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Energies 2017, 10(7), 842; doi:10.3390/en10070842

Applicability of an Artificial Neural Network for Predicting Water-Alternating-CO2 Performance

Department of Energy Resources Engineering, Inha University, Incheon 402-751, Korea
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Author to whom correspondence should be addressed.
Academic Editor: Alireza Bahadori
Received: 2 April 2017 / Revised: 9 May 2017 / Accepted: 10 May 2017 / Published: 24 June 2017
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Abstract

The injection of CO2 as part of the water-alternating-gas (WAG) process has been widely employed in many mature oil fields for effectively enhancing oil production and sequestrating carbon permanently inside the reservoirs. In addition to simulations, the use of intelligent tools is of particular interest for evaluating the uncertainties in the WAG process and predicting technical or economic performance. This study proposed the comprehensive evaluations of a water-alternating-CO2 process utilizing the artificial neural network (ANN) models that were initially generated from a qualified numerical data set. Totally two uncertain reservoir parameters and three installed surface operating factors were designed as input variables in each of the three-layer ANN models to predicting a series of WAG production performances after 5, 15, 25, and 35 injection cycles. In terms of the technical view point, the relationships among parameters and important outputs, including oil recovery, CO2 production, and net CO2 storage were accurately reflected by integrating the generated network models. More importantly, since the networks could simulate a series of injection processes, the sequent variations of those technical issues were well presented, indicating the distinct application of ANN in this study compared to previous works. The economic terms were also briefly introduced for a given fiscal condition which included sufficient concerns for a general CO2 flooding project, in a range of possible oil prices. Using the ANN models, the net present value (NPV) optimization results for several specific cases apparently expressed the profitability of the present enhanced oil recovery (EOR) project according to the unstable oil prices, and most importantly provided the most relevant injection schedules corresponding with each different scenario. Obviously, the methodology of applying traditional ANN as shown in this study can be adaptively adjusted for any other EOR project, and in particular, since the models have demonstrated their flexible capacity for economic analyses, the method can be promisingly developed to engage with other economic tools on comprehensively assessing the project. View Full-Text
Keywords: water-alternating-gas (WAG); artificial neural network (ANN); estimation; CO2 storage; enhanced oil recovery (EOR); critical performance water-alternating-gas (WAG); artificial neural network (ANN); estimation; CO2 storage; enhanced oil recovery (EOR); critical performance
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Le Van, S.; Chon, B.H. Applicability of an Artificial Neural Network for Predicting Water-Alternating-CO2 Performance. Energies 2017, 10, 842.

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