The main tool for screening of EOR techniques is generally based on the criteria presented in a variety of tables and graphs given in the literature. These data are derived from the basic theory of multiphase fluid flow through porous media, reservoir simulation, laboratory experiments and existing field-scale experiences. The purpose of this study is to develop a procedure capable of combining the data extracted from different sources ranging from worldwide field experiences to the existing tables into a unified expert system. This expert system is based on Bayesian network analysis in order to sort the proper EOR techniques for further assessment by economical and environmental criteria.
A data bank has been gathered from worldwide EOR/IOR techniques and analyzed using data mining procedure which is then combined with extracted data from previously published screening tables. Bayesian network quantitative learning technique was applied to different data combinations from the data bank to train the network which is to serve as the expert system.
The produced expert system is also applied to the gathered data pertaining to 10 Iranian southwest reservoirs. The results show that, CO2 flooding can be the most promising among various EOR techniques, which is in agreement with a previous work. According to this study, considering reservoir characteristics, and excluding the economic limitations, CO2 flooding is considered as the most efficient EOR method for Iranian carbonate reservoirs under study.
The results show that Bayesian Belief network analysis can be successful in the prediction of proper EOR technique by providing sufficient Data to train the network.