A Model with Back-Propagation Algorithm for the Estimation of Irreducible Water Saturation


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As one of the most important reservoir parameters, irreducible water saturation, Swir, is a key parameter in evaluating multi-phase flow, as well as its importance in defining oil in-place. Residual oil saturation, the target of tertiary recovery, is also a function of Swir. In traditionally, Swir is determined by conducting capillary pressure experiments, requiring considerable resources and long time periods, with the consequence of a limited number of core plug evaluations for a particular reservoir. Thus, the estimation of Swir with mathematical models is developed in recent years. The study reported in this paper uses artificial neural network to determine Swir. The optimal model is chosen among 25 simulations, subtilizing different combinations of hidden layer nodes and activation functions for the hidden and output layers. Its performance is compared with other conventional models, demonstrating the superior performance of the proposed Swir prediction models.



Advanced Materials Research (Volumes 361-363)

Edited by:

Qunjie Xu, Honghua Ge and Junxi Zhang






P. H. Ma et al., "A Model with Back-Propagation Algorithm for the Estimation of Irreducible Water Saturation", Advanced Materials Research, Vols. 361-363, pp. 445-450, 2012

Online since:

October 2011




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