TY - JOUR
T1 - Iterative multiscale gradient computation for heterogeneous subsurface flow
AU - Jesus de Moraes, Rafael
AU - de Zeeuw, Wessel
AU - R. P. Rodrigues, José
AU - Hajibeygi, Hadi
AU - Jansen, Jan Dirk
PY - 2019
Y1 - 2019
N2 - We introduce a semi-analytical iterative multiscale derivative computation methodology that allows for error control and reduction to any desired accuracy, up to fine-scale precision. The model responses are computed by the multiscale forward simulation of flow in heterogeneous porous media. The derivative computation method is based on the augmentation of the model equation and state vectors with the smoothing stage defined by the iterative multiscale method. In the formulation, we avoid additional complexity involved in computing partial derivatives associated to the smoothing step. We account for it as an approximate derivative computation stage. The numerical experiments illustrate how the newly introduced derivative method computes misfit objective function gradients that converge to fine-scale one as the iterative multiscale residual converges. The robustness of the methodology is investigated for test cases with high contrast permeability fields. The iterative multiscale gradient method casts a promising approach, with minimal accuracy-efficiency tradeoff, for large-scale heterogeneous porous media optimization problems.
AB - We introduce a semi-analytical iterative multiscale derivative computation methodology that allows for error control and reduction to any desired accuracy, up to fine-scale precision. The model responses are computed by the multiscale forward simulation of flow in heterogeneous porous media. The derivative computation method is based on the augmentation of the model equation and state vectors with the smoothing stage defined by the iterative multiscale method. In the formulation, we avoid additional complexity involved in computing partial derivatives associated to the smoothing step. We account for it as an approximate derivative computation stage. The numerical experiments illustrate how the newly introduced derivative method computes misfit objective function gradients that converge to fine-scale one as the iterative multiscale residual converges. The robustness of the methodology is investigated for test cases with high contrast permeability fields. The iterative multiscale gradient method casts a promising approach, with minimal accuracy-efficiency tradeoff, for large-scale heterogeneous porous media optimization problems.
KW - Adjoint method
KW - Direct method
KW - Gradient computation
KW - Iterative multiscale finite volume
KW - Multiscale methods
KW - Subsurface flow
UR - http://www.scopus.com/inward/record.url?scp=85066415088&partnerID=8YFLogxK
U2 - 10.1016/j.advwatres.2019.05.016
DO - 10.1016/j.advwatres.2019.05.016
M3 - Article
SN - 0309-1708
VL - 129
SP - 210
EP - 221
JO - Advances in Water Resources
JF - Advances in Water Resources
ER -