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Multiscale Gradient Computation for Subsurface Flow Models
Author(s) -
Rafael J. de Moraes,
José Roberto Pereira Rodrigues,
Hadi Hajibeygi,
J. D. Jansen
Publication year - 2016
Publication title -
proceedings
Language(s) - English
Resource type - Conference proceedings
ISSN - 2214-4609
DOI - 10.3997/2214-4609.201601891
Subject(s) - computation , computer science , mathematical optimization , automatic differentiation , mathematics , operator (biology) , reservoir simulation , algorithm , scale (ratio) , algebraic number , flow (mathematics) , balanced flow , matching (statistics) , derivative (finance) , partial derivative , computational science , mathematical analysis , geometry , quantum mechanics , gene , economics , repressor , chemistry , biochemistry , statistics , financial economics , transcription factor , thermodynamics , physics
We present an efficient multiscale (MS) gradient computation that is suitable for reservoir management studies involving optimization techniques for, e.g., computer-assisted history matching or life-cycle production optimization. The general, algebraic framework allows for the calculation of gradients using both the Direct and Adjoint derivative methods. The framework also allows for the utilization of any MS formulation in the forward reservoir simulation that can be algebraically expressed in terms of a restriction and a prolongation operator. In the implementation, extra partial derivative information required by the gradient methods is computed via automatic differentiation. Numerical experiments demonstrate the accuracy of the method compared against those based on fine-scale simulation (industry standard).

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