Development of an enhanced oil recovery screening system

Abstract

Selecting a suitable enhanced oil recovery (EOR) process for a given reservoir is a challenge for reservoir engineers because he or she has to compare pros and cons of all the available EOR methods in the context of each selected reservoir. The task becomes even more complex if they have to go through the details of hundreds of mature and depleted reservoirs to come up with a shortlist of EOR candidates for more detailed investigation. Conducting such a screening study could be daunting unless it is approached in a systematic manner. In this paper we describe development of an EOR screening system, which will enable engineers and scientists to sieve through some commonly available reservoir parameters for dozens or hundreds of reservoirs at a time, in a relatively efficient manner without compromising the quality of the outcome. Prior to the development of the screening system, an extensive literature review was conducted to develop the main EOR database and to narrow down the few essential criteria that would be sufficient for judging a reservoir’s candidacy for one or more known EOR techniques. Multilayer feed forward neural network and classification tree methods have been selected for the proposed screening system using the data mining software called XLMiner. The developed EOR screening system was tested by conducting some sensitivity analysis and screening tests. The results generated by the proposed EOR screening system were found satisfactory.