A method for seismic geological lineation mapping, wherein a seismic dataset is collected, with information about minor lineations generated by Seismic dataset subtle structural geological features in an underground earth formation. Seismic attribute volumes are identified in the seismic dataset, relating to trace continuity, amplitude, frequency and phase. The attribute volumes may have an insufficient resolution to display the minor lineations. A seismic multivolume lithological lineation map is generated, in which single attribute 92d lineation maps generated for each of the identified seismic attribute volumes are combined to accurately display the minor lineations generated by the subtle geological features.

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.

Hydrocarbon exploration and extraction can be facilitated using machine-learning models. For example, a system described herein can receive seismic data indicating locations of geological bodies in a target area of a subterranean formation. The system can provide the seismic data as input to a trained machine-learning model for determining whether the target area of the subterranean formation includes one or more types of geological bodies. The system can receive an output from the trained machine-learning model indicating whether or not the target area of the subterranean formation includes the one or more types of geological bodies. The system can then execute one or more processing operations for facilitating hydrocarbon exploration or extraction based on the seismic data and the output from the trained machine-learning model.

A system and method may model physical geological structures. Seismic and geologic data may be accepted. A three-dimensional (3D) transformation may be generated between a 3D present day model having points representing present locations of the physical geological structures and a 3D past depositional model having points representing locations where the physical geological structures were originally deposited. An indication may be accepted to locally change the 3D transformation for a subset of sampling points in a first model of the models. The 3D transformation may be locally changed to fit the updated subset of sampling points. A locally altered or updated version of the first model and, e.g., second model, may be displayed where local changes to the first model are defined by the locally changed 3D transformation. The transformation may also be used to extract geobodies in the past depositional model.

In some embodiments, a time migration diffraction imaging method includes computing a pseudo-depth characterizing a subsurface seismic event by scaling a vertical traveltime using a scaling velocity. A specularity value for a subsurface seismic event is determined according to the pseudo-depth, and a contribution weight for a corresponding seismic trace amplitude is determined according to the specularity value. The specularity value may be determined according to an angle between a traveltime gradient and a normal to a local reflector surface. A diffraction image is generated according to a weighted sum of seismic trace amplitudes. The weighted sum attenuates the contribution of specular events relative to diffraction events.

The present disclosure describes computer-implemented methods, computer-program products, and computer systems, for providing parameters for successful automated fault patch extraction. Attributes are selected for annotating images generated from, and for interpretation of, seismic amplitude volume. Images are generated from layers of a seismic cube, each generated using a different attribute of the plural attributes. The plural images are blended using customized palettes and initial parameters to create a blended image illuminating discontinuities in the layers. Optimal parameters are iteratively determined for automatic derivation of fault discontinuities on an interpreter-selected edge-enhanced sub-volume. The iterations are controlled and terminated based on interpreter inputs. The optimal extraction parameters are applied to an entire edge-enhanced volume. Important extracted fault discontinuities are isolated using commercial filtering tools. Extracted fault patches are refined based on received manual interpretation. Patch results are converted to traditional fault objects for further interpretation and refinement.

Systems and methods for creating a surface in a faulted space, which includes using interpolation techniques.

Embodiments of methods of creating and interpreting animated mosaics of multiple seismic surveys are disclosed herein. Volumes from individual seismic surveys may be flattened in each seismic cube. Animations/movies may then be produced by capturing a series of z-slice movie frames through each of the flattened volumes. The individual sets of movie frames are geo-referenced to a basemap image of well locations using appropriate composition software. Where overlap exists between surveys, the surveys are prioritized and lower priority volumes are masked by higher priority volumes. This technique provides a matched, unbroken image across overlapping volumes at each stratigraphic layer. As the movie or animation plays, a moving arrow pointer shows the vertical position of the current movie frame on a stratigraphic section consisting of a seismic reference section that is optionally labelled with suitable regional sequence names and other stratigraphic zonation data.

A method and system for generating a model function h(x,y,z) implicitly representing geologic horizons. Geological data representing a fault network and horizons automatically extracted from seismic data may be received. A 3D mesh may be generated and divided into a plurality of fault blocks by the fault network. A discrete function h(x,y,z) may be defined having values of the geological data representing horizons at discrete nodes of the mesh. Constraints may be installed on the discrete function h(x,y,z) defining surfaces representing horizons. Constraints may be installed on the discrete function h(x,y,z) to ensure the uniqueness of the function h(x,y,z). The discrete function h(x,y,z) may be interpolated at the nodes of the mesh to create a piecewise continuous function h (x,y,z) while honoring the constraints. The piecewise continuous horizon function h(x,y,z) may be synchronized across multiple fault blocks. A model of the piecewise continuous horizon function h(x,y,z) may be displayed.

A method includes retrieving a seismic data set, receiving training data that includes one or more seed points of an identified geobody, determining a geobody trajectory of the identified geobody, based on the one or more seed points of the identified geobody, displaying the geobody trajectory, receiving inputs expanding the geobody trajectory, shrinking the geobody trajectory, confirming the geobody trajectory, or a combination thereof, training a classification algorithm using the geobody trajectory, running the classification algorithm on the seismic data set, receiving an output of one or more sets of voxels from the classification algorithm, skeletonizing the one or more sets of voxels to present the one or more sets of voxels as a set of possible geobody trajectories, and retraining the classification algorithm based on feedback received from a reviewer.