46 results about "Seismic velocity" patented technology

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 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.

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.

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 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 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.

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 present application relates to the technical field of geophysical exploration and marine engineering geological survey, and provides a time-depth conversion method and system for offshore seismic acquisition imaging. The time-depth conversion method for offshore seismic acquisition imaging comprises synchronously acquiring seismic data of a submarine tunnel line position and preprocessing the seismic data; based on the preprocessed seismic data, combining regional geological priori and drilling data, a multi-scale velocity field combining a macroscopic layered velocity model and a microscopic lateral variable velocity model is constructed; based on the multi-scale velocity field, a time domain seismic profile is converted into a seismic depth domain profile; transient electromagnetic apparent resistivity data are introduced to calculate corrected seismic velocity and thereby re-perform time-depth conversion to obtain seismic depth domain profile imaging; combining a complex spectrum correction method and a drilling data error verification method, the seismic depth domain profile imaging is optimized. The present application can realize efficient and accurate time-depth conversion of offshore complex geological structures and adverse geological bodies.

This invention relates to the field of geophysical exploration and discloses a method, apparatus, medium, and computer program product for extracting geological structural features. The method includes: generating a true velocity model of the target area based on geological data of the target area; wherein the geological data includes geological structural data and well logging interpretation data; performing low-pass filtering on the true velocity model to obtain a low-resolution velocity model, and using the low-resolution velocity model and the true velocity model as training sample pairs corresponding to the geological data; generating a sample training set based on the training sample pairs corresponding to multiple sets of geological data of the target area, and training a preset network model based on the sample training set to obtain a trained network model; and extracting geological structural features of the area to be processed based on the low-resolution velocity model of the area to be processed through the trained network model. This improves the efficiency of geological structural feature extraction, provides structural feature constraint information for seismic velocity inversion, and improves the spatial resolution of seismic inversion.

The present application relates to the field of oil and gas seismic exploration technology, more particularly, it relates to a high-precision velocity modeling method for deep water deep reservoirs, the present application is mainly aimed at the deep water deep reservoir depth prediction in the area with few wells, after the quality evaluation of the seismic data of the target area, the present application optimizes part of the seismic data as the basic data for research, on the basis of the fine construction of the geological model, the selection of regional velocity marker layer, well-seismic calibration, velocity main control factor analysis and seismic velocity body optimization, the high-precision velocity modeling fine research is carried out, so as to realize the accurate prediction of the reservoir depth in the deep water deep area with few wells or no well area. The present application strictly improves the fine degree of each step of modeling, the key is to add the change of reservoir physical properties as the main control factor of regional velocity into the seismic velocity body optimization work, compared with the original seismic velocity body, more velocity details matched with the logging data are added, which is beneficial to improve the prediction accuracy of the final velocity model.

A method (600) and a system (1000) for generating an adaptive migration taper for a pre-stack seismic dataset are disclosed. The method (600) includes obtaining the pre-stack seismic dataset (602) and a seismic velocity model of a subterranean region (604). The method (600) also includes generating the adaptive migration taper based, at least in part, on the pre-stack seismic dataset (606), and forming a migrated seismic image using a migration function, the seismic velocity model, the pre-stack seismic dataset, and the adaptive migration taper (608). The method (600) further includes determining a location of a hydrocarbon reservoir based, at least in part, on the migrated seismic image (610).

This invention provides a system and method integrating perimeter security and underground monitoring, relating to the fields of engineering safety monitoring and intelligent security technology. The system includes: a data acquisition module, a time synchronization module, a data processing module, and an alarm module. The data acquisition module includes multiple sensors deployed at different locations within the monitoring area, which collect vibration data at their respective locations. The time synchronization module synchronizes the vibration data transmitted by each sensor. The data processing module detects perimeter intrusion events and calculates relative changes in seismic velocity based on the vibration data. The data processing module also sends a corresponding alarm command to the alarm module when a perimeter intrusion event is detected or when the relative change in seismic velocity meets preset conditions. The alarm module executes corresponding alarm actions based on the alarm command. This invention can simultaneously and continuously monitor surface activity and underground mechanical conditions with reliable monitoring performance.

The invention discloses an intelligent prediction method for underwater blasting seismic velocity and dominant frequency based on deep learning, and relates to the technical field of machine learning, and the method comprises the steps: obtaining the multi-source data of a plurality of historical underwater blasting modes, verifying the multi-source factor feature vector contribution degree of local seismic velocity-dominant frequency of the plurality of underwater blasting modes, and obtaining the multi-source factor feature vector contribution degree of the local seismic velocity-dominant frequency of the plurality of underwater blasting modes; generating a peak shock velocity-dominant frequency spatial distribution diagram of each underwater blasting mode; according to the sensitive frequency range of the protection target type for the underwater blasting shock velocity-dominant frequency, evaluating the tolerable capacity vector of the protection target type of the to-be-blasted area; and an underwater blasting vibration velocity-dominant frequency prediction model is established, the multi-source data of the to-be-blasted area and the tolerable capacity vector of the protection target type of the to-be-blasted area serve as input, and an optimal underwater blasting mode meeting the protection target type of the to-be-blasted area is generated. The underwater blasting vibration velocity and the dominant frequency are accurately predicted, and the blasting mode decision-making benefit of the to-be-blasted area is maximized on the premise that safety is guaranteed.

