A method, system and non-transitory computer-readable medium for forming a seismic image of a geological structure are provided. After obtaining seismic wave data including a plurality of seismic wave traces at a first region of the geological structure, a predicted time dispersion error of an actual time dispersion error that results from a use of a finite difference approximation in calculating predicted seismic wave data at a second region of the geological structure as if a seismic wave propagates from the first region to the second region of the geological structure, is calculated. A corrected predicted seismic wave data at the second region of the geological structure is calculated by applying the finite difference approximation to the seismic wave data at the first region of the geological structure compensated with the predicted time dispersion error. A seismic image of the second region of the geological structure is generated using the corrected predicted seismic wave data, such that the actual time dispersion error is negated by the predicted time dispersion error.

To generate dispersion curves for acoustic waves in a radially layered system, a matrix M containing solutions to the wave equation subject to the boundary conditions of the system is constructed. The reciprocal condition number (RCN) of the matrix M is determined as a function of acoustic wave frequency and slowness. The local minima of the RCN in the frequency-slowness plane produces the dispersion curves corresponding to allowable acoustic modes in the system. A sensitivity analysis which identifies the dispersion curves dependent on a selected parameter. The dispersion curves independent of the perturbed parameters are eliminated by perturbing the modeling parameters and generating the RCN of the perturbed matrix M and then subtracting the RCN values of the unperturbed matrix M, leaving the dispersion curves that exhibit dependence on the selected parameter.

A method is presented wherein inversion for formation anisotropic constants is achieved using borehole sonic data.

Systems and methods for modeling dispersion curves are disclosed. The method includes obtaining an acoustic dataset along a well that accesses a hydrocarbon reservoir. The method further includes determining a set of depth windows along the well and determining a first subset of dispersion curves for a first subset of depth windows using a dispersion model. The method still further includes initializing a second subset of dispersion curves for a second subset of depth windows using a nearest neighbor search of the first subset of dispersion curves. The method still further includes determining slowness-frequency pairs for the second subset of depth windows using the acoustic dataset and updating the second subset of dispersion curves using a recursive scanning method. The method still further includes characterizing rock properties near the well based, at least in part, on the first subset of dispersion curves and the second subset of dispersion curves.

Device and method for calculating a set of surface wave dispersion curves. The method includes receiving seismic data recorded with seismic sensors over an area to be surveyed; selecting region units that cover the area to be surveyed; gathering traces for the region units; processing in a computing device the traces to obtain a set of candidate measurements for each region unit; teaching a decision algorithm based on a first subset of the set of candidate measurements; and calculating the set of surface wave dispersion curves by running the decision algorithm on a second subset of the set of candidate measurements.

To generate dispersion curves for acoustic waves in a radially layered system, a matrix M containing solutions to the wave equation subject to the boundary conditions of the system is constructed. The reciprocal condition number (RCN) of the matrix M is determined as a function of acoustic wave frequency and slowness. The local minima of the RCN in the frequency-slowness plane produces the dispersion curves corresponding to allowable acoustic modes in the system. A sensitivity analysis which identifies the dispersion curves dependent on a selected parameter. The dispersion curves independent of the perturbed parameters are eliminated by perturbing the modeling parameters and generating the RCN of the perturbed matrix M and then subtracting the RCN values of the unperturbed matrix M, leaving the dispersion curves that exhibit dependence on the selected parameter.

A method for generating a seismic image representing a subsurface includes receiving seismic data for the subsurface formation, including receiver wavelet data and source wavelet data. Source wavefield data are generated based on a forward modeling of the source wavelet data. Receiver wavefield data are generated that compensate for distortions in the seismic data by: applying a dispersion-only model to the receiver wavelet data to generate a first reconstructed back-propagated receiver wavefield portion, applying a dissipation-only model to the receiver wavelet data to generate a second reconstructed back-propagated receiver wavefield portion, and combining the first back-propagated receiver wavefield portion and the second back-propagated receiver wavefield portion into the receiver wavefield data. The method includes applying an imaging condition to the receiver wavefield data and the source wavefield data and generating, based on applying the imaging condition, visco-acoustic reverse time migration (VARTM) result data.

In acoustic well logging, for each inversion depths of a well at which logging of data occurs, acoustic log signals representative of waveforms received at acoustic receivers are processed in a frequency domain to derive field dispersion curve(s). A neural net is operated to generate formation shear slowness value(s) from the curve(s), and resulting signal(s) indicative of shear slowness values are saved, transmitted, plotted, printed or processed. An apparatus for carrying out the method includes a logging tool having at least one activatable acoustic wave source; spaced and acoustically isolated therefrom in the logging tool an array of acoustic detectors that on the detection of acoustic wave energy generate electrical or electronic log signal(s) characteristic of acoustic energy waves detected by the acoustic detector(s); and at least one processing device associated with or forming part of the logging tool for processing the log signal(s).

Acoustic waves are obtained from an acoustic logging tool within a borehole passing through a formation. Signal properties in a time domain, frequency domain, or both are determined based on the obtained acoustic waves. A machine learning analysis is used to determine formation slowness based on the determined signal properties and a downhole operational parameter is adjusted based on the determined formation slowness.

A method for generating a seismic image representing a subsurface includes receiving seismic data for the subsurface formation, including receiver wavelet data and source wavelet data. Source wavefield data are generated based on a forward modeling of the source wavelet data. Receiver wavefield data are generated that compensate for distortions in the seismic data by: applying a dispersion-only model to the receiver wavelet data to generate a first reconstructed back-propagated receiver wavefield portion, applying a dissipation-only model to the receiver wavelet data to generate a second reconstructed back-propagated receiver wavefield portion, and combining the first back-propagated receiver wavefield portion and the second back-propagated receiver wavefield portion into the receiver wavefield data. The method includes applying an imaging condition to the receiver wavefield data and the source wavefield data and generating, based on applying the imaging condition, visco-acoustic reverse time migration (VARTM) result data.