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Surrogate modeling-based optimization of SAGD processes by Nestor V Queipo; Javier V Goicochea; Salvador Pintos is a Engineering article available to read on EtoBox.

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This paper presents a solution methodology for the optimization of geometrical and operational parameters of SAGD processes in a heterogeneous and multiphase petroleum reservoir. The optimization refers to the maximization or minimization of performance measures such as net present value, cumulative oil production, or cumulative steam injected. The solution methodology includes the construction of a ''fast surrogate'' of an objective function whose evaluation involves the execution of a time-consuming mathematical model (i.e. reservoir numerical simulator) based on neural networks, DACE modeling, and adaptive sampling. Using adaptive sampling, promising areas are searched considering the information provided by the surrogate model and the expected value of the errors. The proposed methodology provides a global optimization method, hence avoiding the potential problem of convergence to a local minimum in the objective function exhibited by the commonly Gauss -Newton methods. Furthermore, it exhibits an affordable computational cost, is amenable to parallel processing, and is expected to outperform other general-purpose global optimization methods such as simulated annealing, genetic

Who reads Surrogate modeling-based optimization of SAGD processes?

Author

Nestor V Queipo; Javier V Goicochea; Salvador Pintos

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0920-4105)

Published

2002

Language

EN

Field

Engineering (Physical Sciences)