Computer-implemented methods, apparatus, and computer programs disclosed herein are for obtaining horizon data, and comprise determining at least one extrema binary volume from a seismic data volume comprising a plurality of voxels and assigning a predetermined extrema value to each one of the found extreme voxels; determining an extrema graph from the at least one extrema binary volume; determining a neighbour graph from the extrema graphs; partitioning the nodes of the neighbour graph into a set of clusters, wherein a cluster contains a plurality of connected nodes representing extrema voxels of the at least one extrema binary volume, and each node is part of one cluster only; for each subset of the set of clusters, identifying whether or not the subset is a contradictory set of clusters according to at least one first predetermined condition; hierarchically partitioning the neighbour graph into a plurality of subgraphs, for example using a minimum cut approach, each subgraph being provided by a separate non-contradictory cluster, which is not part of any contradictory sets of clusters; and obtaining horizon data representing a plurality of horizons from the non-contradictory clusters inducing the plurality of subgraphs of the neighbour graph.

A sonic logging method is provided that transmits acoustic signals using a high order acoustic source and processes waveform data to identify a set of arrival events and time picks by automatic and/or manual methods. Ray tracing inversion is carried out for each arrival event over a number of possible raypath types that include at least one polarized shear raypath type to determine two-dimensional reflector positions and predicted inclination angles of the arrival event for the possible raypath types. One or more three-dimensional slowness-time coherence representations are generated for the arrival event and raypath type(s) and evaluated to determine azimuth, orientation and raypath type of a corresponding reflector. The method outputs a three-dimensional position and orientation for at least one reflector. The information derived from the method can be conveyed in various displays and plots and structured formats for reservoir understanding and also output for use in reservoir analysis and other applications.

A fully automated method for correcting errors in one interpretation (13) of seismic data based on comparison to at least one other interpretation (14) of the same subsurface region. The errors may occur in any feature of the seismic data volume, for example a horizon, surface, fault, polyline, fault stick, or geo-body. In some embodiments of the invention, an error may be a hole in a horizon (53), and the whole is patched by a piece of a horizon in another interpretation (55). In an alternative embodiment of the invention, a single interpretation may be used to repair itself, for example by identifying similarly shaped, adjacent horizons (67), and merging them (68).

Methods and systems for identifying a multiple artifact are disclosed. The method includes obtaining a post-stacked seismic image of a subterranean region and identifying a horizon with the post-stacked seismic image. The method further includes determining a spectral section over the horizon by applying spectral decomposition to the post-stacked seismic image. The method still further includes detecting a frequency anomaly within the spectral section by comparing the spectral section to a reference spectral section and identifying the multiple artifact based on the frequency anomaly.

The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for smoothing seismic data. One computer-implemented method includes obtaining, by a hardware data processing apparatus, a plurality of seismic data samples; forming, by the hardware data processing apparatus, guiding vectors using the plurality of seismic data samples and a plurality of guiding structure attributes; generating, by the hardware data processing apparatus, a structure guided directional weighted vector filter using the guiding vectors and a plurality of weighting factors; filtering, by the hardware data processing apparatus, the seismic data samples using the structure guided directional weighted vector filter to generate smoothed seismic data; and initiating output of the smoothed seismic data.

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.

Well information may define subsurface configuration of different wells. Marker information defining marker positions within the wells may be obtained. A dissimilarity matrix for the wells may generated, with the element values of the dissimilarity matrix determined based on comparison of corresponding subsurface configuration of the wells. A gated dissimilarity matrix may be generated from the dissimilarity matrix based on the marker positions within the wells. The elements values of the gated dissimilarity matrix corresponding to one set of marker positions and not corresponding to the other set of marker positions may be changed. Correlation between the wells may be determined based on the gated dissimilarity matrix such that correlation exists between a marker position in one well and a marker position in another well.

A method is described for assessing subsurface structure uncertainty based on at least one subsurface horizon. The method calculates seismic continuity attributes to determine a mappability of the subsurface horizon(s); determines horizontal uncertainty for each fault in vertical uncertainty for each horizon; generates probabilistic scenarios for a subsurface geometry for at least one conceptual model; and generates a map of geological model uncertainty based on the probabilistic scenarios. In some embodiments, the probabilistic scenarios are stochastic simulations. In some embodiments, generating a map of geological model uncertainty is based on information entropy. The method may be executed by a computer system.

Systems and methods for geochemical sampling grid locations on a seafloor. At least one of the methods includes generating, using received seismic data, an image representing an interpretation of a seafloor horizon surface; extracting, from the image and based on the seismic data, one or more discontinuity attributes of the seafloor horizon surface; extracting, from the image and based on the seismic data, one or more amplitude attributes of a window extending below the seafloor horizon surface; combining the one or more discontinuity attributes and the one or more amplitude attributes; and selecting, using the image and based at least partly on the combining, one or more locations of the seafloor horizon surface for sampling.

A computer-implemented method includes receiving a test seismic dataset associated with a known truth interpretation, receiving one or more hard constraints, training a machine learning system based on the test seismic dataset, the known truth interpretation, and the one or more hard constraints, determining an error value based on the training the machine learning system, adjusting the error value based on one or more soft constraints, updating the training of the machine learning system based on the adjusted error value, receiving a second seismic dataset after the updating the training; applying the second seismic dataset to the machine learning system to generate an interpretation of the second seismic dataset, generating a seismic image representing a subterranean domain based on the interpretation of the second seismic dataset, and outputting the seismic image.