A method for extracting a downgoing wavelet and attenuation parameters from VSP data, comprising: performing upgoing and downgoing P-waves separation processing on VSP data to obtain downgoing P-wave data; performing a FFT on seismic data with a preset time window length starting from the P-wave first arrival time and cut from the downgoing P-wave data to obtain FFT transformed downgoing P-wave data and a multi-trace downgoing P-wave log spectrum; subtracting a downgoing wavelet log spectrum from the multi-trace downgoing P-wave log spectrum to obtain a wavelet-corrected multi-trace downgoing P-wave log spectrum; performing, based on parameters of the wavelet-corrected multi-trace downgoing P-wave log spectrum, a correction and an inverse FFT on the FFT transformed downgoing P-wave data to obtain a downgoing wavelet; and obtaining attenuation parameters based on P-wave first arrival time and the parameters of the wavelet-corrected multi-trace downgoing P-wave log spectrum. The method can extract a downgoing wavelet and attenuation parameters with high accuracy. Also provided are an apparatus for extracting a downgoing wavelet and attenuation parameters from VSP data, a computer device, and a computer-readable storage medium.

A method of obtaining a relative amplitude preserved seismic volume acquired in a time-domain for a subterranean region of interest and transforming it into a low-frequency monospectral amplitude volume. The method further determines a seismic attenuation volume from the relative amplitude preserved seismic volume acquired in the time-domain. Furthermore, the method generates a low-frequency monospectral amplitude map for a surface of interest by averaging the low-frequency monospectral amplitude volume over a depth-window around the surface of interest, and generates a seismic attenuation map for a surface of interest by averaging the seismic attenuation volume over a depth-window around the surface of interest. The method further determines an attribute map based on the seismic attenuation map and the low-frequency monospectral amplitude map for the surface of interest, and determines a presence of gas in the subterranean region of interest based on the attribute map.

This disclosure describes a system and method for generating a subsurface model representing lithological characteristics and attributes of the subsurface of a celestial body or planet. By automatically ingesting data from many sources, a machine learning system can infer information about the characteristics of regions of the subsurface and build a model representing the subsurface rock properties. In some cases, this can provide information about a region using inferred data, where no direct measurements have been taken. Remote sensing data, such as aerial or satellite imagery, gravimetric data, magnetic field data, electromagnetic data, and other information can be readily collected or is already available at scale. Lithological attributes and characteristics present in available geoscience data can be correlated with related remote sensing data using a machine learning model, which can then infer lithological attributes and characteristics for regions where remote sensing data is available, but geoscience data is not.

Processes and systems described herein are directed to imaging a subterranean formation from seismic data recorded in a marine survey with moving marine vibrators. The marine vibrators generate random sweeps with random sweep signatures. Processes and systems generate an up-going pressure wavefield from measured pressure and vertical velocity wavefield data recorded in the marine survey and obtain a downgoing vertical acceleration wavefield that records source wavefields, directivity, source ghosts, and random signatures of the random sweeps. The downgoing vertical acceleration wavefield data is deconvolved from the up-going pressure wavefield to obtain a subsurface reflectivity wavefield that is used to generate an image of the subterranean formation with reduced contamination from source wavefields, directivity, source ghosts, and random signatures of the random sweeps.

Methods and systems for identifying a pay zone in a subterranean formation can include: logging a well extending into the subterranean formation including measuring bulk density, compressional wave travel time and shear wave travel time at different depths in the subterranean formation; calculating elastic attributes including acoustic impedance and compressional velocity-shear velocity ratio at different depths in the subterranean formation; and displaying and analyzing the calculated elastic attributes to identify the low resistivity pay zones.

A method for providing a calibrated rock-physics model of a subsoil. First, a geological model of the subsoil comprising a grid made of cells, associated with a rock-physics parameter is obtained. A group of cells forming a calibration body is selected in the grid. The calibration body corresponds to a region of the subsoil having substantially homogenous rock-physics parameter values. Finally, an adjustable constant parameter in a physical equation expressing a relationship between the petro-physical parameter and a petro-elastic parameter in the calibration body is calibrated so as to reduce a mismatch between the petro-elastic parameter estimated using the physical equation and a petro-elastic parameter value determined from inverted seismic data, the calibrated physical equation providing a calibrated rock-physics model of the subsoil in the calibration body.

A method may include obtaining various well logs or various core samples regarding a geological region of interest. The method may further include determining various permeability values, various porosity values, and various dolomite volume fraction values regarding the geological region of interest using the well logs or the core samples. The dolomite volume fraction values may correspond to a percentage of dolomite in a total mineral volume. The method may further include determining, using the porosity values, various permeability thresholds corresponding to various predetermined reservoir qualities. The method may further include generating, using the permeability thresholds, the permeability values, and the dolomite volume fraction values, a reservoir model including various dolomite boundaries defining the predetermined reservoir qualities. The method may further include determining a hydrocarbon trap prediction using the reservoir model.

A method for waveform inversion, the method including receiving observed data d, wherein the observed data d is recorded with sensors and is indicative of a subsurface of the earth; calculating estimated data p, based on a model m of the subsurface; calculating, using a trained neural network, a misfit function J.sub.ML; and calculating an updated model m.sub.t+1 of the subsurface, based on an application of the misfit function J.sub.ML to the observed data d and the estimated data p.

Described embodiments generally relate to a computer-implemented method for modelling a subsurface formation. The method comprises receiving measurement data related to the subsurface formation, the measurement data comprising a plurality of data points; determining at least one rock physics model, each rock physics model relating to a rock type; assigning each data point of the measurement data to at least one initial rock class membership; fitting each determined rock physics model of the at least one rock physics model to the data points of the measurement data to produce at least one fitted rock physics model; reassigning each data point to at least one rock class based on the fitted rock physics models; determining whether a convergence criterion has been met; and responsive to the convergence criterion not being met, repeating the fitting and reassigning steps.

A method for seismic processing includes extracting, using a first machine learning model, one or more seismic features from seismic data representing a subsurface domain, receiving one or more well logs representing one or more subsurface properties in the subsurface domain, and predicting, using a second machine learning model, the one or more subsurface properties in the subsurface domain at a location that does not correspond to an existing well based on the seismic data, the one or more well logs, and the one or more seismic features that were extracted from the seismic data.