The disclosed embodiments include systems and methods to assess geological data. The method includes obtaining data associated with a geological state of a geological entity. The method also includes assessing a nature of a geological age constraint of the geological entity. The method further includes generating a first probability distribution of a geological age of the geological entity based on the nature of the geological age constraint of the geological entity. The method further includes selecting a time of interest for analysis of the geological entity. The method further includes assessing a nature of the geological age constraint during the time of interest. The method further includes generating a second probability distribution for the time of interest. The method further includes determining a likelihood that the geological age constraint of the geological entity coincides with the time of interest.

A model-driven deep learning-based seismic super-resolution inversion method includes the following steps: 1) mapping each iteration of a model-driven alternating direction method of multipliers (ADMM) into each layer of a deep network, and learning proximal operators by using a data-driven method to complete the construction of a deep network ADMM-SRINet; 2) obtaining label data used to train the deep network ADMM-SRINet; 3) training the deep network ADMM-SRINet by using the obtained label data; and 4) inverting test data by using the deep network ADMM-SRINet trained at step 3). The method combines the advantages of a model-driven optimization method and a data-driven deep learning method, and therefore the network has the interpretability; and meanwhile, due to the addition of physical knowledge, the iterative deep learning method lowers requirements for a training set, and therefore an inversion result is more reliable.

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 techniques described herein relate to methods, apparatus, and computer readable media for real-time induced seismicity management. A distribution value, such as a b value, and an uncertainty of the distribution value is calculated based on the received magnitude data, wherein the distribution represents the proportion of each magnitude earthquake observed in the distribution. A seismogenic index is calculated based on a set of fluid injection rates and the distribution value, wherein the seismogenic index represents the proportion of earthquakes per volume of fluid injected into the earth at a particular location. A distribution of a number of earthquakes that will be induced of each magnitude from future injection is forecasted based on the seismogenic index. A ground motion prediction model is calculated, representing shaking intensity and distance based on the forecasted distribution of earthquakes. Seismicity can then be managed to not exceed a tolerated chance of induced shaking consequences.

A computer system receives multiple datapoints of a geomechanical property of a hydrocarbon reservoir modeled by a three-dimensional (3D) grid. Each datapoint corresponds to a respective grid cell of the 3D grid. Each grid cell of the 3D grid is represented by 3D coordinates. For each grid cell of the 3D grid, the computer system generates a data component of the geomechanical property based on the 3D coordinates of the grid cell. The computer system adds the data component to a datapoint corresponding to the grid cell to provide an augmented set of datapoints. The computer system transforms the augmented set of datapoints into a Gaussian distribution using Gaussian approximation. The computer system simulates the geomechanical property of the hydrocarbon reservoir based on the Gaussian distribution using sequential Gaussian simulation. A display device of the computer system generates a graphical representation of the geomechanical property of the hydrocarbon reservoir based on the sequential Gaussian simulation.

A computer-implemented method is described for seismic facies identification including receiving a seismic dataset representative of a subsurface volume of interest; applying a model conditioned by petrophysical classifications to the seismic dataset to identify seismic facies and generate a classified seismic image; and identifying geologic features based on the classified seismic image. The method generates seismic facies probability volumes.

A method, computer program product, and computing system are provided for receiving reservoir data associated with the reservoir. A simulation may be performed on the reservoir data to generate simulated reservoir data. A subset of realizations including a minimal number of realizations from a plurality of realizations may be determined based upon, at least in part, one or more statistical moments of the simulated reservoir data. An optimized reservoir model associated with an objective may be generated based upon, at least in part, the subset of realizations including the minimal number of realizations.

Geostatistical realizations are generated based on estimates, which are based on measurements for which one can compute corresponding variances. A shift field is generated by generating random values, which may be constrained according to a confidence restraint. A value of the shift field is applied to a standard deviation for an estimate calculated based on the corresponding variance for that estimate. The random values may be generated according to a gradient noise algorithm taking as an input a wavelength defining smoothness of an array of random values. The wavelength is decoupled from a grid spacing of the estimated values.

An illustrative hydraulic fracture mapping method includes: collecting microseismic signals during a multistage hydraulic fracturing operation; deriving microseismic event locations from the microseismic signals to create a microseismic event map for each stage of the operation; fitting a set of fracture planes to the microseismic event maps; determining a stimulated reservoir volume (“SRV”) region for each said stage; identifying where SRV regions overlap to form an overlap region; partitioning the overlap region to eliminate any overlap between the SRV regions; truncating the set of fracture planes for the SRV regions to discard any portion outside the revised SRV regions; and storing or displaying the truncated set of fracture planes for the first revised SRV region.

The present invention relates to a method for determination of real subsoil composition or structure characterized in that the method comprises: receiving a model representing the real subsoil, said model comprising at least one parametric volume describing a geological formation in said model, said volume having a plurality of cells; for each cell in the plurality of cells, determining a quality index (QI.sub.cell) function of a respective position of the cell in the geological formation; receiving a set of facies, each facies in said set being associated with a proportion and a quality index ordering in said formation; associating a facies to each cell, said association comprising: /a/ selecting a cell with a lowest quality index within cells in the plurality of cells having no facies associated to; /b/ associating, to said cell, a facies with a lowest Quality index ordering within facies of the set of facies for which the respective proportion is not reached in the formation; /c/ reiterating steps /a/ to /c/ until all cells in the plurality of cells are associated with a facies.