Schlumberger Technology Corporation

Chevron U.s.a. Inc.

Chevron Limited

Total

Services Petroliers Schlumberger

Prad Research and Development Limited

Logined B.v.

Jostein Natvig

Kyrre Bratvedt

Shingo Watanabe

Zhuoyi Li

Dominic Walsh

Carlos Boneti

Hadi Hajibeygi

Alexander Lukyanov

Knut-Andreas Lie

Antonina Kozlova

Sindhu Chittireddy

Pavel Tomin

Olav Moyner

A subsurface representation may define subsurface configuration of a subsurface region. A grid connectivity graph for the subsurface representation may include (1) nodes that represent cells within the subsurface representation, and (2) edges between the nodes that represent connectivity between the cells within the subsurface representation. The grid connectivity graph may be filtered to remove edges that do not satisfy a connectivity criterion. The filtered grid connectivity graph may be used to compute a linear solver preconditioner that improves the performance of the subsurface simulation.

Acceleration of subsurface simulation using filtered grid connectivity graph

Systems, computer-readable media, and methods for performing a reservoir simulation by obtaining reservoir data; translating the reservoir data into grid properties to create a grid; dividing the grid into domains; generating coarse grids corresponding to each domain; processing the domains, where processing a domain includes: calculating pressure for the domain using a coarse grid corresponding to the domain, calculating flux for the domain using a coarse grid corresponding to the domain, and calculating transport of fluids for the domain using a coarse grid corresponding to the domain; and generating a reservoir simulation corresponding to the grid based on processing each domain. The domains can be processed in parallel on different computer systems, different processors, or different cores.

Parallel multiscale reservoir simulation

Methods, computing systems, and computer-readable media for multi-scale modeling. The method includes determining a first matrix for a plurality of fine cells of a model based at least in part on a physical property value represented by respective fine cells, identifying one or more overlapped cells of the plurality of fine cells that are part of at least two of the plurality of subdomains, and determining a second matrix. Determining the second matrix includes determining an intermediate product by multiplying the first matrix by a prolongation matrix, which includes predicting a row of zeros in the intermediate product for the plurality of fine cells that are not the one or more overlapped cells and are not part of the at least two of the plurality of subdomains that include the one or more overlapped cells. Determining the second matrix also includes multiplying the intermediate product by a restriction matrix.

Multiscale method for reservoir models

Computer-implemented systems and methods for modeling behavior of at least one fluid in a reservoir are disclosed. The techniques can include obtaining measurements of physical parameters, including pressure, at locations within the reservoir, and discretizing, based on a three-dimensional fine grid, a system of partial differential mass balance equations that model, based on the measurements, at least the physical parameters at the locations within the reservoir, such that a system of nonlinear equations is produced. The techniques can include iterating from a current time step to a next time step, such that a solution to the system of nonlinear equations for a time interval that includes the current time step and the next time step is produced. The iterating can include an adaptive multi-fidelity multiscale technique that employs multiple restriction operators, prolongation operators, and coarse grids, to model various computationally challenging reservoir features, behaviors, or both.

Adaptive multiscale multi-fidelity reservoir simulation

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Oslo, Norway

Jostein Natvig