Computing device, computer instructions and method for identifying seismic traces prone to cycle-skipping in a full waveform inversion method. The method includes receiving recorded seismic data recorded with seismic sensors over a subsurface of interest; selecting a model that describes the subsurface; calculating, based on the model and the recorded seismic data, estimated seismic data; and choosing a probabilistic measure that characterizes a relationship between the recorded seismic data and the estimated seismic data. The probabilistic measure includes at least one statistical function.

Techniques to avoid a cycle skip in conjunction with a full waveform inversion are disclosed herein. A method includes selecting a first objective function of a full waveform inversion (FWI) from a set of objective functions, selecting a second objective function of the FWI from the set of objective functions, calculating a first misfit based upon the first objective function using modeled data with respect to observed data, calculating a first search direction based upon the first misfit between the modeled data and the observed data, calculating a second misfit based upon the second objective function using the modeled data with respect to the observed data, calculating a second search direction based upon the second misfit between the modeled data and the observed data, combining the first search direction with the second direction and computing an update to the modeled data based upon the first search direction and the second search direction combination.

A method is described for seismic depth uncertainty analysis including receiving wavelet basis functions and cutoff thresholds and randomly perturbing wavelet coefficients in reduced wavelet space based on the wavelet basis functions and the cutoff thresholds to generate a plurality of random wavelet fields; receiving a reference model in a depth domain; transforming the plurality of random wavelet fields to the depth domain and combining them with the reference model to form candidate models; performing a hierarchical Bayesian modeling with Markov Chain Monte Carlo (MCMC) sampling methods using the candidate models as input to generate a plurality of realizations; and computing statistics of the plurality of realizations to estimate depth uncertainty. The method may be executed by a computer system.

A system and methods for determining an updated geophysical model of a subterranean region of interest are disclosed. The method includes obtaining a preprocessed observed geophysical dataset based, at least in part, on an observed geophysical dataset of the subterranean region of interest, and forming a training dataset composed of a plurality of geophysical training models and corresponding simulated geophysical training datasets. The method further includes iteratively determining a simulated geophysical dataset from a current geophysical model, determining a data loss function between the preprocessed observed geophysical dataset and the simulated geophysical dataset, training a machine learning (ML) network, using the training dataset, to predict a predicted geophysical model and determining a model loss function between the current and predicted geophysical models. The method still further includes updating the current geophysical model based on an inversion using the data loss and model loss functions.

A method is described for estimating depth uncertainty including receiving seismic data, a reference model, and trial model realizations; generating realization gathers from the trial model realizations; generating reference gathers from the reference model; determining a reference data fit based on the reference gathers and a data fit for trial models based on the realization gathers; selecting refined models from the trial model realizations based on the reference data fit, the data fit for trial models, and a data fit tolerance criterion; and calculating depth uncertainty based on statistics of the refined models. The method may be executed by a computer system.

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 and apparatus for modeling a subsurface region, including: obtaining a training set of geologically plausible models for the subsurface region; training an autoencoder with the training set; extracting a decoder from the trained autoencoder, wherein the decoder comprises a geologic-model-generating function; using the decoder within a data-fitting objective function to replace output-space variables of the decoder with latent-space variables, wherein a dimensionality of the output-space variables is greater than a dimensionality of the latent-space variables; and performing an inversion by identifying one or more minima of the data-fitting objective function to generate a set of prospective latent-space models for the subsurface region; and using the decoder to convert each of the prospective latent-space models to a respective output-space model. A method and apparatus for making one or more hydrocarbon management decisions based on the estimated uncertainty.

Systems and methods are disclosed for generating elastic property data as a function of position and time. Exemplary implementations may include obtaining a first initial elastic model and a first set of elastic parameters, obtaining training subsurface data and a first training elastic property dataset, generating a first conditioned elastic model, and storing the first conditioned elastic model.

A system includes a processor and a memory. The memory includes instructions that are executable by the processor to access a plurality of seismic images of a subterranean formation in a first geological area. The instructions are also executable to generate a plurality of fault estimates from each of the plurality of seismic images. Further, the instructions are executable to generate a processed seismic image of the first geological area by normalizing and merging the plurality of seismic images and the plurality of fault estimates. Additionally, the instructions are executable to generate a statistical fault uncertainty volume of the first geological area using the processed seismic image. Furthermore, the instructions are executable to control a drilling operation in the first geological area using the statistical fault uncertainty volume of the first geological area.

Techniques for controlling a hydrocarbon production system include determining a first estimate of a prior FVC detectability probability map based on a plurality of reservoir data that includes four-dimensional (4D) seismic data of a subterranean reservoir; determining a second estimate of the prior FVC detectability probability map under seismic data noise conditions; determining an updated detectable FVC probability based on the 4D seismic data; determining an updated FVC probability based on the updated detectable FVC probability and the first and second estimates of the prior FVC detectability probability maps; and generating a control instruction for at least one of a fluid injection system or a hydrocarbon production assembly based on the updated FVC probability.