EOR screening using artificial intelligence Bayesian network

oil - production form enhanced oil recovery (EOR) projects continues supply an increasing in percentage of the world s oil. Therefore , the importance of choosing the best recovery method becomes increasingly important to petroleum engineers . In recent years computer technology has improved the application of screening criteria through the use of artificial intelligence techniques but the value of these programs depends on the accuracy of the input data used. bayesian network analysis is a powerful tool, which is already applied in some oil industry field. in this work we present screening criteria using bayesian network based on a combination of the reservoir and oil characteristics of successful projects plus generated data by taber table. we provide screening criteria for the six methods that are either the most important or still have some promise . the purpose is to develop software capable of combining the data extracted from different sources either experimental or modeling into a unified expert system in order to select the most proper technique for the situation. the efficiency is also checked with another set of data the accuracy of which in inside the acceptability margins.

کلیدواژه ها:

bayesian ، enhanced oil recovery (EOR) ، screening