A process for seismic imaging of a subterranean geological formation includes generating a source wavefield from seismic data representing a subterranean formation. The process includes generating a receiver wavefield from the seismic data representing the subterranean formation. The process includes decomposing the source wavefield to extract a source depth component and decomposing the receiver wavefield to extract a receiver depth component. The process includes applying a transform to each of the source depth component and the receiver depth component. The process includes combining the source depth component and the receiver depth component to generate an imaging condition. The process includes extracting a low-frequency term from the imaging condition to generate a wave-path tracking data, generating a wave path from the wave-path tracking data, and rendering a seismic image of at least a portion of the subterranean geological formation from the generated wave path.

A method to process vertical seismic profile (VSP) data includes receiving VSP data, migrating the VSP data output using an initial velocity model to produce migrated depth values associated with the respective receivers, sorting and collecting the migrated depth values corresponding to each receiver to produce a migrated common receiver gather (CRG) associated with each receiver, stacking the migrated depth values of the CRGs corresponding to respective fixed lateral positions in an image volume to produce a common image gather (CIG) associated with each lateral position, and generating a semblance panel having the stacked depth migration values plotted as contours on a first axis for velocity ratio (vr), which is based on migration velocity and true velocity) and a second axis for true depth (Zt). The method further includes updating the initial velocity model based on a plurality of data points selected from the semblance panel to provide an updated velocity model.

Prediction and subtraction of multiple diffractions may include transforming previously acquired seismic data from a time-space domain to a transformed domain using a dictionary of compressive basis functions and separating, within the transformed previously acquired seismic data, a first portion and a second portion of the transformed previously acquired seismic data. Prediction and subtraction of multiple diffractions may also include predicting a plurality of multiple diffractions based on the separated first and second portions and adaptively subtracting the predicted multiple diffractions from the previously acquired seismic data. Prediction and subtraction of multiple diffractions may also include inverse transforming a particular seismic data set from the transformed domain to the time-space domain.

Methods and systems, including computer programs encoded on a computer storage medium can be used for identifying primary-wave (P-wave) and secondary-wave (S-wave) characteristics of an underground formation by separating P-wave and S-wave modes of seismic data generated by applying a seismic source to a subterranean region of a geological area. Particle motion vectors of a P-wave are parallel to a propagation vector of the P-wave, whereas particle motion vectors of an S-wave are perpendicular to a propagation vector of the S-wave. The parallel and perpendicular relationship between the motion and propagation vectors of the respective P- and S-waves provide a basis for separating P- and S-wave components from a wavefield. The separation methodology extracts P-wave components and S-wave components from the wavefield based on an estimated angle between propagation vectors and wave motion vectors for the wavefield.

Methods, systems, devices, and products for formation evaluation. Methods include automatically characterizing an acoustic reflective boundary in the earth formation by: generating a plurality of multipole acoustic signals within the borehole; generating acoustic wave data at at least one acoustic receiver on the logging tool in response to a plurality of acoustic reflections of acoustic waves from a corresponding plurality of reflection points along the boundary responsive to the multipole acoustic signals; estimating from the acoustic wave data a location in the formation for each reflection point of the plurality of reflection points, which may include performing coherence processing on at least a portion of the acoustic wave data to generate a coherence map; and identifying acoustic reflections from the coherence map; and using the location in the formation for each reflection point to estimate at least one property of the acoustic reflective boundary.

A device and method to compress and store ultrasound data from an ultrasound logging device. The logging device is deployed in a well or pipe to be logged with one or more ultrasound transducers, preferably an array of transducers. On-board the device, are processors and memory units for convolving the ultrasound data with a wavelet transformation to generate wavelet coefficients and then compress the wavelet coefficients to generate compressed wavelet coefficients. The compressed wavelet coefficients are stored on the memory units and transferred to a remote computer once the device leaves the well or pipe.

A computer implemented method for denoising a set of seismic datasets, specifically belonging to different 3D subsets of a 4D survey the method including: (a) receiving a baseline and a monitor seismic dataset which were acquired by surveying over the same subsurface formation over different periods of time; (b) cross-equalizing the monitor seismic dataset to match to the baseline seismic dataset in terms of amplitude, frequency, phase and timing of events; (c) computing an initial 4D difference between the monitor and baseline seismic datasets; (d) formulating a common noise template from the initial 4D difference; (e) de-noising the baseline and monitor seismic datasets, independently, using the common noise template in a curvelet domain; (f) updating the initial 4D difference to form an updated 4D difference, which reflects de-noised baseline and monitor datasets from step (e); and iterating the steps (d) through (F) until the updated 4D difference satisfies a predetermined criteria.

One or more first sensors may be configured to sense seismic signals and one or more second sensors may be configured to sense electromagnetic crosstalk signals. The second sensors are not responsive to the seismic signals. The data from the first and second sensors may be recorded as first data and second data, respectively. The first data may be modified based on the second data to remove the electromagnetic crosstalk noise form the seismic data.

A method and system for producing a Quasi-Static Stoneley Slowness log. The method for producing a Quasi-Static Stoneley Slowness log may comprise recording a pressure wave at a receiver; determining a slowness-frequency range with an information handling system from the pressure wave, processing a frequency-domain semblance, extracting a Stoneley Dispersion, minimizing a misfit between theoretical and the Stoneley Dispersion, and identifying Quasi-Static Stoneley slowness from the Stoneley Dispersion. The well measurement system for producing an Quasi-Static Stoneley Slowness log and shear slowness anisotropy may comprise a downhole tool, a vehicle, and an information handling system. Wherein the information handling system may be operable to record a pressure wave at a receiver, determine a slowness-frequency range with an information handling system from the pressure wave, process a frequency-domain semblance, extract a Stoneley Dispersion; minimize a misfit between theoretical and the Stoneley Dispersion; and identify Quasi-Static Stoneley slowness from the Stoneley Dispersion.

A method for deblending seismic signals includes entering as input to a computer recorded signals comprising seismic energy from a plurality of actuations of one or more seismic energy sources. A model of deblended seismic data and a blending matrix are initialized. A blending matrix inversion is performed using the initialized model. The inversion includes using a scaled objective function. The inversion is constrained by a thresholding operator. The thresholding operator is arranged to recover coefficients of the model of the deblended seismic data that are substantially nonzero, against a Gaussian white noise background. The thresholded model is projected into data space. Performing the blending matrix inversion is repeated if a data residual exceeds a selected threshold and the inversion is terminated if the data residual is below the selected threshold. At least one of storing and displaying an output of the blending matrix inversion is performed when the blending matrix inversion is terminated.