There is provided herein a system and method of seismic data collection for land and marine data that utilizes narrowband to monochromatic low-frequency non-impulsive sources designed to optimize the ability of migration/inversion algorithms to image the subsurface of the Earth, in particular, full-waveform inversion.

A method including: generating an updated subsurface property model of a subsurface region, with a computer, from an initial estimate of the subsurface property model by performing an iterative inversion, which includes inverting geophysical data to infer the updated subsurface property model, wherein the generating the updated subsurface property model includes linearly remapping the initial estimate of the subsurface property model based on an inverse of a regularization operator, included with the initial estimate of the subsurface property model, into one in which the regularization operator is represented by an identity matrix, performing a unitary matrix decomposition in order to group the identity matrix with sparse matrices output from the unitary matrix decomposition, and performing a search over at least one trade-off parameter to reduce a misfit between simulated data generated from a most recent estimate of the subsurface property model and the geophysical data until a predetermined stopping criteria is satisfied; and generating, with a computer, an image of the subsurface region using the updated subsurface property model.

A method for detecting an unknown fault in a target seismic volume. The method includes generating a number of patches from a training seismic volume that is separate from the target seismic volume, where a patch includes a set of training areas, generating a label for assigning to the patch, where the label represents a subset, of the set of training areas, intersected by an known fault specified by a user in the training seismic volume, training, during a training phase and based at least on the label and the training seismic volume, a machine learning model, and generating, by applying the machine learning model to the target seismic volume during a prediction phase subsequent to the training phase, a result to identify the unknown fault in the target seismic volume.

Methods for seismic imaging of a subterranean geological formation include receiving first acoustic impedance data, having a first resolution, associated with wells in a subsurface region. A system receives seismic data including second acoustic impedance data having a second resolution. The system performs a quality control process configured to identify a mismatch between the first acoustic impedance data and the second acoustic impedance data. The system resamples the second acoustic impedance data into a three-dimensional (3D) grid model. The system scales up the first acoustic impedance data into the 3D grid model. The system downscales the second acoustic impedance data controlled by the first acoustic impedance data in the 3D grid model. The system generates third acoustic impedance data representing fine-scale impedance data. The system extrapolates the fine-scale impedance into areas or regions having no seismic data coverage or poor seismic data coverage.

A method of identifying at least one fluid migration feature comprises receiving seismic data and processing the seismic data to identify the at least one fluid migration feature, wherein the seismic data comprises speed or velocity data and/or seismic amplitude data.

A computer-implemented method for integrating a full wavefield inversion (FWI) solution with a non-FWI solution. Computational costs for generating a large bandwidth FWI solution, such as to 50 Hz, may be considerable. However, limiting the FWI solution to a narrower frequency band, such as up to 20 Hz, renders the FWI solution less useful. To remedy this, an FWI solution that is band limited, such as up to 20 Hz, is integrated with a non-FWI solution. The non-FWI solution may comprise seismic stacks or non-seismic data, and is directed to a different frequency band, with at least part of the non-FWI solution frequency band being greater than 20 Hz. In this way, bandwidth extension may be performed for at least one subsurface physical property parameter by combining a band limited FWI solution with a non-FWI solution.

Disclosed is a method of monitoring the behavior of a subsurface volume. The method comprises transforming a single discrete parameter or an ensemble of discrete parameters describing an attribute of the subsurface volume, each discrete parameter having N possible discrete values with N2, into N indicator parameters each having 2 possible discrete values; for each of the two value classes of each indicator parameter, determining the anisotropic distance to a value transition interface; transforming each of the indicator parameters into a corresponding continuous parameter using the determined anisotropic distance to the value transition interface; and using the continuous parameters in a history matching process.

Systems and methods using a time-seismic-depth interval velocity curve and the difference between a time-depth interval velocity curve and a time-seismic depth interval velocity curve for validating depth-depth curves which calibrate a synthetic generated from well logs to depth seismic data.

A method for determining organic richness of a formation is disclosed. The method involves obtaining a microresistivity image of the formation, obtaining acoustic logging data for the formation, fusing the microresistivity image with the acoustic logging data to generate a fused pseudo-acoustic image of the formation, and determining an organic richness image based on the fused pseudo-acoustic image. The difference between the fused pseudo-acoustic image and the microresistivity image indicates organic richness.

Systems and methods for selecting the best logging data for petrophysical modelling and completion optimization by analyzing sensitivity and errors in the logging data.