Chevron U.s.a. Inc.

Sebastien B Strebelle

Tao Sun

Lewis Li

Julian Arthur Thorne

Michael James Pyrcz

Maisha Lara Amaru

Ashley D Harris

Andrew Royle

Claude Edward Cavelius

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

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.

Generation of subsurface representations using layer-space

A computer implemented method for identifying reservoir facies in a subsurface region includes obtaining a set of seismic data points of both petrophysical and geophysical parameters relating to the subsurface region, identifying one or more correlated clusters of petrophysical parameters, generating, from the one or more correlated clusters of petrophysical parameters, one or more corresponding multi-dimensional clusters of seismic data points, storing, in a facies database, a multi-dimensional cluster center point for at least one multi-dimensional clusters, and recursively splitting the multi-dimensional clusters into distinct sub-clusters of seismic data points corresponding to facies types.

System and method for reservoir facies classification that segments both petrophysical and geophysical properties

A computer system and a computer-implemented method for optimizing the number of conditioning data used in multiple point statistics simulation. The method includes inputting a training image representative of subsurface geological heterogeneity; and inputting an initial conservative number of conditioning data. The method further includes selecting a geometry of a template wherein a size of the template is defined by the conservative number of conditioning data; building a search tree using the template by scanning the training image with the template and storing data patterns present in the training image in the search tree to obtain a plurality of patterns; and determining a threshold number of conditioning data smaller than the initial conservative number of conditioning data beyond which estimated facies probabilities are not significantly modified by additional number of conditioning data.

System and method for optimizing the number of conditioning data in multiple point statistics simulation

A computer-implemented method and system for simulating reservoir property trends, including petrophysical trends within facies geobodies, is provided. One embodiment includes discretizing a range of values of the reservoir property to be modeled into at least two categories and utilizing the categories to discretize the associated well data and training image into categorical well data and a categorical training image. An embodiment includes applying MPS simulation using the categorical training image on a stratigraphic grid of nodes discretizing the reservoir volume to create a categorical property model. The embodiment additionally includes applying variogram-based simulation in each sub-region of the categorical property model to create a reservoir property model that reproduces the category histogram of each associated sub-region and models the reservoir property trends.

Method and system for using multiple-point statistics simulation to model reservoir property trends

An enhanced multi-point statistical (MPS) simulation is disclosed. A multiple-grid simulation approach is used which has been modified from a conventional MPS approach to decrease the size of a data search template, saving a significant amount of memory and cpu-time during the simulation. Features used to decrease the size of the data search template include: (1) using intermediary sub-grids in the multiple-grid simulation approach, and (2) selecting a data template that is preferentially constituted by previously simulated nodes. The combination of these features allows saving a significant amount of memory and cpu-time over previous MPS algorithms, yet ensures that large-scale training structures are captured and exported to the simulation exercise.

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Houston, TX, United States of America

Sebastien B Strebelle