travel time data provided by the present invention can replace manual processing of seismic imaging travel time data, achieving fully automated processing of seismic imaging travel time data and offering the advantages of high efficiency and excellent accuracy.

The present invention discloses a method and apparatus for fusion velocity modeling and seismic velocity modeling while drilling (SWD) using well-seismic information. The method includes a wellpoint location velocity fusion step, wherein for each drilled well, a velocity difference curve between a VSP logging velocity curve and a surface seismic velocity volume is determined; a control node corresponding to each layer is determined based on the well's layered data; the average velocity between two adjacent control nodes is determined based on the well-seismic calibration results and the well's layered data; the velocity difference curve is smoothed using a cubic spline function curve simulation with the average velocity as a constraint; a fused velocity curve is obtained from the smoothed velocity difference curve and the seismic velocity volume; and a seismic velocity modeling step, wherein a velocity model is established using a co-kriging interpolation method with the surface seismic velocity volume and the fused velocity curve of at least one drilled well as constraints, thereby obtaining a fused surface seismic velocity volume. This method can improve the efficiency and accuracy of seismic velocity modeling.

A method for training travel time-based networks and building an image of a velocity model includes obtaining a seismic dataset of seismic traces and determining an observed travel time for each seismic trace. The method further includes obtaining a velocity network, that depends on one or more velocity parameters, and a travel time network, that depends on one or more travel time parameters. The method further includes training the velocity network and the travel time network using a cost function and an optimizer. The cost function is based on the travel times parameters, the velocity parameters, a travel times equation, a derivative of the travel times equation, and a travel time mismatch between a first observed travel time and a first travel time value output by the travel time network. The method further includes building the image of a velocity model using the trained velocity network.

A method for training travel time-based networks and building an image of a velocity model includes obtaining a seismic dataset of seismic traces and determining an observed travel time for each seismic trace. The method further includes obtaining a velocity network, that depends on one or more velocity parameters, and a travel time network, that depends on one or more travel time parameters. The method further includes training the velocity network and the travel time network using a cost function and an optimizer. The cost function is based on the travel times parameters, the velocity parameters, a travel times equation, a derivative of the travel times equation, and a travel time mismatch between a first observed travel time and a first travel time value output by the travel time network. The method further includes building the image of a velocity model using the trained velocity network.

This invention relates to the field of seismic imaging technology and discloses a method, apparatus, equipment, and medium for forward modeling of seismic acoustic waves in a viscoelastic-acoustic (VTI) medium. The method includes: obtaining a desired Q value; calculating the viscoelastic velocity of the seismic acoustic wave based on the desired Q value; fitting parameters of a generalized standard linear body model; constructing a wave equation for the seismic acoustic wave in the VTI medium based on the generalized standard linear body model; and solving the wave equation for the seismic acoustic wave in the VTI medium using the finite difference method and the viscoelastic velocity. This invention solves the problem that traditional seismic simulations cannot simultaneously consider seismic anisotropy and seismic attenuation, significantly improving the accuracy and applicability of seismic simulations, as well as the accuracy of seismic simulations in complex terrain, geology, and oil and gas reservoir areas. It provides a foundation for subsequent high-precision seismic velocity modeling and seismic migration imaging.

The present invention relates to a method for depth migration while drilling based on a horizontal well trajectory, belonging to the field of seismic velocity modeling and imaging technology. The specific steps are as follows: determining two control points on the well trajectory, namely the first control point and the second control point; loading the well information of the first control point and the second control point; calculating the well-seismic difference at each control point and obtaining the anisotropy parameter δ; interpolating and extrapolating the δ values ​​of the first control point and the second control point to the entire target area; setting the anisotropy parameter ε; and calculating the vertical velocity V. an ; Update the model depth according to the "travel time remains unchanged" principle; Generate anisotropy field; Input δ, ε and V into GeoEast software an Perform anisotropic prestack depth migration processing and check whether the anisotropic prestack depth migration profile meets the requirements of horizontal well drilling. This solves the problem of low imaging accuracy at the boundaries of the seismic work area and significantly improves the well-seismic consistency rate at the boundaries.

Systems and methods are disclosed. The method includes obtaining a sparse seismic velocity model for a subterranean region of interest. The sparse seismic velocity model includes populated positions and missing positions, where each populated position includes a velocity. The method further includes determining an interpolated seismic velocity model by determining the velocity at each missing position using the velocity at one or more of the populated positions and interpolation. The method still further includes determining an intermediate dense seismic velocity model by updating the velocity at each missing position using, at least in part, the interpolated seismic velocity model and a projections onto convex sets method and determining a dense seismic velocity model using, at least in part, the intermediate dense seismic velocity model.

