Abstract: Methods, systems, and non-transitory computer readable media for analyzing type curve regions in a subsurface volume of interest are disclosed. Exemplary implementations may include: obtaining initial clusters of type curve regions in the subsurface volume of interest; obtaining production values as a function of position; generating an autocorrelation correction factor; attributing the autocorrelation correction factor to the production values as a function of position; generating type curve mean values; generating range distribution values; generating a type curve cluster probability value for each of the type curve regions; generating a first representation of the type curve regions as a function of position; clustering the type curve regions in updated clusters; generating a second representation of the type curve regions as a function of position; and displaying one or more of the first representation and the second representation.

Abstract: Systems and methods are disclosed for identifying and displaying geostructural properties as a function of lithology, horizons, and faults interpreted from well and seismic data. Exemplary implementations may include obtaining an initial fracture distribution grid model; obtaining training structural deformation data; obtaining training subsurface lithology parameter data; obtaining training fracture attribute data; and training the initial fracture distribution grid model to generate a trained fracture distribution grid model.

Abstract: Systems and methods are disclosed for generating subsurface feature prediction probability distributions from a subsurface feature as a function of position in a subsurface volume of interest. For example, a computer-implemented method may include: obtaining subsurface data and well data, generating subsurface feature values, generating subsurface feature realizations, generating subsurface feature realization uncertainty values, generating subsurface parameter values, generating subsurface parameter realizations, generating subsurface feature prediction probability distributions, generating a first representation of likelihoods of the subsurface feature values, and displaying the first representation.

Abstract: Systems and methods are disclosed for identifying and displaying geostructural properties as a function of lithology, horizons, and faults interpreted from well and seismic data. Exemplary implementations may include obtaining an initial fracture distribution grid model; obtaining training structural deformation data; obtaining training subsurface lithology parameter data; obtaining training fracture attribute data; and training the initial fracture distribution grid model to generate a trained fracture distribution grid model.

Abstract: Systems and methods are disclosed for generating subsurface feature prediction probability distributions from a subsurface feature as a function of position in a subsurface volume of interest. For example, a computer-implemented method may include: obtaining subsurface data and well data, generating subsurface feature values, generating subsurface feature realizations, generating subsurface feature realization uncertainty values, generating subsurface parameter values, generating subsurface parameter realizations, generating subsurface feature prediction probability distributions, generating a first representation of likelihoods of the subsurface feature values, and displaying the first representation.