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Energies 2017, 10(7), 837; doi:10.3390/en10070837

Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields

1
School of Engineering, College of Physical Sciences, University of Aberdeen, Aberdeen AB24 3FX, UK
2
Polytechnic Institute of Technology and Sciences (ISPTEC), Department of Engineering and Technology (DET), Av. Luanda Sul, Rua Lateral Via S10, Talatona, Luanda PO Box 1316, Angola
3
Sonangol EP; Academia Sonangol, Av. Luanda Sul, Rua Lateral Via S10, Talatona, Luanda PO Box 1316, Angola
*
Author to whom correspondence should be addressed.
Academic Editor: Moran Wang
Received: 4 May 2017 / Revised: 12 June 2017 / Accepted: 13 June 2017 / Published: 22 June 2017

Abstract

In this work, a neuro-fuzzy (NF) simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR) projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL) and the learning capability of neural network (NN) to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K) where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques. View Full-Text
Keywords: enhanced oil recovery (EOR); neuro-fuzzy (NF); artificial intelligence (AI); reservoir screening; neural network (NN) enhanced oil recovery (EOR); neuro-fuzzy (NF); artificial intelligence (AI); reservoir screening; neural network (NN)
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Ramos, G.A.R.; Akanji, L. Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields. Energies 2017, 10, 837.

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