The application discloses a VTI medium ray tracing method and device, electronic equipment and medium. The method can comprise: for a VTI medium, solving a Christoffel equation according to Thomsen parameters by an eigenvalue method to obtain an eikonal equation of a longitudinal wave; solving the eikonal equation of the longitudinal wave to obtain a kinematic ray tracing equation of the VTI medium; and solving the kinematic ray tracing equation by a Runge-Kutta method to realize ray tracing of the VTI medium. The application can establish the travel time required by depth domain migration imaging of the underground medium by ray tracing of the VTI medium according to the VTI medium Thomsen parameters obtained in actual seismic velocity modeling, thereby improving the efficiency and effect of depth domain migration imaging of the VTI medium.

The present application relates to a kind of microseismic velocity inversion methods of deep learning and wave equation combined drive, underground model is dispersed, and setting detector;Establish underground velocity model, utilize finite difference algorithm to calculate the waveform information of random seismic source at each detector position;Waveform record, velocity model and seismic source coordinate are batched into Unet neural network;Pure data driven calculation obtains preliminary velocity structure, physical drive calculation, uses finite difference to solve wave equation, obtains seismic record;Loss function is composed of two parts of velocity error and waveform error;The structural similarity between the predicted velocity and the real velocity is calculated, and the average value is set as the dynamic weight of physical drive loss value;The mapping relationship between waveform feature and velocity model is extracted using neural network, and the underground velocity structure is inverted to predict.The present application realizes artificial intelligence real-time prediction microseismic velocity model, and the precision of inversion velocity model is high, and the interpretability is strong.

The application discloses a joint inversion method of seismic and electromagnetic based on bidirectional cross gradient constraint, and first establishes three initial models of physical fields; takes a seismic grid as a main grid, and maps respective models of direct current method and transient electromagnetic method to the main network through interpolation; constructs a joint inversion objective function containing data fitting terms of three physical fields, model constraint terms and cross gradient constraint terms between the three physical fields; decomposes the joint inversion objective function into three mutually coupled inversion objective functions, adopts a collaborative alternating iteration strategy, and iteratively updates a seismic velocity model, a direct current method resistivity model and a transient electromagnetic method resistivity model, and the cross gradient constraint derived from the iteration result of the other two models is applied to each iteration update of any model, so as to forcibly realize the structural consistency between the models; and outputs a geological structure model fusing three field information, thereby effectively improving the detection precision and interpretation reliability of the geological structure under complex geological conditions.

The application discloses a method for analyzing uncertainty of structural trap area and related equipment, relates to the field of geophysical exploration, and mainly aims to solve the problem that the influence of well control radius on velocity field correction is not considered when correction is performed on a seismic velocity field at present. The method comprises the following steps: determining a structural trap area and a well control radius range; establishing a well control radius and trap area relationship curve based on the structural trap area and the well control radius range; and determining the uncertainty of the trap area based on the well control radius and trap area relationship curve. The application is used in the process of analyzing the uncertainty of the structural trap area.

The application discloses a wavelet base function selection method and device for identifying seismic velocity pulse, and the method comprises the following steps: performing integral calculation on seismic record data, and calculating an energy concentrated interval of an original velocity time history; iteratively analyzing the original velocity time history by using different wavelet base functions in sequence, and obtaining time history distribution data and the energy concentrated interval corresponding to each wavelet base function; performing down-sampling on the energy concentrated interval corresponding to each wavelet base function, and obtaining the down-sampled interval of each wavelet base function; calculating a first correlation coefficient of the down-sampled interval of each wavelet base function and the energy concentrated interval of the original velocity time history; calculating a second correlation coefficient of the velocity pulse time history identified by each wavelet base function and the original velocity time history; and selecting the wavelet base function by using the size order of the first correlation coefficient and the second correlation coefficient. The application can improve the fidelity of velocity pulse identification.

