Chevron U.s.a. Inc.

Maisha Lara Amaru

Tao Sun

Ashley D Harris

Morgan David Sullivan

Lewis Li

Fabien J Laugier

Brett M Hern

Brian J Willis

Cory James Hoelting

Ke Wang

Sebastien B Strebelle

Andrey H Shabelansky

Huafeng Liu

Marek Kacewicz

Jinsong Chen

Lisa Renee′ Goggin

Ying Tan

Min Yang

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.

System and method for seismic depth uncertainty analysis

A computational stratigraphy model may be run for M mini-steps to simulate changes in a subsurface representation across M mini-steps (from 0-th subsurface representation to M-th subsurface representation), with a mini-step corresponding to a mini-time duration. The subsurface representation after individual steps may be characterized by a set of computational stratigraphy model variables. Some or all of the computational stratigraphy model variables from running of the computational stratigraphy model may be provided as input to a machine learning model. The machine learning model may predict changes to the subsurface representation over a step corresponding to a time duration longer than the mini-time duration and output a predicted subsurface representation. The subsurface representation may be updated based on the predicted subsurface representation outputted by the machine learning model. Running of the computational stratigraphy model and usage of the machine learning model may be iterated until the end of the simulation.

System and method for accelerated computation of subsurface representations

A method is described for analysis of subsurface data including the use of physics-based modeling and experimental design that allows calculation of probabilities of physical subsurface properties. The method may include calculations of key controlling parameters. The method may include using multiple dimension scaling. The method may be executed by a computer system.

System and method for analysis of subsurface data

A subsurface representation may define simulated subsurface configuration of a simulated subsurface region. The simulated subsurface region may include simulated wells, and the simulated subsurface configuration may define simulated correlation between the simulated wells. Subsurface configuration of wells may be compared with the simulated subsurface configuration to generate similarity maps for the wells. Simulated wells may be matched to the wells based on the similarity maps and the arrangement of the wells. Correlation between the wells may be determined based on the simulated correlation between the matched simulated wells.

Correlation of multiple wells using subsurface representation

Correlation matrices may be used to simultaneously correlate multiple wells. A correlation matrix may be generated for individual pairs of multiple wells. The values of elements of the correlation matrices may be determined based on matching between segments of the multiple wells and segments of one or more computational stratigraphic models. An N-dimensional space including an axis for individual wells may be generated. Directed walk may be performed within the N-dimensional space to generate paths representing scenarios of correlations for segments of the multiple wells.

Correlation matrix for simultaneously correlating multiple wells

A method is described for seismic imaging that may include receiving digital seismic data; processing the digital seismic data to create a digital seismic image in a seismic domain; flattening the digital seismic image to generate a digital flattened image; identifying artifacts in the digital flattened image; transforming the artifacts back into the seismic domain; and reprocessing the digital seismic data based on the artifacts in the seismic domain to generate a digital image with reduced artifacts. The method may be executed by a computer system.

System and method for identifying artifacts in seismic images

One embodiment of generating a pore pressure prediction through integration of seismic data and basin modeling includes crossplotting seismically derived velocities and effective stress at spatial coordinates; defining seismic transform functions and an uncertainty range from the crossplotting; transforming the seismically derived velocities into calculated effective stress using selected seismic transform functions and calculating pore pressure using an equation transforming the calculated effective stress into calculated pore pressure; identifying a subset of the selected seismic transform functions, where the subset is identified in response to the calculated pore pressure being adequate based on a comparison; using an inverse of the subset to convert the effective stress from the basin model into basin model derived velocities; building a hybrid velocity model by selecting velocities from the basin model derived velocities or from the seismically derived velocities in each region; and generating a digital seismic image using the hybrid velocity model.

Pore pressure prediction based on an integrated seismic and basin modeling approach

Embodiments of a method for correcting velocity models for complex topographies are disclosed herein. In general, embodiments of the method utilize velocity corrections based on geomechanical effects to correct a velocity model to take into account complex surface topographies. In particular, embodiments of the method use a nucleus strain theory to determine the velocity corrections. Further details and advantages of various embodiments of the method are described in more detail herein.

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Houston, TX, United States of America

Maisha Lara Amaru