A Model with Back-Propagation Algorithm for Recovery Efficiency Forecast of Carbon Dioxide Flooding

Abstract:

As one of the most important displacements in producing petroleum, CO2 flooding has been developed for nearly 50 years around the world in order to improve the tertiary recovery. The recovery efficiency, R, is a key parameter in the CO2 displacement of crude oil. Traditionally, R is determined by conducting CO2 flooding experiment, 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 R with mathematical models is developed in recent years, which also needs plenty of relevant parameters considered. The study reported in this paper uses artificial neural network to determine R. Five dimensionless variables are considered to analyse the CO2 immiscible displacement process. An optimal model is chosen with its suitable hidden layer nodes and activation functions for the hidden and output layers. Its performance is compared with the numerical simulation model, demonstrating the superior performance of the proposed R prediction model.

• Periodical: Applied Mechanics and Materials (Volumes 295-298)
• Pages: 3137-3141
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.295-298.3137
• Online since: February 2013
• Authors: Ping Hua Ma, Hong Fu Fan, Ke Li
• Keywords: Artificial Neural Network (ANN), Back Propagation Algorithm (BPA), Carbon Dioxide Flooding, Model, Recovery Efficiency Forecast

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