A method for estimating sonic slowness comprising: obtaining (700) a plurality of sonic waveforms are received by a plurality of receivers of a logging tool after emission of a source sonic wave by a transmitter, obtaining (710) slowness models of the subterranean formation, a slowness model being defined by a at least one cell of constant slowness for at least one wave energy mode, computing (720), for each slowness model, a set of candidate travel times, a candidate travel time of a set of candidate travel times being computed for a wave energy mode and a position of a receiver of the plurality of receivers, computing (730) a relevance indicator for each set of candidate travel times based on the recorded sonic waveforms; searching (740) a match between the sets of candidate travel times and the recorded sonic waveforms by searching a relevance indicator which is optimum, computing (750) a sonic slowness estimate for the subterranean formation from a set of candidate travel times for which the relevance indicator is optimum.

A method includes determining a top of salt (TOS) surface in a seismic volume based on a crossline direction of the seismic volume and an inline direction of the seismic volume. The method also includes determining a binary mask based upon the TOS surface. The method also includes sampling seismic data in the seismic volume to obtain a training seismic slice. The method also includes sampling the binary mask to obtain a mask slice. The method also includes selecting a first coordinate in the training seismic slice to produce a first tile. The method also includes selecting a second coordinate in the mask slice to produce a second tile. The method also includes generating or updating a model of the seismic volume based upon the first tile and the second tile.

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 can include receiving a node-based mesh that represents a geologic environment and a structural feature in the geologic environment; defining a node-based parameter space for structural uncertainty of nodes that represent the structural feature in the geologic environment; defining a hull with respect to nodes of a portion of the mesh where the hull encompasses at least a portion of nodes that represent the structural feature; for a system of equations, imposing boundary conditions on the nodes of the hull and on the at least a portion of the nodes that represent the structural feature; solving the system of equations for a displacement field; and generating a structural uncertainty realization of the node-based mesh based at least in part on the displacement field.

Unsegmented continuous subsurface data may be analyzed using one or more moving windows to characterize a subsurface region. Unsegmented continuous subsurface data may be scanned using the moving window(s). Probabilities that portions of the subsurface region include a subsurface feature may be determined based on analysis of the portions of the unsegmented continuous subsurface data within the moving window(s).

A system and method for determining an uncertainty of a distance to bed boundary (DTBB) inversion of a geologic formation. The system or method includes receiving logging data from a borehole tool, performing a first DTBB inversion using the logging data to calculate first DTBB solutions, adding quantified noise to the logging data to produce an adjusted signal, performing a second DTBB inversion using the adjusted signal to calculate second DTBB solutions, comparing the first DTBB solutions to the second DTBB solutions to determine an uncertainty of the first DTBB solutions based on a relationship of the quantified noise and the difference between the first DTBB solutions and the second DTBB solutions.

A method and system for modeling a subsurface region include applying a trained machine learning network to an initial petrophysical parameter estimate to predict a geologic prior model; and performing a petrophysical inversion with the geologic prior model, geophysical data, and geophysical parameters to generate a rock type probability model and an updated petrophysical parameter estimate. Embodiments include managing hydrocarbons with the rock type probability model. Embodiments include checking for convergence of the updated petrophysical parameter estimate; and iteratively: applying the trained machine learning network to the updated petrophysical parameter estimate of a preceding iteration to predict an updated rock type probability model and another geologic prior model; performing a petrophysical inversion with the updated geologic prior model, geophysical seismic data, and geophysical elastic parameters to generate another rock type probability model and another updated petrophysical parameter estimate; and checking for convergence of the updated petrophysical parameter estimate.

A method is described for generating a reservoir property model based on the quality of a seismic inversion product. The method may include receiving a seismic inversion product volume, a seismic attribute volume, and well data from wells drilled in a subsurface volume of interest; identifying collocated cells in the seismic volumes which correspond to the well data; creating attribute vectors from the seismic volumes in each of the collocated cells and a range of neighboring cells; calculating a seismic inversion error magnitude property at the collocated cells; training a data analytics method to predict the observed seismic inversion error magnitude property; verifying that the data analytics method accurately predicts the seismic inversion error magnitude using cross-validation; generating an inversion quality volume; and generating the reservoir property model based on the inversion quality volume. The method may be executed by a computer system.

A method is described for generating a reservoir property model based on the quality of a seismic inversion product. The method may include receiving a seismic inversion product volume, a seismic attribute volume, and well data from wells drilled in a subsurface volume of interest; identifying collocated cells in the seismic volumes which correspond to the well data; creating attribute vectors from the seismic volumes in each of the collocated cells and a range of neighboring cells; calculating a seismic inversion error magnitude property at the collocated cells; training a data analytics method to predict the observed seismic inversion error magnitude property; verifying that the data analytics method accurately predicts the seismic inversion error magnitude using cross-validation; generating an inversion quality volume; and generating the reservoir property model based on the inversion quality volume. The method may be executed by a computer system.

Systems and methods for training a model that uses probabilities of lithologies as prior information in an inversion are disclosed. Exemplary implementations may: obtain training data, the training data including (i) subsurface map data sets, and (ii) known lithologies; obtain an initial seismic mapping model; generate a conditioned seismic mapping model by training the initial seismic mapping model; store the conditioned seismic mapping model; obtain a target subsurface map data set; apply the conditioned seismic mapping model to generate a classified lithology map data set; apply an inversion to the classified lithology map data set to generate volumes of lithologies; generate an image that represents the volumes of lithologies; display the image.