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

Geraldo A. R. Ramos and Lateef Akanji
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Geraldo A. R. Ramos: School of Engineering, College of Physical Sciences, University of Aberdeen, Aberdeen AB24 3FX, UK
Lateef Akanji: School of Engineering, College of Physical Sciences, University of Aberdeen, Aberdeen AB24 3FX, UK

Energies, 2017, vol. 10, issue 7, 1-33

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.

Keywords: enhanced oil recovery (EOR); neuro-fuzzy (NF); artificial intelligence (AI); reservoir screening; neural network (NN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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