The invention belongs to the technical field of oil and gas field development, and discloses a velocity modeling method and device for an oil reservoir static model and a medium, and the method comprises the steps: S1, extracting the velocity of a stratum depth midpoint at a well point based on an interval transit time logging curve and time domain seismic data; s2, based on the seismic velocity field, extracting the velocity of a stratum depth midpoint at the control point; s3, carrying out sampling and statistics on the stratum depth midpoint velocities at the well point and the control point, and calculating a stratum velocity surface; s4, according to a plurality of layers of velocity surfaces, establishing a velocity model of the whole region; and S5, performing time-depth conversion by using the speed model, and completing adjustment and improvement of the speed model on the basis of well correction. The time domain can be accurately and efficiently converted into the depth domain, and the working efficiency of oil and gas field development and production is improved.

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 present application relates to a kind of multi-information fusion phased low-frequency modeling method, device and storage medium.The method is drilled well logging data and measured elastic parameter curve, fitting out the fitting relationship of logging longitudinal wave velocity curve and other elastic parameter curve.According to fitting relationship, seismic velocity is converted into low-frequency elastic parameter body.Seismic velocity is the longitudinal wave velocity obtained by processing seismic data.From low-frequency elastic parameter body, the elastic parameter pseudo well curve of drilled well is extracted, and according to the elastic parameter pseudo well curve of drilled well and measured elastic parameter curve, the relative elastic parameter curve of depth-trend is calculated.Based on fault, horizon structure framework and relative elastic parameter curve, relative elastic parameter model is obtained.Low-frequency elastic parameter body is phased backfilled into relative elastic parameter model to obtain multi-information fusion phased low-frequency model.The present application can improve the precision of low-frequency modeling, and then improve the accuracy and reliability of subsequent seismic inversion reservoir prediction result.

The invention provides a building structure seismic wave velocity change extraction method, device, equipment and program product. The method comprises the following steps: deploying seismometers at a plurality of floors of a target building to continuously collect a microseismic time sequence of each of the plurality of floors in a specified direction; and based on the micro-seismic time sequence of each adjacent floor, calculating an interference result corresponding to each adjacent floor, and performing inverse Fourier transform through the interference result to obtain an impulse response function corresponding to each adjacent floor. And determining travel time offset of the pulse response function corresponding to each adjacent floor and a reference pulse response function, and performing linear fitting on the travel time offset to obtain seismic wave velocity change of each adjacent floor.

The application discloses a well control velocity modeling method based on structure guidance and application thereof, and belongs to the technical field of seismic data processing in oil exploration. The application utilizes logging, VSP velocity and seismic velocity analysis, establishes a space variable factor field based on structure guidance, further uses the space variable factor field to constrain a multi-azimuth grid tomography residual velocity field, optimizes velocity update quantity, completes multi-azimuth grid tomography multi-round iteration of the well control velocity field based on structure guidance, and obtains the well control velocity model based on structure guidance. The well control velocity model obtained by using the modeling method provided by the application can consider the longitudinal resolution and the lateral change law of the velocity field, ensures that the updated velocity field is consistent with the well velocity, and the velocity change trend is also consistent with the seismic geology. The application can be used for establishing a high-precision velocity field and a Q field and for "true" surface TTI prestack depth migration, and is further applied to the fields of prestack inversion and calculation of geomechanical parameters.

The present invention relates to a method for accurately delineating the sand-mud transition zone of a uranium reservoir based on three-dimensional seismic velocity inversion, comprising the steps of positioning and calibrating the uranium reservoir based on well seismic data, implementing structural interpretation of the target layer and constructing an initial inversion model, implementing high-resolution velocity inversion to obtain a velocity body and quality control, filtering the velocity body to obtain a low-frequency velocity body, maximizing the dominant velocity value of the ore-bearing sand-mud transition zone, obtaining a body characterizing the sand-mud transition zone attribute, and delineating the sand-mud transition zone. This method achieves a three-dimensional depiction of the sand-mud transition zone in full three-dimensional space; it can analyze the spatial configuration relationship between the ore body and the sand-mud transition zone, thereby facilitating the summary of the corresponding mineralization laws and mineralization-controlling factors; the sand-mud transition zone finally delineated is of great significance for the comprehensive analysis of the mineralization mechanism and distribution law of sandstone-type uranium deposits in the sand-mud transition zone, and provides strong technical support for enriching the overall mineralization theory of sandstone-type uranium deposits and establishing prospecting models.

