Development and application of reduced-order modeling procedures for subsurface flow simulation

2009 ◽  
Vol 77 (9) ◽  
pp. 1322-1350 ◽  
Author(s):  
M. A. Cardoso ◽  
L. J. Durlofsky ◽  
P. Sarma
Keyword(s):  
Subsurface Flow ◽  
Flow Simulation ◽  
Reduced Order Modeling ◽  
Reduced Order

Hyper-Reduced-Order Models for Subsurface Flow Simulation

SPE Journal ◽  
2016 ◽  
Vol 21 (06) ◽  
pp. 2128-2140 ◽  
Author(s):  
Seonkyoo Yoon ◽  
Zeid M. Alghareeb ◽  
John R. Williams
Keyword(s):  
Subsurface Flow ◽  
Flow Simulation ◽  
Reduced Order Modeling ◽  
Production Optimization ◽  
Galerkin Projection ◽  
Flow Modeling ◽  
State Reduction ◽  
Constraint Reduction ◽  
Reduced Order ◽  
Modeling Procedure

Summary Subsurface flow modeling is an indispensable task for reservoir management, but the associated computational cost is burdensome because of model complexity and the fact that many simulation runs are required for its applications such as production optimization, uncertainty quantification, and history matching. To relieve the computational burden in reservoir flow modeling, a reduced-order modeling procedure based on hyper-reduction is presented. The procedure consists of three components: state reduction, constraint reduction, and nonlinearity treatment. State reduction based on proper orthogonal decomposition (POD) is considered, and the impact of state reduction, with different strategies for collecting snapshots, on accuracy and predictability is investigated. Petrov-Galerkin projection is used for constraint reduction, and a hyper-reduction that couples the Petrov-Galerkin projection and a “gappy” reconstruction is applied for the nonlinearity treatment. The hyper-reduction method is a Gauss-Newton framework with approximated tensors (GNAT), and the main contribution of this study is the presentation of a procedure for applying the method to subsurface flow simulation. A fully implicit oil/water two-phase subsurface flow model in 3D space is considered, and the application of the proposed hyper-reduced-order modeling procedure achieves a runtime speedup of more than 300 relative to the full-order method, which cannot be achieved when only constraint reduction is adopted.

Enhanced linearized reduced-order models for subsurface flow simulation

2011 ◽  
Vol 230 (23) ◽  
pp. 8313-8341 ◽  
Author(s):  
J. He ◽  
J. Sætrom ◽  
L.J. Durlofsky
Keyword(s):  
Subsurface Flow ◽  
Flow Simulation ◽  
Reduced Order Models ◽  
Reduced Order

Lattice Boltzmann Flow Simulations With Applications of Reduced Order Modeling Techniques.

2014 ◽  
Author(s):  
Donald L. Brown ◽  
Jun Li ◽  
Victor M. Calo ◽  
Mehdi Ghommem ◽  
Yalchin Efendiev
Keyword(s):  
Lattice Boltzmann ◽  
Reduced Order Modeling ◽  
Reduced Order ◽  
Flow Simulations ◽  
Modeling Techniques
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