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.

Systems, computer-readable media, and methods for generating machine learning training data by obtaining reservoir data, determining subsections of the reservoir data, labeling the subsections of the reservoir data to generate labeled reservoir data, and feeding the labeled reservoir data into an artificial neural network. The reservoir data can be labeled using analysis data or based on interpretive input from an interpreter.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, to generate a custom seismic surface and volume attribute. In one aspect, a method includes receiving a seismic cube and a seismic surface, and the seismic cube includes traces recorded at receivers deployed to collect seismic data. The seismic surface is picked on the seismic cube. Seismic wavelets are extracted with a selected length from the seismic cube along an intersection with the seismic surface for each spatial coordinate associated with the seismic surface. A reference wavelet is determined. A surface attribute map is generated based on comparing each of the seismic wavelets to the reference wavelet. A productivity of the seismic surface is evaluated using the surface attribute map.

Methods of providing digital images of living tissue that may include: obtaining data of a propagating wavefield through living tissue; obtaining a reference digital image of the living tissue; 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; and determining a quantitative difference measure between the reference digital image and the new digital image based on the changing of one or more parameters selected from the group consisting of field sampling, imaging sampling, and image quality.

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.

A process for seismic imaging of a subterranean geological formation includes generating a source wavefield from seismic data representing a subterranean formation. The process includes generating a receiver wavefield from the seismic data representing the subterranean formation. The process includes decomposing the source wavefield to extract a source depth component and decomposing the receiver wavefield to extract a receiver depth component. The process includes applying a transform to each of the source depth component and the receiver depth component. The process includes combining the source depth component and the receiver depth component to generate an imaging condition. The process includes extracting a low-frequency term from the imaging condition to generate a wave-path tracking data, generating a wave path from the wave-path tracking data, and rendering a seismic image of at least a portion of the subterranean geological formation from the generated wave path.

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.

Some implementations provide a method including: accessing measurement data that characterize one or more features of a reservoir, wherein the measurement data are from more than well locations of the reservoir and from a range of depths inside the reservoir; detecting portions of the measurement data that characterize the one or more features with a statistical metric that is below a pre-determined threshold; based on removing the portions of the measurement data, identifying a plurality of layers along the range of depths of the reservoir; within each layer of the plurality of layers, grouping the measurements data among a plurality of clusters, each corresponding to a flow unit (FU) and determined by a machine learning algorithm; generating a three-dimensional (3D) permeability model of the reservoir based on the FU of each layer and a saturation height function; and simulating a performance of the reservoir based on the 3D permeability model.