Chevron U.s.a. Inc.

Lewis Li

Tao Sun

Sebastien B Strebelle

Julian Arthur Thorne

Maisha Lara Amaru

Ashley D Harris

Shane James Prochnow

Morgan David Sullivan

Fabien J Laugier

Brett M Hern

Liliia Reddy

Petros Papazis

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

Methods and systems for predicting hydrocarbon production and production uncertainty are disclosed. Exemplary implementations may: obtain training data, the training data including (i) training production data, (ii) training engineering parameters, and (iii) a training set of geological parameters and corresponding training geological parameter uncertainty values; obtain an initial production model; generate a trained production model by training the initial production model; store the trained production model; obtain a target set of geological parameters and corresponding target geological parameter uncertainty values and target engineering parameters; apply the trained production model to generate a set of production values and corresponding production uncertainty values; generate a representation using visual effects to depict at least a portion of the set of production values and corresponding production uncertainty values as a function of position within the subsurface volume of interest; and display the representation.

Systems and methods for predicting hydrocarbon production and assessing prediction uncertainty

Systems and methods for estimating reservoir productivity as a function of position in a subsurface volume of interest are disclosed. Exemplary implementations may: obtain subsurface data and well data corresponding to a subsurface volume of interest; obtain a parameter model; use the subsurface data and the well data to generate multiple production parameter maps; apply the parameter model to the multiple production parameter maps to generate refined production parameter values; generate multiple refined production parameter graphs; display the multiple refined production parameter graphs; generate one or more user input options; receive a defined well design and the one or more user input options selected by a user to generate limited production parameter values; generate a representation of estimated reservoir productivity as a function of position in the subsurface volume of interest using the defined well design and visual effects; and display the representation.

Systems and methods for estimating a well design reservoir productivity as a function of position in a subsurface volume of interest based on a reservoir productivity parameter

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

Data in physical space may be converted to layer space before performing modeling to generate one or more subsurface representations. Computational stratigraphy model representations that define subsurface configurations as a function of depth in the physical space may be converted to the layer space so that the subsurface configurations are defined as a function of layers. Conditioning information that defines conditioning characteristics as the function of depth in the physical space may be converted to the layer space so that the conditioning characteristics are defined as the function of layers. Modeling may be performed in the layer space to generate subsurface representations within layer space, and the subsurface representations may be converted into the physical space.

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Houston, TX, United States of America

Lewis Li