Systems and methods that include receiving reservoir data of a hydrocarbon reservoir, receive an indication related to selection of a wavefield propagator, application of the wavefield propagator utilizing Fourier Finite Transforms and Finite Differences to model a wavefield associated with a Tilted Orthorhombic media representative of a region of a subsurface comprising the hydrocarbon reservoir, and processing the reservoir data in conjunction the wavefield propagator to generate an output for use with seismic exploration above a region of a subsurface comprising the hydrocarbon reservoir and containing structural or stratigraphic features conducive to a presence, migration, or accumulation of hydrocarbons.

Methods, systems, and devices for downhole evaluation using a sensor assembly that includes a sensor plate, wherein a surface of the sensor plate forms a portion of a surface of a downhole tool. Methods include bringing the surface of the sensor plate into contact with downhole fluid; generating a guided wave that propagates in the sensor plate by activating the sensor assembly at at least one frequency configured to excite both a symmetric mode and an anti-symmetric mode; making at least one first attenuation measurement of the symmetric mode of the guided wave; making at least one second attenuation measurement of the anti-symmetric mode of the guided wave; and using the at least one first attenuation measurement and the at least one second attenuation measurement to estimate at least one parameter of interest of the fluid. Methods may include submerging the surface of the sensor plate in a downhole fluid.

A computer-implemented method for obtaining reconstructed seismic data for determining a subsurface feature, includes: determining an initial training velocity model, training a machine learning model based on first training seismic data and second training seismic data generated from the training velocity model, the first training seismic data corresponding to one or more first frequencies, the second training seismic data corresponding to one or more second frequencies lower than the one or more first frequencies, obtaining, based on measured seismic data and the machine learning model, reconstructed seismic data corresponding to the one or more second frequencies, generating a velocity model based on the measured seismic data, the reconstructed seismic data, and a full waveform inversion (FWI), and when the generated velocity model does not satisfy a preset condition, updating the training velocity model based on the generated velocity model, to obtain updated reconstructed seismic data for determining a subsurface feature.

A method, and a system for implementing the method, are disclosed wherein coordinates of survey region are used to locate small pieces of a seismic wave model, usually defined by their nodes (or vertices) and contain information about physical properties, such as liquid or solid, density, velocity that seismic waves propagates in it; and connects them to the appropriate source and receiver sensor. In particular, the method and system disclosed, generates a multi-layer mapping of the survey region by decomposing the survey region into cubes containing small pieces of seismic wave models (the elements), as well as source and receiver location. Those cubes are then indexed depending upon their location and the elements, sources and receivers are assigned to a particular cube thereby creating a multi-layer relationship between the survey region map, the cube map, the elements map, as well as the source and receiver locations.

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 present invention relates to a method of modelling a sedimentary basin by means of a numerical basin simulation solving at least a balance equation of poromechanics according to a face-based smoothed finite-element method for determining at least a stress field and a deformation field. The method according to the invention notably comprises the following steps: subdividing the hexahedral cells of a mesh representation of a state of the basin into at least eight hexahedral subcells, determining a transition relation between the degrees of freedom of the nodes of the subcells and the degrees of freedom of the nodes of the cell to which the subcells belong, and determining a stiffness and nodal forces from at least this transition relation and a strain-displacement relation determined for the subcells.

A method for partial differential equation inversion, the method including receiving measured data do; selecting an objective function having first and second measures N.sub.1 and N.sub.2, wherein the objective function depends on three independent variables V, u, and f, V being a perturbation of a wave equation operator L from a background operator L.sub.0, u being a wavefield that satisfies the wave equation operator L, and f being a source function that describes the source of the waves; optimizing with a processor the objective function by finding a minimum or a maximum using the inversion; calculating with the processor solutions V*, u*, and f* of the three independent variables V, u, and f; and generating with the processor an image of an object based on the solutions V*, u*, and f*.

A computer-implemented method for obtaining reconstructed seismic data for determining a subsurface feature, includes: determining an initial training velocity model, training a machine learning model based on first training seismic data and second training seismic data generated from the training velocity model, the first training seismic data corresponding to one or more first frequencies, the second training seismic data corresponding to one or more second frequencies lower than the one or more first frequencies, obtaining, based on measured seismic data and the machine learning model, reconstructed seismic data corresponding to the one or more second frequencies, generating a velocity model based on the measured seismic data, the reconstructed seismic data, and a full waveform inversion (FWI), and when the generated velocity model does not satisfy a preset condition, updating the training velocity model based on the generated velocity model, to obtain updated reconstructed seismic data for determining a subsurface feature.

The presently disclosed seismic acquisition technique employs a receiver array and a processing methodology that are designed to attenuate the naturally occurring seismic background noise recorded along with the seismic data during the acquisition. The approach leverages the knowledge that naturally occurring seismic background noise moves with a slower phase velocity than the seismic signals used for imaging and inversion and, in some embodiments, may arrive from particular preferred directions. The disclosed technique comprises two steps: 1) determining from the naturally occurring seismic background noise in the preliminary seismic data a range of phase velocities and amplitudes that contain primarily noise and the degree to which that noise needs to be attenuated, and 2) designing an acquisition and processing method to attenuate that noise relative to the desired signal.

A method, system and non-transitory computer-readable medium for forming a seismic image of a geological structure are provided. After obtaining seismic wave data including a plurality of seismic wave traces at a first region of the geological structure, a predicted time dispersion error of an actual time dispersion error that results from a use of a finite difference approximation in calculating predicted seismic wave data at a second region of the geological structure as if a seismic wave propagates from the first region to the second region of the geological structure, is calculated. A corrected predicted seismic wave data at the second region of the geological structure is calculated by applying the finite difference approximation to the seismic wave data at the first region of the geological structure compensated with the predicted time dispersion error. A seismic image of the second region of the geological structure is generated using the corrected predicted seismic wave data, such that the actual time dispersion error is negated by the predicted time dispersion error.