The invention relates to the technical field of seismic velocity modeling, and particularly discloses a full waveform inversion method and device based on matched filtering approximate Hessian, and the method comprises the steps: constructing a filter, and solving an approximate Hessian operator through the filter; a filter is matched through Gabor; a filter matched with Gabor acts on inversion, and a matched filtering full-waveform inversion iteration solution formula is established; and performing full-waveform inversion based on an iterative solution formula to obtain a final inversion result. According to the method, the approximate Hessian matrix is solved through matched filtering, observation seismic records and simulation seismic records are enabled to be well matched, an amplitude adjustment effect is achieved, modeling truncation illusion generated when conventional full-waveform inversion is limited and seismic data are lost can be well solved, false structures are effectively prevented from being generated in the model, and the method is suitable for large-scale popularization and application. And the precision of full-waveform inversion modeling and the iterative convergence efficiency are improved.

The application discloses a method and system for identifying carbonate reservoirs in a well-free work area, comprising the following steps: determining a target interval according to well logging and seismic data of a conjugate basin of a basin where the well-free work area is located; establishing a model for representing the relationship between acoustic velocity and density according to acoustic velocity and density of a well work area in the conjugate basin; obtaining a layer velocity body according to the depth domain seismic velocity of the well-free work area; establishing a pseudo-well density curve and a density body of the well-free work area by using the model for representing the relationship between acoustic velocity and density, and based on this, performing wave impedance inversion calculation of the well-free work area by using the layer velocity body; and identifying favorable carbonate reservoirs in the target interval based on the wave impedance body of the well-free work area.

Systems and methods for creating a plurality of dip-constrained pseudo-angle common image gathers (DPACIGs) to pick a residual moveout are disclosed. The method includes obtaining a plurality of plane-wave CIGs and an initial seismic velocity model, stacking the plane-wave CIGs to estimate a structural dip and applying a vector median filter (VMF) to attenuate a dip noise contained in the structural dip. The method also includes smoothing the plurality of plane-wave CIGs by applying a structural oriented smoothing (SOS) to create a plurality of dip-constrained plane-wave CIGs and applying a non-linear transform to the plurality of dip-constrained plane-wave CIGs to create a plurality of DPACIGs. The method further includes performing a migration velocity analysis (MVA) on the DPACIGs to pick a residual moveout, determining an updated seismic velocity model based on the residual moveout and creating a seismic image using the updated seismic velocity model and the seismic dataset.

This invention provides a method for converting resistivity logging to sonic velocity logging. Based on a defined rock physics model, it utilizes the relationships and patterns between different physical parameters to establish an analytical expression between rock porosity, resistivity, and velocity, obtaining a discrete mapping relationship between resistivity and P-wave velocity. By continuously optimizing the rock physics parameters, it obtains the mapping curve between resistivity and P-wave velocity, avoiding underfitting caused by cross-sectional analysis and linear fitting of rock physics parameters alone. This reliably achieves an effective conversion from resistivity logging to sonic velocity logging. This invention can support pre-stack depth migration velocity updates based on logging information from drilled sections, predict seismic velocities ahead of the drill bit, and enable more accurate seismic imaging and drilling decisions.

The invention relates to the technical field of seismic data processing, and particularly discloses a well-to-seismic joint modeling method and device suitable for wave equation numerical simulation, and the method comprises the steps: carrying out the extrapolation of well logging speed along horizon data, and forming depth domain three-dimensional speed body data; determining a depth interface fused by the logging speed and the seismic data; establishing a fusion coefficient at the depth interface along the depth direction on the basis of a Hanning window function; and based on the fusion coefficient, carrying out speed fusion to obtain a final fusion speed. According to the method, the velocity model capable of simulating underground geology and geophysical characteristics is established through well-seismic combination, the travel time characteristic of the seismic velocity and the stratum change characteristic of the logging velocity are fully utilized, the velocity model capable of meeting the wave equation numerical simulation requirement is formed, and support is provided for studying the propagation mechanism of some special seismic waves.

The invention discloses a method and system for identifying a carbonatite reservoir in a well-free work area, and the method comprises the steps: determining a target interval according to the logging and seismic data of a conjugate basin of a basin where the well-free work area is located; according to the sound wave velocity and density of the well work area in the conjugate basin, establishing a model representing the relationship between the sound wave velocity and the density; obtaining an interval velocity body according to the depth domain seismic velocity of the well-free work area; establishing a pseudo-well density curve and a density body of the well-free work area by using the sound wave velocity and density relation model, and performing wave impedance inversion calculation of the well-free work area by using the interval velocity body based on the pseudo-well density curve and the density body; and on the basis of the wave resistance body of the well-free work area, a carbonate rock favorable reservoir in the target interval is identified.

Techniques to allow for increases in the accuracy of the determination of properties of a formation (e.g., a formation's fluid content, porosity, density, etc.) or seismic velocity, shear wave information, etc. are disclosed herein. The techniques include generating initial input data comprising based at least in part on initial seismic data, modeling the initial input data to generate synthetic seismic data based on different combinations of the initial input data, iteratively updating a value of each particle of a set of particles utilizing the synthetic seismic data to generate a final set of particles and outputting the final set of particles as a target distribution.