Described embodiments generally relate to a method of determining parameters of VTI anisotropy of a subsurface shale formation. The method comprises receiving wireline log data relating to the subsurface formation, the data comprising density and a clay content indicator; identifying at least one layer of shale in the subsurface formation based on the wireline log data; calculating porosity, clay fraction and silt fraction based on the wireline log data; calculating an orientation distribution function (ODF) of clay platelets within the at least one layer of shale based on the clay fraction and porosity data; estimating at least three independent anisotropy parameters based on the ODF, porosity and silt fraction, the at least three anisotropic parameters comprising a shear wave anisotropy parameter; comparing the estimated shear wave anisotropy parameter with a measured shear wave anisotropy parameter determined based on the sonic log data; upon determining that the estimated shear wave anisotropy parameter is different from the measured shear wave anisotropy parameter by more than a threshold amount, determining parameters of best fit to minimise the difference between the estimated shear wave anisotropy parameter and the measured shear wave anisotropy parameter; adjusting the estimated anisotropy parameters based on the parameters of best fit; and outputting the adjusted anisotropy parameters.

The disclosure provides systems and methods to enhance pre-stack data for seismic data analysis by: sorting the reflection seismic data acquired from cross-spread gathers into sets of data sections; performing data enhancement on the sets of data sections to generate enhanced traces by: (i) applying forward normal-moveout (NMO) corrections such that arrival times of primary reflection events become more flat, (ii) estimating beamforming parameters including a nonlinear traveltime surface and a summation aperture, (iii) generating enhanced traces that combine contributions from original traces in the sets of data sections, and (iv) applying inverse NMO corrections to the enhanced traces such that temporal rearrangements due to the forward NMO corrections are undone.

A computer-implemented method of enhancing a computer to estimate an uncertainty of an onset of a signal of interest in time-series noisy data. A first mathematical model of first time series data that contains only noise is calculated. A second mathematical model of second time series data that contains the noise and an onset of a signal of interest in the second time series data is calculated. A difference is evaluated between a first combination, being the first mathematical model and the second mathematical model, and a second combination, being the first time series data and the second time series data, wherein evaluating is performed using a generalized entropy metric. A specific time when an onset of the signal of interest occurs is estimated from the difference. An a posteriori distribution is derived for an uncertainty of the specific time at which the onset occurs.

A method includes receiving a first transition probability matrix (TPM) of a subsurface region, wherein the TPM defines, for a given lithology at a current depth sample (or micro-layer), a probability of particular lithologies at a next depth sample (or micro-layer), receiving seismic data for the subsurface region, utilizing the first TPM and the seismic data to generate first pseudo wells, calculating a second TPM from the first pseudo wells, determining whether the second TPM is consistent with the first TPM, and utilizing the first pseudo wells to characterize a reservoir in the subsurface region when the second TPM is determined to be consistent with the first TPM.

Described herein are implementations of various technologies for a method for seismic data processing. The method may receive seismic data for a region of interest. The seismic data may be acquired in a seismic survey. The method may determine sparse seismic data by selecting shot points in the acquired seismic data using statistical sampling. The method may determine simulated seismic data based on an earth model for the region of interest, a reflection model for the region of interest, and the selected shot points. The method may determine an objective function that represents a mismatch between the sparse seismic data and the simulated seismic data. The method may update the reflection model using the objective function.

A system, method and program product for implementing a machine learning platform that processes a data map having feature and operational information. A system is disclosed that includes an interpretable machine learning model that generates a function in response to an inputted data map, wherein the data map includes feature data and operational data over a region of interest, and wherein the function relates a set of predictive variables to one or more response variables; an integration/interpolation system that generates the data map from a set of disparate data sources; and an analysis system that evaluates the function to predict outcomes at unique points in the region of interest.

Multi-vintage energy mapping selects a first seismic survey data and a second seismic survey dataset from a plurality of seismic survey datasets. The first seismic survey dataset includes a set of first energies associated with a first seismic survey geometry, and the second seismic survey dataset includes a set of second energies associated with a second seismic survey geometry. The first set of energies are mapped from the first seismic survey geometry to the second seismic survey geometry, and the second set of energies are mapped from the second seismic survey geometry to the first seismic survey geometry. An updated first seismic dataset and an updated second seismic dataset are generated such that only energies from the first and second seismic datasets associated with changes in a subsurface are preserved in the updated first and second seismic datasets.

A method can include selecting a type of geophysical data; selecting a type of algorithm; generating synthetic geophysical data based at least in part on the algorithm; training a deep learning framework based at least in part on the synthetic geophysical data to generate a trained deep learning framework; receiving acquired geophysical data for a geologic environment; implementing the trained deep learning framework to generate interpretation results for the acquired geophysical data; and outputting the interpretation results.

Techniques are disclosed relating to acquisition and imaging for marine surveys. In some embodiments, a transition survey that uses both one or more sources of a first type (e.g., impulsive sources) and one or more sources of a second type (e.g., vibratory sources) may facilitate calibration of prior surveys that use the first type of sources with subsequent surveys that use the second type of source. In some embodiments, the different types of sources may be operated simultaneously at approximately the same location. In some embodiments, signals generated by the sources are separated, e.g., using deconvolution. The signals may then be compared to generate difference information, which in turn may be used to adjust sensor measurements from a previous or subsequent survey. In various embodiments, the disclosed techniques may improve accuracy in images of geological formations and may facilitate transitions to new types of seismic sources while maintaining continuity in 4D surveys.

Systems and methods for determining shear failure of a rock formation are disclosed. The method includes receiving, by a processor, a plurality of parameters related to physical properties of the rock formation, applying the plurality of parameters to a predetermined failure criterion, and determining shear failure of the rock formation based on the failure criterion. In some embodiments the failure criterion is a modified Hoek-Brown failure criterion that takes into consideration an intermediate principal stress, and the difference between normal stresses and an average confining stress.