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Abstract

ulation are increasingly included in best-practice workflows in the Oil & Gas industry. Most optimization methods applied to model validation in reservoir simulation, including so-called Evolutionary Algorithms like Genetic Algorithms (GA) and Evolution Strategies (ES), use an objective function definition based on the overall simulation period. The integration of a sequential data assimilation process is conceptually not embedded in those optimization methods. The Ensemble Kalman Filter (EnKF) has entered this field for its appealing features. Sequential data assimilation allows the implementation of real-time model updates where classical optimization techniques require simulating the complete history period. This may have negative effects on efficiency and use of computing time. In contrast, EnKF sequentially assimilates information streams into a set of numerical models. While being a special case of a fully fledged particle filter the EnKF method with application to reservoir simulation has proven to generate results with a reasonable amount of ensemble members. The similarities between a particle filter (Monte Carlo Filter) and an Evolutionary Algorithm (Generic Algorithm) have been previously pointed out from a rather theoretical point of view (1,2). In this work we present a concise overview of Evolutionary Algorithms and the Ensemble Kalman Filter in such a way that the cross-relations become apparent. Similarities are highlighted and the potential for hybrid couplings is discussed. Practical implications for the implementation of these methods are derived. 1. Higuchi, Tomoyuki. Monte carlo filter using the genetic algorithm operators. Journal of Statistical Computation and Simulation. 1997, Vol. 1, 59, pp. 1-23. 2. —. Self-organizing Time Series Model. [book auth.] A. Doucet, J.F.G. de Freitas and N.J. Gordon. Sequential Monte Carlo Methods in Practice. s.l. : Springer, 2001, pp. 429-444.

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/content/papers/10.3997/2214-4609.20146438
2008-09-08
2020-10-22
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