Systems and method are claimed for forming an artifact attenuated seismic image. The method includes obtaining an input seismic image, selecting a seismic partition from the input seismic image and determining a seismic dip for the seismic partition. The method further includes determining flattened seismic partition from the seismic partition based, at least in part, on the seismic dip, determining a filtered seismic partition from the flattened seismic partition, and determining an unflattened seismic segment based on the filtered seismic partition. The method still further includes determining the artifact attenuated seismic image based on the unflattened seismic segment. The system includes a seismic source, a plurality of seismic receivers for detecting and recording an observed seismic dataset generated by the radiated seismic wave; and a seismic processor configured form the artifact attenuated seismic image.

Systems and methods for estimating reservoir productivity as a function of position in a subsurface volume of interest are disclosed. Exemplary implementations may: obtain an initial depth uncertainty model; obtain training depth uncertainty parameter values from the non-transient storage medium; obtain corresponding training depth uncertainty values; generate a trained depth uncertainty model by training the initial depth uncertainty model using the training depth uncertainty parameter values and the corresponding training depth uncertainty values; and store the trained depth uncertainty model.

Geologic modeling methods and systems disclosed herein employ an improved simulation gridding technique. For example, an illustrative geologic modeling method may comprise: obtaining a geologic model representing a faulted subsurface region in physical space; mapping the physical space geologic model to a design space model representing an unfaulted subsurface region; gridding the design space model to obtain a design space mesh; partitioning cells in the design space mesh with faults mapped from the physical space geologic model, thereby obtaining a partitioned design space mesh; mapping the partitioned design space mesh to the physical space to obtain a physical space simulation mesh; and outputting the physical space simulation mesh.

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.

The present invention describes a method for adaptively determining a plurality of sedimentary facies from 3D seismic data, comprising the steps of (a) generating an attribute volume comprising at least one attribute from said 3D seismic data; (b) generating at least one frequency decomposition colour blend volume from said 3D seismic data; (c) generating a data volume comprising at least one geological object utilising data from said attribute volume and said frequency decomposition colour blend volume; (d) generating a facies classification model dataset for a predetermined region of interest of said 3D seismic data applying a probabilistic algorithm and utilizing data from said geobody volume and said frequency decomposition colour blend volume; (e) selectively adjusting at least one first model parameter, so as to optimise said facies classification model dataset in accordance with a conceptual geological model; and (f) selectively providing said facies classification model dataset in a representative property model of said region of interest of said 3D seismic data.

A method to model a subterranean formation of a field. The method includes obtaining a seismic volume comprising a plurality of seismic traces of the subterranean formation of the field, computing, based on the seismic volume, a seismically-derived value of a structural attribute representing a structural characteristic of the subterranean formation, computing, based on a structural model, a structurally-derived value of the structural attribute, the structural model comprising a plurality of structural layers of the of the subterranean formation, comparing the seismically-derived value and the structurally-derived value to generate a difference value representing a discrepancy of modeling the structural attribute at a corresponding location in the subterranean formation, and generating a seismic interpretation result based on the difference value and the corresponding location.

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.

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.

Methods, systems, and computer-readable medium to perform operations including: clipping an average velocity grid of a seismic reference surface (SRSAV), in an oil and gas field, to remove average velocity data of a region containing high-angle, horizontal (HA/HZ) boreholes, wherein the seismic reference surface approximates a geological reference surface; based on (i) a depth grid of the geological reference surface (GRSD) generated using HA/HZ borehole data, and (ii) a time grid of the seismic reference surface (SRST), generating borehole average velocity grid (BAV) along the HA/HZ boreholes; gridding the BAV with the clipped SRSAV to generate a hybrid seismic borehole average velocity grid (HSBAV) of the oil and gas field; and based on the HSBAV and the SRST, generating a hybrid seismic geological depth grid (HSGD) of the oil and gas field.