A method of predicting pore pressure based on seismic data can include obtaining seismic inversion data based in part on seismic data collected from a formation. The method also includes calculating a pore-pressure transform, wherein the pore-pressure transform comprises parameters derived using measured pore pressure data, upscaled sonic logs, and density logs, wherein the pore-pressure transform comprises an objective function to reduce unphysical variations in predicted pore pressure corresponding to depth. Additionally, the method can include adjusting the pore-pressure transform for sampling bias caused by pore pressure measurements being restricted to a plurality of lithologies by accounting for a difference between upscaled seismic velocities and average sonic velocities within each of the lithologies. Furthermore, the method can include generating pore pressure prediction values based on the pore-pressure transform for the lithologies and the seismic inversion data, and modifying a seismic model based on the generated pore pressure prediction values.

A method for fluid identification (water, oil, gas or CO.sub.2) and saturation estimation in subsurface rock formations using the prestack inverted Seismic by calculating the target fluid saturation (S.sub.fl) (114) in a reservoir given the magnitude obtained from the P- to S-wave velocity ratio (Vp/Vs) (103), and acoustic impedance (AI) (102) extracted from the seismic data inversion, comprising the following steps: a) obtaining wireline log data within a zone of interest in a nearby well (101) and determining the suitable cementation and mineralogy factors by calibrating the background water-bearing sand trend with the reference 0% (or 0 fraction) S.sub.fl curve onto the acoustic impedance-Vp/Vs ratio plane (110), b) calibrating S.sub.fl computed from the acoustic impedance-Vp/Vs ratio curves with S.sub.fl obtained from a conventional method by iterating P-wave velocity (Vp.sub.f) and density (ρ.sub.fl) of the target fluid (111), c) obtaining inverted seismic data in the form of Acoustic Impedance (AI) (102) and Vp/Vs ratio (103) cubes, and d) calculating the target fluid saturation using the calibrated rock physics model inputting the obtained parameters from model calibration (cementation factor, mineralogy factor, density and P-wave velocity of the target fluid) along with inverted Vp/Vs ratio and acoustic impedance cubes data (113), resulting in a S.sub.fl cube (114).

Methods of identifying a subterranean tunnel using digital imaging that may include: obtaining data of a propagating wavefield through a propagating volume that includes a portion of the earth's subsurface; obtaining a reference digital image of the propagating volume; selecting a holographic computational method of wavefield imaging; selecting a wavefield based on one or more parameters; calculating a sampling ratio by dividing a number of data samples in the data subset by a number of image samples in the data subset; decimating the data subset; generating a new digital image based on the selected holographic computational method of imaging, the decimated data subset, and parameters corresponding to the data subset; determining a quantitative difference measure between the reference digital image and the new digital image, and image quality; and identifying the subterranean tunnel.

A seismic source is positioned at the surface of a geologic formation and a plurality of seismic receivers is positioned in a wellbore of the geologic formation. Seismic wavefield data is obtained based on the seismic source outputting seismic energy into the wellbore and the plurality of seismic receivers receiving the seismic energy. A velocity profile is determined along the wellbore based on the seismic wavefield data. P and S wave data in a downgoing direction is separated from the seismic wavefield data based on an inversion and the velocity profile. The P and S wave data in the downgoing direction is adaptively subtracted from the seismic wavefield data to form residual wavefield data. The P and S wave data in a upgoing direction is separated from the residual wavefield data based on the inversion and an updated velocity profile. The P and S wave data in the upgoing and downgoing direction is output.

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 of seismic modeling using an elastic model, the elastic model including a grid having a grid spacing sized such that, when synthetic seismic data is generated using the elastic model, synthetic shear wave data exhibits numerical dispersion, the method including: generating generated synthetic seismic data using the elastic model, wherein the generated synthetic seismic data includes synthetic compression wave data and synthetic shear wave data, and modifying the generated synthetic seismic data to produce modified synthetic seismic data by attenuating at least some of the synthetic shear wave data in order to attenuate at least some of the numerically dispersive data.

Disclosed are a method and apparatus for estimating S-wave velocities by learning well logs, whereby the method includes a model formation step of forming an S-wave estimation model to output S-wave velocities corresponding to measured depth when the well logs are input based on train data sets including train data having values of multiple factors included in the well logs, the values being arranged corresponding to measured depth, and label data having S-wave velocities corresponding to measured depth as answers, and an S-wave velocity estimation step of inputting unseen data having values of multiple factors included in well logs acquired from a well at which S-wave velocities are to be estimated, the values being arranged corresponding to measured depth, to the S-wave estimation model to estimate S-wave velocities corresponding to measured depth.

Methods for determination of mechanical properties of geological formations using deep learning include receiving, by a computer system, data acquired during drilling a geological formation. The computer system generates features of the data acquired during drilling. The features are indicative of mechanical properties of the geological formation. The computer system segments the features of the data acquired during drilling into sequences readable by a trained temporal convolutional network (TCN). The computer system determines the mechanical properties of the geological formation using the TCN based on the sequences obtained from the features of the data. A display device of the computer system generates a graphical representation of the mechanical properties of the geological formation.

A computer-implemented method can include the following. Seismic vibratory waves through the Earth along a selected vector path are received. An initial value is selected for a first arrival for each of the seismic vibratory waves. Initial values are determined for travel times and velocities of the seismic vibratory waves. Reversed signs of amplitudes of the seismic vibratory waves are determined and corrected. Time intervals are determined based on the initial values of the travel times. Time windowing and filtering in a frequency domain are performed. Final values are determined for first arrivals and travel times for each of the seismic vibratory waves based on the time windowing and filtering. Final values are determined for velocities of the seismic vibratory waves.

A method of realizing an shear wave propagation velocity anisotropy characterization within a display for a wellbore region including, obtaining a shear wave propagation velocity anisotropy intensity, and a shear wave propagation velocity anisotropy azimuth. A directional line segment is determined to represent the anisotropy for each of a plurality of measured depth points along the wellbore, and plotted on the display as a plurality of directional line segments to produce a 1-dimensional anisotropy characterization plot.