Constraint reduction procedures for reduced-order subsurface flow models based on POD-TPWL

2015 ◽  
Vol 103 (1) ◽  
pp. 1-30 ◽  
Author(s):  
J. He ◽  
L. J. Durlofsky
Keyword(s):  
Subsurface Flow ◽  
Constraint Reduction ◽  
Reduced Order ◽  
Flow Models

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.

Clustered iterative stochastic ensemble method for multi-modal calibration of subsurface flow models

2013 ◽  
Vol 491 ◽  
pp. 40-55 ◽  
Author(s):  
Ahmed H. Elsheikh ◽  
Mary F. Wheeler ◽  
Ibrahim Hoteit
Keyword(s):  
Subsurface Flow ◽  
Ensemble Method ◽  
Flow Models

Parameter estimation of subsurface flow models using iterative regularized ensemble Kalman filter

2012 ◽  
Vol 27 (4) ◽  
pp. 877-897 ◽  
Author(s):  
A. H. ELSheikh ◽  
C. C. Pain ◽  
F. Fang ◽  
J. L. M. A. Gomes ◽  
I. M. Navon
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Subsurface Flow ◽  
Flow Models

Reduced order models for many-query subsurface flow applications

2013 ◽  
Vol 17 (4) ◽  
pp. 705-721 ◽  
Author(s):  
George Shu Heng Pau ◽  
Yingqi Zhang ◽  
Stefan Finsterle
Keyword(s):  
Subsurface Flow ◽  
Reduced Order Models ◽  
Reduced Order

scholarly journals A generalized constraint reduction method for reduced order MBS models

2016 ◽  
Vol 41 (3) ◽  
pp. 259-274 ◽  
Author(s):  
Daniel Stadlmayr ◽  
Wolfgang Witteveen ◽  
Wolfgang Steiner
Keyword(s):  
Reduction Method ◽  
Constraint Reduction ◽  
Reduced Order ◽  
Generalized Constraint

Boosting iterative stochastic ensemble method for nonlinear calibration of subsurface flow models

2013 ◽  
Vol 259 ◽  
pp. 10-23 ◽  
Author(s):  
Ahmed H. Elsheikh ◽  
Reza Tavakoli ◽  
Mary F. Wheeler ◽  
Ibrahim Hoteit
Keyword(s):  
Subsurface Flow ◽  
Ensemble Method ◽  
Flow Models ◽  
Nonlinear Calibration
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