The world's energy consumption has risen geometrically over the last three decades due to advancement in technology. In response to this ever increasing rising demand, other sources of energy have been explored. Reports show that fossil fuels (crude oil and natural gas) continue to take the lead despite these efforts. Hence, the Oil and Gas industry has put in a lot of technical measures to meet up with this high energy demand. Many works have been done on how to increase reserves and ultimately increase recovery but little has been achieved in this area. Therefore, a more reliable, efficient and effective way of enhancing recovery is necessary to make headway in countering this challenge. This research seeks to provide the feasibility of enhanced oil recovery (EOR) projects using high level data mining technology in the African Oil Producing Regions.

Data mining is a process which finds useful patterns from large amount of data. Data analysis process involves data exploration, pattern identification and pattern deployment to make accurate judgement necessary for EOR investment decisions. It provides a significant reduction in the level of uncertainty when compared with other existing techniques. In our concept, artificial intelligence and genetic algorithm was employed and recommendation made.

Results show that data mining technique is a robust tool for making EOR investment decisions, right from the early life of a field. Thus, this concept makes it more economical to execute EOR projects with lower level of uncertainties. Marginal fields can also be invested upon using data mining techniques.

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