The present invention relates to a method of prediction of hydrocarbon accumulation in a geological region comprising the following steps of: a. Generation of a geological basin model; b. Generation of a geomechanical model; c. Generation of an integrated model; d. Generation of a strain map based on the information obtained in steps a to c; e. Prediction of hydrocarbon accumulation from the strain maps.

Enhanced seismic velocity models are generated using a geomechanical model. A tomographic velocity model is generated based on raw seismic data. One or more initial seismic images are generated based at least partially on the tomographic velocity model. Geomechanical data and the initial seismic images are used to generate a geomechanical model. The geomechanical model produces geomechanical outputs that are used to generate a geomechanical velocity model. A second tomographic velocity model is generated based on the first tomographic velocity model and the geomechanical velocity model.

A method for predicting a stress attribute of a subsurface earth volume includes simulating a linearly independent far field stress model, a discontinuity pressure model, and a discontinuity pressure shift model for the subsurface earth volume. A stress value, a discontinuity pressure shift at a surface of the Earth, a strain value, a displacement value, or a combination thereof is computed for data points in the subsurface earth volume based on a superposition of the linearly independent far field stress model, the discontinuity pressure models, and the discontinuity pressure shift model. The stress attribute of the subsurface earth volume is predicted, based on the computed stress value, the computed discontinuity pressure shift at the surface of the Earth, the computed strain value, the computed displacement value, or the combination thereof.

Methods, systems, and computer-readable media for processing seismic data. The method includes receiving a plurality of seismic traces representing a subterranean domain, and receiving an implicit stratigraphic model of at least a portion of the subterranean domain. The method also includes selecting an iso-value in the implicit stratigraphic model, and defining, using a processor, a geologically-consistent interval in the implicit stratigraphic model based at least partially on a position of the iso-value in the implicit stratigraphic model. The method further includes calculating one or more attributes of the plurality of seismic traces in the interval.

A method of analysing seismic data from a geological structure. The method includes determining a set of tiles from a data cube of seismic data and determining which tiles of the set of tiles can be grouped into one or more patches of tiles.

A method for determining a position of a geological feature in a formation includes acquiring a seismic dataset, wherein the seismic dataset is based on signals of one or more seismic sensors and determining a set of indicators of candidate discontinuities in the formation based on the seismic dataset. The method also includes labeling a subset of the set of indicators of candidate discontinuities using a neural network with a label based on the set of indicators of candidate discontinuities, wherein the label distinguishes an indicator of a candidate discontinuity between being an indicator of a target discontinuity or being an indicator of a non-target discontinuity and determining the position of the geological feature in the formation, wherein the geological feature in the formation is associated with at least one target discontinuity based on the subset of the set of indicators of candidate discontinuities.

An illustrative geologic modeling method may comprise: obtaining a geologic model representing a subsurface region in physical space, the subsurface region being divided into multiple zones; sequentially generating a physical space simulation mesh for each of said multiple zones by: (a) mapping a current zone of the physical space geologic model to a current zone of a design space model representing a current zone of an unfaulted subsurface region; (b) gridding the design space model to obtain a design space mesh; (c) partitioning cells in the current zone of the design space mesh with faults mapped from the current zone of the physical space geologic model, thereby obtaining a partitioned design space mesh for the current zone; and (d) reverse mapping the partitioned design space mesh for the current zone to the physical space for the current zone.

Systems and methods develop a three-dimensional model of a subterranean formation based on vertical seismic profiles at a plurality of well locations. This approach can include receiving seismic data for the subterranean formation including the vertical seismic profiles; for each vertical seismic profile, injecting a ground force into the vertical seismic profile to provide a reference trace at depth zero to estimate energy loss in each receiver providing data in the vertical seismic profile and estimating time and depth variant quality factors for the well location associated with the vertical seismic profile based on the seismic profile; estimating quality factors for points within a three-dimensional volume representing the subterranean formation by interpolating between the time and depth variant quality factors for the location associated with each vertical seismic profile; and combining estimated quality factors to generate a three-dimensional quality factor model of the three-dimensional volume representing the subterranean formation.

Methods and systems, including computer programs encoded on a computer storage medium can be used to preserve edges while performing Gaussian grid smoothing of noise components in subsurface grids to generate geological maps. A subsurface grid is generated from data indicating properties of subsurface formations. A weighting grid is generated by: i) receiving seismic data representing the subsurface formations; ii) generating seismic attributes associated with discontinuities in the subsurface formations; and iii) assigning a particular weight value to weighting grid points that the seismic attributes associated with discontinuities in the subsurface formations indicate the presence of a discontinuity. The subsurface grid is processed by iteratively computing local averages of grid points in the subsurface grid using a compact Gaussian filter weighted by values in the weighting grid. A geological map of subsurface formations is generated based on the filtered subsurface grid.

Systems and methods include a computer-implemented method: Seismic data is gathered for a reservoir with unknown fractured and unfractured areas. A structural model is generated. A geomechanical model is built. Geomechanically-estimated fractured areas are determined using the geomechanical model, including: areas where fractures are not likely to exist based on a likelihood lower than a first threshold likelihood, areas where fractures are likely to exist based on a likelihood greater than a second threshold likelihood, and areas where fracturing is unknown based on a likelihood between the first threshold likelihood and the second threshold likelihood. Machine learning-based estimates of a likelihood of a fracture of each area of the reservoir are determined using machine learning based on mathematical calculations of the seismic data. Fractured and unfractured areas are determined based on where fractures are likely to exist or not using the geomechanically-estimated fractured areas, machine learning-based likelihoods, and Bayes' Rule.