178 results about "Seismic velocity" patented technology

The invention discloses a VSP sound wave full-waveform inversion method of a double-branch physical driving recurrent neural network, which is applied to the field of seismic data processing and aims to solve the problems of low interpretability, low calculation cost and the like due to the adoption of a complete data driving mode in the existing seismic velocity inversion technology. As the wave equation describes the change of the wave field in time and space, a time stepping method is adopted during numerical simulation, namely, the state of the wave field at the next moment is calculated according to the state at the previous moment. Taking a wave field state at a previous moment as a hidden layer of the RNN, performing finite difference solution on an acoustic wave equation, taking a speed parameter in the equation as a trainable parameter in a physically-driven RNN forward modeling network, and taking a shot gather record obtained by outputting through the physically-driven RNN forward modeling network each time as a predicted value; taking shot gather records obtained through physical driving RNN forward modeling output by setting real speed parameters as observation values; the process of solving the loss between the predicted value and the observed value to carry out back propagation to correct the speed parameter is called as a speed inversion process.

The invention relates to an elastic wave full waveform inversion method based on an optimal transmission function, and belongs to the field of seismic velocity modeling. The method comprises the following steps: firstly, establishing an observation system, selecting an excitation source with a certain frequency and a speed model, and receiving an actual seismic record and a simulated seismic record through the excitation source; secondly, introducing an optimal transmission function as a target function of inversion, and establishing a target functional formula; a timing wave field and a reverse time wave field are obtained through forward modeling and reverse time continuation, derivation is carried out on a target functional formula to obtain a gradient calculation formula, the gradient calculation formula is substituted into the timing wave field and the reverse time wave field, and the gradient is obtained. And optimizing the obtained gradient by using an Adam gradient optimization algorithm, and substituting the optimized gradient into iterative inversion to update the model. And repeating the steps until an inversion result meeting the precision requirement is obtained. Model experiments show that compared with a traditional L2 objective function, the method has the advantages that the event continuity of the inversion model is enhanced, the deep structure is clearer, and the overall precision is greatly improved.

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.

A method includes drilling a wellbore into a subsurface using a drill bit and, for each of a plurality of depths of the drill bit in the wellbore, detecting, at each of a plurality of receiver stations located along a fiber optic cable disposed behind a casing string within the wellbore, a seismic signal created by the drill bit drilling the wellbore, sending an optical signal generated by the detection of the seismic signal from each of the plurality of receiver stations to an interrogator, sorting, using a computer processor, the optical signals by receiver station, and determining, using the computer processor, a seismic velocity using first breaks picked from the sorted optical signals.

Systems and methods for noise-robust time-domain multi-scale full waveform inversion using convolved data are disclosed. The methods include obtaining, using a seismic acquisition system, an observed seismic dataset pertaining to a subsurface region of interest; obtaining, using a seismic processor, a seismic velocity model; and iteratively, using the seismic processor, until a stopping criterion is met: selecting a wavelet with a frequency parameter, wherein the frequency parameter increases with each iteration, forming a convolved seismic dataset based on a convolution of the wavelet with the observed seismic dataset, and updating, using a full waveform inversion, the seismic velocity model based, at least in part, on the convolved seismic dataset. The methods further include forming a seismic image of the subsurface region of interest using the updated seismic velocity 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 provides a MultiResUNet neural network intelligent speed modeling method and device based on physical constraints. The method comprises the following steps: constructing a MultiResUNet network model; designing different types of seismic velocity models, and performing forward modeling on the velocity models to obtain a corresponding data set; the data set is preprocessed, the preprocessing comprises normalization and resampling, and the data set is divided into a training set and a test set according to a preset proportion; carrying out training optimization on the network by taking the loss of the seismic record obtained by forward modeling of the network prediction speed model and the original seismic record as physical constraints; and testing the trained network model by using the test set. According to the method, the problem that the network precision of a conventional U-Net network is reduced due to a semantic gap between an encoder and a decoder is solved, so that higher prediction precision is obtained, high-precision seismic velocity modeling is successfully realized, efficient processing of seismic data can be performed, and effective guidance and help are provided for oil-gas exploration.

