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

A method (1200) can include receiving implicit functions values for a mesh that represents a geologic environment that includes intersecting faults defined by fault patches (1210); assigning states to the fault patches (1220); revising the implicit function values based at least in part on the assigned states to provide revised implicit function values (1230); and outputting a structural model of the geologic environment based at least in part on the revised implicit function values (1240).

A method for interpreting seismic data includes receiving seismic data that represents a subterranean volume, and generating inline probability values and crossline probability values using a first machine learning technique. The first machine learning technique is trained to identify one or more vertical fault lines in a seismic volume based on the seismic data. The method includes generating a merged data set by combining the inline probability values and the crossline probability values, training a second machine learning technique based on a subset of labeled horizontal planes from the merged data set, the second machine learning technique trained to identify horizontal fault lines from the seismic volume, and generating a representation of the seismic volume based on the second machine learning technique, the representation including an indication of a three-dimensional fault structure within the seismic volume.

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.

A method (1310) for attributing a systems tract to a geological environment, including computing (1314) a shelf break for a geologic environment based at least in part on implicit function values associated with the geologic environment; identifying (1318) sea level variations with respect to geological time for the shelf break; and assigning (1322) at least one systems tract to the geologic environment based at least in part on the sea level variations.

A method, apparatus, and program product may model a subsurface formation by computing an iso-surface for an iso-value from a three-dimensional stratigraphic function (436) for a volume of interest in the subsurface formation, computing first and second strike traces (454, 456) following a topography of the computed iso-surface on respective first and second sides of a fault (452) in the volume of interest, extracting seismic data (458, 460) along the first and second strike traces, correlating the extracted seismic data along the first and second strike traces, and computing a fault displacement vector (C) for the fault from the correlated extracted seismic data along the first and second strike traces.

Systems and methods of placing wells in a hydrocarbon field based on seismic attributes and quality indicators associated with a subterranean formation of the hydrocarbon field can include receiving seismic attributes representing the subterranean formation and seismic data quality indicators. A cutoff is generated for each seismic attribute and seismic data quality indicator. A weight is assigned to each seismic attribute and seismic data quality indicator. The weighted seismic attributes and data quality indicators are aggregated for each location in the hydrocarbon field. A risk ranking is assigned based on the weighted seismic attributes and data quality indicators associated with each location in the hydrocarbon field based on the cutoffs. A map is generated with each location on the surface of the subterranean formation color-coded based on its assigned risk ranking.

A method for computing effective permeability for a discrete fracture network in a subterranean environment. The method may include: obtaining (800) attributes of the discrete fracture network, spatially sampling (810) points on the surfaces of fractures inside a computation cell; allocating (820) to each sampled point at least one attribute of the corresponding fracture, computing (850) discrete pressure values in the computation cell at the location of the sampled points by solving partial derivative equations of a flow model; computing effective permeability (860) values for the computation cell from the pressures values.

A method for generating a seismic image representing a subsurface includes receiving seismic data for the subsurface formation, including receiver wavelet data and source wavelet data. Source wavefield data are generated based on a forward modeling of the source wavelet data. Receiver wavefield data are generated that compensate for distortions in the seismic data by: applying a dispersion-only model to the receiver wavelet data to generate a first reconstructed back-propagated receiver wavefield portion, applying a dissipation-only model to the receiver wavelet data to generate a second reconstructed back-propagated receiver wavefield portion, and combining the first back-propagated receiver wavefield portion and the second back-propagated receiver wavefield portion into the receiver wavefield data. The method includes applying an imaging condition to the receiver wavefield data and the source wavefield data and generating, based on applying the imaging condition, visco-acoustic reverse time migration (VARTM) result data.

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.