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.

Methods for inversion of seismic data to infer subsurface physical property parameters, comprising constructing an inhomogeneous anisotropy model and/or inhomogeneous V.sub.S/V.sub.P or V.sub.P/V.sub.S model; and inverting the seismic data in a sequential or simultaneous approach to obtain at least one subsurface physical property parameter using an elastic inversion algorithm and the inhomogeneous anisotropy model and/or inhomogeneous V.sub.S/V.sub.P or V.sub.P/V.sub.S model. Constructing an inhomogeneous anisotropy model may comprise deriving geobodies from at least one of seismic facies analysis, regional geologic information, or seismically derived earth models; and adjusting at least one of , , , or parameters of the elastic stiffness tensor matrix in a homogeneous anisotropy model in areas corresponding to the geobodies. Constructing an inhomogeneous V.sub.S/V.sub.P or V.sub.P/V.sub.S model may comprise deriving geobodies and adjusting values in a homogeneous V.sub.S/V.sub.P or V.sub.P/V.sub.S model in areas corresponding to the geobodies.

Systems, methods, and computer programs for monitoring a drilling operation in a subterranean formation include receiving, from a first sensor array, one or more seismic signals caused, at least in part, by the drilling operation in the subterranean formation; receiving, from the first sensor array, one or more electromagnetic signals generated by an electroseismic or seismoelectric conversion of the one or more seismic signals caused, at least in part, by the drilling operation in the subterranean formation; and determining a property of one or more of the drillstring and the subterranean formation based, at least in part, on the seismic signals and the corresponding electromagnetic signals received from the first sensor array. The first sensor array is arranged to monitor the drilling operation.

Methods, systems, and computer program products for characterizing materials in a wellbore having multiple casing strings uses well completion data and instantaneous frequency, instantaneous phase, and/or amplitude attributes, including waveform amplitude and instantaneous amplitude, of an acoustic waveform to determine material densities, acoustic velocities and acoustic travel distances for the materials between the various stages of casings.

Systems and methods are disclosed that include generating reservoir property profiles corresponding to reservoir properties for pseudo wells based on reservoir data, generating seismic attributes for the pseudo wells, and training a machine learning model by comparing the reservoir property profiles against the seismic attributes. In this manner, the machine learning model may be used to predict reservoir properties for use with seismic exploration above a region of a subsurface that contains structural or stratigraphic features conducive to a presence, migration, or accumulation of hydrocarbons.

Modeling basin geology in a subsurface region includes receiving seismic data representing acoustic signals that are reflected from regions of the subsurface; receiving potential fields data comprising potential field values that are mapped to locations in the subsurface; determining a relationship between the seismic data and the potential field values for each of the locations in the subsurface; generating, based on the relationship for each location, a three-dimensional (3D) map of thermal conductivity in the subsurface region; and based on the 3D map of thermal conductivity, identifying at least one area comprising source rock having a threshold maturity, the threshold maturity indicative of potential hydrocarbons in the subsurface.

A method is disclosed that includes obtaining a seismic data set and a seismic wave propagation velocity model and approximating the seismic wave propagation velocity model as a plurality of layers each bounded by a first and second bounding depth. For each of the plurality of layers, the method includes: simulation of the propagation of a seismic wave through the layer using a two-way seismic wave propagation simulator; forming an over-determined system of linear equations relating at least one mono-frequency component of the seismic wave at the first depth to one mono-frequency component at the second depth; and determining a plurality of one-way seismic wave propagation operators by inverting the over-determined system of linear equations. The method further includes processing the seismic data set using the one-way seismic wave propagation. A system and a non-transitory computer readable medium for implementing the method are also disclosed.

Processes and systems described herein are directed to performing marine surveys with marine vibrators that emit orthogonal coded pseudo-random sweeps. In one aspect, coded pseudo-random signals are generated based on coded pseudo-random sequences. The coded pseudo-random sequences are used to activate the marine vibrators in a body of water above a subterranean formation. The activated marine vibrators generate orthogonal coded pseudo-random sweeps. A wavefield emitted from the subterranean formation in response to the orthogonal coded pseudo-random sweeps is detected at receivers located in a body of water. Seismic signals generated by the receivers may be cross-correlated with a signature of one of the orthogonal coded pseudo-random sweeps to obtain seismic data with incoherent residual noise.

A method is herein presented to statistically combine multiple seismic attributes for generating a map of the spatial density of fractures. According to an embodiment a first step involves interpreting the formation of interest in 3D seismic volume first to create its time structure map. The second step is creating depth structure of the formation of interest from its time structure map. In this application geostatistical methods have been used for depth conversional, although other methods could be used instead. The third step is extraction of a number of attributes, such as phase, frequency and amplitudes, from the time structure map. The next step is to project the fracture density onto the top of the target formation. The final step is to combine these attributes using a statistical method known as Multi-variant non-linear regression to predict fracture density.

A method includes receiving information for a subsurface region; based at least in part on the information, identifying sub-regions within the subsurface region; assigning individual identified sub-regions a dimensionality of a plurality of different dimensionalities that correspond to a plurality of different models; via a model-based computational framework, generating at least one result for at least one of the individual identified sub-regions based at least in part on at least one assigned dimensionality; and consolidating the at least one result for multiple sub-regions.