The application discloses a method for pre-stack seismic velocity inversion of natural gas hydrate reservoir based on deep learning. According to the occurrence characteristics of natural gas hydrate and free gas under actual geological conditions, combined with the real seabed layered sedimentary area and the public ocean velocity model, a synthetic velocity model of thin layer associated structure containing high and low speed alternating hydrate and free gas is constructed; the model is forward by solving the wave equation to generate forward seismic records; the forward seismic records and the synthetic velocity model are registered, and the training, testing and verification sets are divided; the deep neural network is constructed, and the training environment and hyperparameters are configured; the perception loss is introduced, the mixed loss function is designed, the network training is multi-dimensionally constrained, the network is optimized and the optimal parameters are saved; the optimal parameters are called to batch inversion of seismic records, and high-precision velocity inversion and thin layer identification are realized. Through the above method, the inversion efficiency and reservoir characterization ability are improved, and effective technical means are provided for natural gas hydrate exploration.

The present application relates to a kind of two-dimensional seismic velocity model automatic generation method and system in petroleum exploration, it includes: with diversity training sample to generate curved interface with combination linear function, sine function, based on stratum parameter generated by random number generator automatically generates two-dimensional layered velocity model;Extract the shape of multiple mushroom images as the basic form of salt dome, and after the basic form of salt dome is handled in posture, superimpose one of the mushroom images on the two-dimensional layered velocity model that rises upward, generate salt dome two-dimensional velocity model;With two-dimensional layered velocity model as basis, generate normal fault and reverse fault, construct two-dimensional fault velocity model;Determine the similarity between all two-dimensional layered velocity model, salt dome two-dimensional velocity model and two-dimensional fault velocity model, eliminate high similarity velocity model, obtain the diversity of two-dimensional seismic velocity model.The present application can provide the diversity of data guarantee for data-based seismic velocity inversion;In the field of oil and gas exploration.

Method and apparatus for constructing seismic velocity models of the underground geologic formation using full waveform inversion are provided. The method evaluates velocity model accuracy by measuring local traveltime differences between recorded seismic data and synthetic data. The travetime misfit may be calculated with a hybrid L1 / L2 norm that relatively lowers down the influence of large traveltime difference in each iteration and ensures the inversion stability. The method tolerates poor quality initial model and generates high resolution models.

The invention relates to a multi-pulse low-frequency component extraction method for sliding impulse type near-fault seismic oscillation. The method comprises the following steps: inputting a sliding impulse type near-fault seismic velocity record SW as a target seismic velocity time history V (t); processing the target seismic velocity time history V (t) by adopting a moving average filtering method to obtain a quasi low-frequency velocity component MnD; fitting the quasi-low-frequency velocity component MnD by using a Gaussian mixture multi-pulse fitting function vp (t) based on a least square method to obtain a Gaussian mixture multi-pulse fitting function vpv (t) of the target seismic velocity time history V (t); and calculating a multi-pulse low-frequency component by a Gaussian mixture multi-pulse fitting function vpv (t) of the target seismic velocity time history V (t), including calculating a multi-pulse velocity time history v (t), a multi-pulse acceleration time history a (t) and a multi-pulse displacement time history s (t). A main pulse signal, a secondary pulse and a local pulse can be extracted, and the defect that an existing extraction means cannot accurately extract the secondary pulse and the local pulse is overcome.

The application discloses a VSP acoustic wave full waveform inversion method of a double-branch physical driving cyclic neural network, applied to the field of seismic data processing, and aims at the problems of weak explanation and high calculation cost of the existing seismic velocity inversion technology in a full data driving mode. Since a wave equation describes the change of a wave field in time and space, a time stepping method is adopted in numerical simulation, that is, the wave field state of the next moment is calculated according to the state of the previous moment. Thus, the wave field state of the previous moment is taken as a hidden layer of the RNN, the acoustic wave equation is solved by finite difference, and the velocity parameter in the equation is taken as a trainable parameter in the physical driving RNN forward network. The shot record output by the physical driving RNN forward network each time is taken as a predicted value. The shot record output by the physical driving RNN forward network by setting a real velocity parameter is taken as an observed value. The process of correcting the velocity parameter by reverse propagation of the loss between the predicted value and the observed value is called a velocity inversion process.