In contrast to existing methods wherein derived horizons are interpreted in isolation, the disclosure provides a process that does not interpret patches themselves but determines the relationships between patches, in order to associate and link patches to derive a holistic geological interpretation. Predefined patches, such as from a pre-interpreted suite, are received as inputs to determine the relationships and derive an interpretation for a complete volume. In one aspect the disclosure provides an automated method of generating a geological age model for a subterranean area. In one example, the automated method includes: (1) abstracting seismic data of a subsurface into a limited number of patches, (2) abstracting the patches by defining patch-links between the patches, and (3) generating a geological age model of the subsurface by solving for the relative geological age of each of the patches using the patch-links.

In a first step, a defined scope value is selected for each of a plurality of hydrodynamic input parameters. A simulated topographical result is generated using the selected scope values and a forward model. A detailed seismic interpretation is generated to represent specific seismic features or observed topography. A calculated a misfit value representing a distance between the simulated topographical result and a detailed seismic interpretation is minimized. An estimated optimized sand ratio and optimized hydrodynamic input parameters are generated. In a second step, a genetic algorithm is used to determine a proportion of each grain size in the estimated optimized sand ratio. A misfit value is used that is calculated from thickness and porosity data extracted from well data and a simulation result generated by the forward model to generate optimized components of different grain sizes. Optimized hydrodynamic input parameters and optimized components of different grain sizes are generated.

Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for using direction-angles to identify geologic features and geologic attributes for use in geothermal, oil and gas, mining, and other applications. An example embodiment operates by receiving a discrete three-dimensional (3D) representation of a geologic volume comprising a set of 3D orientations, where each 3D orientation is represented as a set of direction-angles measured relative to a set of coordinate axes. The example embodiment further operates by receiving a set of other measurements of properties of the geologic volume, In response, the example embodiment operates by correlating the set of 3D orientations with the set of other measurements to generate a geologic correlation data structure. Subsequently, the example embodiment operates by identifying a geologic attribute or a geologic feature associated with the geologic volume based on the geologic correlation data structure.

The present disclosure relates to a method for determination of a real subsoil geological formation. In at least one embodiment, the method includes receiving a model representing the real subsoil, determining a first fluvial geological formation in said model using parametric surfaces, determining a subsequent fluvial geological formation as a deformation of the first fluvial geological formation using parametric surfaces, and subtracting the first fluvial geological formation from the subsequent fluvial geological formation to create a new geological formation named point bar formation.

A method includes receiving a model representing a real subsoil geological formation. The model includes a stratigraphic layer, which includes a shore line dividing the stratigraphic layer into a continental zone and a marine zone. First and second flow speed fields are received, with the first flow speed field representative of a continental domain for the stratigraphic layer, and the second flow speed field representative of a marine domain for the stratigraphic layer. A global flow speed field is determined and includes a weighted combination of the first and second flow speed fields for each position in the stratigraphic layer. Weights of the combination are based on a distance of the position to the shore line and whether the position is within the continental zone or the marine zone. The real subsoil geological formation for the stratigraphic layer is determined based on the determined global flow speed field.

A computer system and method for determining subterranean rock composition is described in which user input data is received having a plurality of parameters defining a desired subterranean rock composition from a wellbore. Data associated with at least one geologic environment is received, which data contains data acquired from at least one wellbore. An analytical analysis is then conducted by a computer processor utilizing the user input data and the received geologic environment data to determine a match between the user desired subterranean rock composition and the received geologic environment data. Output graphic data is then determined and generated, based at least in part on the analytical analysis, on a computer graphical display consisting of a two-dimensional (2D) graphical representation indicating a region of the geologic environment having a match between the user desired subterranean rock composition and the received geologic environment data.

A hierarchical conditioning methodology for building and conditioning a geological model is disclosed. In particular, the hierarchical conditioning may include separate levels of conditioning of template instances using larger-scale data (such as conditioning using large-scale data and conditioning using medium-scale data) and using smaller-scale data (such as fine-scale data). Further, one or more templates, to be instantiated to generate the geological bodies in the model, may be selected from currently available templates and/or machine-learned templates. For example, the templates may be generated using unsupervised or supervised learning to re-parameterize the functional form parameters, or may be generated using statistical generative modeling.

Displaying a spiral-shaped visualization of a geological time scale according to some aspects may include accessing a time-attributed data set representing a geological time scale of a subterranean region. The geological time scale may be segmented into a hierarchy of intervals (e.g., periods, epochs, and stages). The spiral-shaped visualization may include a path formed in a spiral formation. The path may begin at a center position of the spiral-shaped visualization and may end at an outer portion of the spiral-shaped visualization. The beginning of the path may represent a first time of the geological time scale. The ending of the path may represent a second time of the geological time scale. The spiral-shaped visualization may also be segmented to represent the hierarchy of intervals. Additionally, the spiral-shaped visualization may be interactive. Selecting an interval of the path may automatically cause the intervals of the spiral-shaped visualization to be filtered.

A method is claimed that includes obtaining a measured present-day value of at least one parameter for each member of a set of unvalidated geological layers arranged in order of increasing depth and iteratively selecting a member of the set as a current layer. For each current layer in turn, the method further determines an estimated archaic value of at least one parameter of the current layer based on its measured present-day value by applying an alternating cycle of decompaction followed by geomechnical modeling to predict a present-day value of the parameter of the current layer based on its estimated archaic value. The method still further determines a validated archaic value of at least one parameter of each current layer based on a difference between the predicted and the measured present-day values. A non-transitory computer readable medium storing instructions for validating the archaic value for each layer is claimed.

The present disclosure relates to the field of geophysical processing methods for oil and gas exploration, and more particularly, to a method for gas detection using multiple quantum neural networks. A plurality of stratigraphic and structural seismic attributes are extracted from the seismic data of a target horizon, and input seismic characteristic parameters are divided into different classes by using an unsupervised learning and supervised learning combined quantum self-organizing feature map network. Gas detection is then performed using a particle swarm optimization based quantum gate node neural network with clustering results of various seismic characteristic parameters output by the quantum self-organizing feature map network as inputs. The present method uses the unsupervised learning and supervised learning combined quantum self-organizing feature map network for a plurality of stratigraphic and structural seismic attributes of the seismic data and thus has improved accuracy and uniqueness of clustering.