Appendix Finite Element Solution for Flow of Immiscible Fluids in Petroleum Reservoirs

Kukreti, Anant R.; Civan, Faruk; Rajapaksa, Yatendra

doi:10.1016/s0376-7361(08)70469-2

Cited by 1 publication

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“…This approach is applied to the modeling of an oil reservoir whose characterization through normal simulation would require inhibitely large computation times. For example, a number of approaches (Subramanian et al , 2007; Jian et al , 2004; Kurerti et al , 1989; Lake, 1989) have been proposed for the characterization of such systems. However, they suffer from either long simulation times, or lack of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Model order reduction using neural network principal component analysis and generalized dimensional analysis

Bellamine

Elkamel

2008

Engineering Computations

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PurposeThis paper seeks to present a novel computational intelligence technique to generate concise neural network models for distributed dynamic systems.Design/methodology/approachThe approach used in this paper is based on artificial neural network architectures that incorporate linear and nonlinear principal component analysis, combined with generalized dimensional analysis.FindingsNeural network principal component analysis coupled with generalized dimensional analysis reduces input variable space by about 90 percent in the modeling of oil reservoirs. Once trained, the computation time is negligible and orders of magnitude faster than any traditional discretisation schemes such as fine‐mesh finite difference.Practical implicationsFinding the minimum number of input independent variables needed to characterize a system helps in extracting general rules about its behavior, and allows for quick setting of design guidelines, and particularly when evaluating changes in the physical properties of systems.Originality/valueThe methodology can be used to simulate dynamical systems characterized by differential equations, in an interactive CAD and optimization providing faster on‐line solutions and speeding up design guidelines.

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Section: Introductionmentioning

confidence: 99%