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 provides a speed model correction method based on well drilling constraint. The speed model correction method based on well drilling constraint comprises the following steps: step 1, constructing an initial speed model; step 2, establishing an interval velocity model through velocity spectrum data; 3, establishing a well calibration time domain model; 4, speed inversion of model constraint is carried out; 5, speed conversion is carried out, and the well calibration average speed is calculated; and step 6, time-depth conversion is carried out through the average velocity after well calibration, and an inversion interval velocity body, a seismic attribute body and horizon data of a depth domain are obtained. According to the velocity model correction method based on the drilling constraint, a three-dimensional time domain velocity model is converted into a three-dimensional depth domain velocity model, and the seismic velocity field and logging are combined, so that time-depth conversion with relatively high transverse and longitudinal accuracy is realized.

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 well bore structure optimization method based on three-dimensional formation pressure, and belongs to the technical field of oil and gas well engineering. The method comprises the following steps: collecting data required by modeling in a work area, and storing the data into a geological modeling database; the required data comprises single well data and seismic data; based on the data in the geological modeling database, establishing a work area three-dimensional geological model; on the basis of the three-dimensional geological model, completing a three-dimensional geological attribute model of each stratum of the work area; and based on the three-dimensional geological model and the three-dimensional geological attribute model, the well bore structure optimization design is completed by taking the pressure system distribution condition as an evaluation and selection method. Through seismic velocity data processing, seismic stratification information of each layer section is obtained, a three-dimensional geological model of a work area is established, three-dimensional attribute modeling calculation is completed, and three-dimensional attribute modeling of the work area is realized; and finally, the three-dimensional three-pressure calculation profile result of the single well is extracted, reasonable design of the well bore structure can be achieved, and the stability and safety of a well bore are improved.

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 invention relates to a VTI medium anisotropy parameter inversion method based on a two-step method, and belongs to the field of seismic velocity modeling, gradients of three model parameters Vp, Vs and rho are obtained through inversion, the gradients are optimized through a conjugate gradient optimization algorithm and then substituted into iterative inversion, and the three model parameters Vp, Vs and rho are obtained. The three model parameters of Vp, Vs and rho serve as real model parameters, only epsilon and delta are inversed in the inversion process to obtain gradients of the epsilon and the delta, then the gradients are optimized through an Adam gradient algorithm and then substituted into iterative inversion till the precision requirement is met, and finally the two anisotropic parameters of epsilon and delta are obtained. According to the method, the crosstalk effect of multi-parameter simultaneous inversion is reduced, different gradient optimization algorithms are selected according to the characteristics of the parameters in the inversion process, the gradient optimization precision is improved in a targeted mode, and the effect of VTI medium anisotropy parameter full-waveform inversion is remarkably improved.

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 present invention relates to a seismic layer velocity field correction method, belonging to the technical field of oil and gas field exploration. The method first calculates a normalized coherence body of seismic data, then determines a region to be corrected by a coherence value, and finally corrects the seismic layer velocity field in a cluttered region by using a multi-control correction equation, thereby solving the problem of inaccurate velocity field modeling in cluttered data regions such as low signal-to-noise ratio regions and fracture regions.

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.

The present invention discloses a method and device for constructing a seismic velocity body. The method comprises: selecting the time-depth relationship of the drilled wells in the target area that meets the requirements based on the synthetic seismic records and actual seismic records of the drilled wells in the target area; determining the velocity data of the drilled wells according to the time-depth relationship of the drilled wells; extracting the preferred seismic attributes related to the target layer based on the velocity data, the seismic interpretation layer of the target layer and the seismic data body; fitting the layer velocity body of the target layer based on the time-depth relationship of the drilled wells, the seismic interpretation layer and the seismic attributes related to the target layer and the pre-established time structure model of the target layer; establishing the average velocity body from the surface to the target layer based on the layer velocity body of the target layer to obtain the seismic velocity body of the target area. It can effectively guide the preferred exploration target of the target area, improve the success rate of the deployment of exploration wells, optimize the drilling footage, save drilling costs, and improve the exploration efficiency of the early exploration blocks.

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.

The invention discloses a seismic velocity modeling optimization method and device, computing equipment and a storage medium, and the method comprises the steps: obtaining three-dimensional seismic time domain interval velocity data based on three-dimensional seismic depth domain migration velocity data; performing learning training on the three-dimensional seismic time domain interval velocity data by using the three-dimensional seismic time domain result profile data based on a neural network model to obtain a training model; and inputting the three-dimensional earthquake time domain result profile data into the training model to obtain optimized three-dimensional earthquake time domain interval velocity data. According to the seismic velocity modeling optimization method, the existing velocity model can be optimized, the precision of the seismic velocity model is improved, human intervention and calculation time can be reduced, and the working efficiency is improved.

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.