A method for determining a shear slowness of a subterranean formation includes receiving waveforms data acquired by receivers in an acoustic measurement tool in response to energy emitted by at least one dipole source. The waveforms are processed to extract a formation flexural acoustic mode and a tool flexural acoustic mode. The processing includes transforming the time domain waveforms to frequency domain waveforms, processing the frequency domain waveforms with a Capon algorithm to compute a two-dimensional spectrum over a chosen range of group slowness and phase slowness values; and processing the two-dimensional spectrum to extract the multi-mode slowness dispersion. The method further includes selecting a plurality of slowness-frequency pairs from the formation flexural mode of the extracted multi-mode dispersion wherein each slowness-frequency pair comprises a slowness value at a corresponding frequency and processing the selected slowness frequency pairs to compute the shear slowness of the subterranean formation.

A method and a system for implementing the method are disclosed wherein the seismic input data and land acquisition input data may be obtained from a non-flat surface, sometimes mild or foothill topography as well as the shot and receiver lines might not necessarily be straight, and often curve to avoid obstacles on the land surface. In particular, the method and system disclosed, decomposes the cross-spread data into sparse common spread beams, then maps those sparse beams into common-spread depth domain, in order to finally stack them to construct the subsurface depth images. The common spread beam migration and processing have higher signal to noise ratio, as well as faster turn-around processing time, for the cross-spread land acquisition over the common-shot or common offset beam migration/processing. The common spread beam migration method and system disclosed, will eventually help illuminate and interpret the hydro-carbonate targets for the seismic processing.

Apparatus, methods, and systems for determining body wave slowness values for a target formation zone. A method includes selecting a target axial resolution based on the size of a receiver array, obtaining a plurality of waveform data sets corresponding to a target formation zone and each acquired at a different shot position, reconstructing the plurality of waveform data sets to generate a plurality of subarray data sets corresponding to the target formation zone, determining a slowness value for each subarray data set and determining a slowness versus offset value for each subarray data set. The method may also include generating a borehole model having at least one alteration formation zone and a virgin formation zone and generating a slowness versus offset model based at least in part on the borehole model. The method may also include determining a radial depth of the alteration formation zone.

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.

Waves from cement bond logging with a sonic logging-while-drilling tool (LWD-CBL) are often contaminated with tool waves and may yield biased CBL amplitudes. The disclosed LWD-CBL wave processing corrects the first echo amplitudes of LWD-CBL before calculating the BI. The LWD-CBL wave processing calculates a tool wave amplitude and a phase angle difference as the difference of the phases between the tool waves and casing waves. The tool waves are then used to correct the LWD-CBL casing wave amplitude and remove errors introduced from tool waves. In conjunction with the sets of operations described, the LWD-CBL wave processing also include array preprocessing operations. Array preprocessing may employ variation of bandpass filtering and frequency-wavenumber (F-K) filtering operations to suppress tool wave.

Embodiments of determining a vertically transverse isotropy (VTI) anisotropy along a horizontal section of a wellbore drilled into a formation are provided. One embodiment comprises determining elastic constants C11, C44, and C66 of the horizontal section and determining a vertical compressional slowness of the horizontal section corresponding to an elastic constant C33 of the horizontal section using a model with a condition. The model is built using second sonic log data and second density log data of the vertical wellbore. The condition is that the shear slowness (DTS) of the vertical wellbore is equal to the vertically polarized shear slowness (DTSV) of the horizontal section. The embodiment further comprises determining a VTI anisotropy along the horizontal section using the elastic constants C11, C44, C66, and C33 of the horizontal section.

A method and system for measuring a compressional and a shear slowness. The method may comprise disposing a downhole tool into a wellbore. The downhole tool may comprise a transmitter, wherein the transmitter is a monopole, and a receiver, wherein the receiver is a monopole receiver. The method may further comprise broadcasting the sonic waveform into the formation penetrated by the wellbore, recording a reflected wave on one or more receivers, wherein the reflected wave is a compressional wave or a shear wave, processing the reflected wave into at least one measurement, and applying a validation scheme to the at least one measurement. The system may be a downhole tool comprising a transmitter configured to transmit a sonic waveform into a formation, wherein the transmitter is a monopole, and a receiver configured to record a reflected wave, wherein the receiver is a monopole receiver.

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.

A method for identifying a leak for dynamic logging may comprise estimating a Stoneley wave slowness, separating a Stoneley wave into an up-going Stoneley wave and a down-going Stoneley wave, estimating an amplitude of the up-going Stoneley wave and the down-going Stoneley wave, identifying a difference between the amplitude of the up-going Stoneley wave and the down-going Stoneley wave, forming an amplitude summation curve or an amplitude difference curve, and identifying a location of the leak.

Methods to estimate formation shear wave slowness from multi-firings of different types of acoustic sources and multi-mode dispersion estimation systems are presented. The method includes obtaining waveform data of waves traversing through a downhole formation, where the waves are generated from multi-firings of different types of acoustic sources. The method also includes performing a multimode dispersion analysis of the waveform data for each firing of the multi-firings, and removing one or more tool waves generated from the multi-firings. The method further includes determining a formation type of the formation the waves traverse based properties of the waves and determining an initial shear wave slowness estimate of the waves. The method further includes generating a modeling of the waves, and reducing a mismatch between the modeling of the waves and a slowness dispersion of the waves to improve the modeling of the waves.