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.
Highlights
The process of selecting potential candidates for enhanced oil recovery (EOR) operation is a complex task involving integration of a set of rock and fluid parameters governing technical and economic performance of a reservoir
The first category is training or validation data: data derived from laboratories studies, data generated from oil reservoirs simulation, data from successful worldwide projects
The data base from the worldwide successful EOR projects was maximised by tuning the parameters of each variables associated with each five (5) different EOR techniques; steam, CO2, miscible hydrocarbon gas, polymer and combustion
Summary
The process of selecting potential candidates for enhanced oil recovery (EOR) operation is a complex task involving integration of a set of rock and fluid parameters governing technical and economic performance of a reservoir. In order to increase the chances of success and to make an informed decision, parameters obtained from either successful EOR field applications or from existing knowledge of the EOR operation could be effectively utilised Comparison between these criteria and the reservoir of interest will provide an indication of the possibility of success for future EOR projects [4]. Matching the parameters from the worldwide successful EOR techniques is a challenge from data mining and screening points of view. This is the case since these parameters may not necessarily be directly dependent on each other. Several methods have been developed and published for screening oil reservoirs such as data analysis by using tables and graphs [5,6,7,8] and artificial intelligence (AI) [3,9,10,11,12,13]
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