Neural Network Based Performance Evaluation of a Waterflooded Oil Reservoir
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International Journal of Recent Engineering Science (IJRES) |  |
| © 2021 by IJRES Journal | ||
| Volume-8 Issue-3 |
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| Year of Publication : 2021 | ||
| Authors : Mahlon Marvin Kida, Zakiyyu Muhammad Sarkinbaka, Abdulhalim Musa Abubakar, Aminullah Zakariyyah Abdul |
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| DOI : 10.14445/23497157/IJRES-V8I3P101 |
How to Cite?
Mahlon Marvin Kida, Zakiyyu Muhammad Sarkinbaka, Abdulhalim Musa Abubakar, Aminullah Zakariyyah Abdul, "Neural Network Based Performance Evaluation of a Waterflooded Oil Reservoir," International Journal of Recent Engineering Science, vol. 8, no. 3, pp. 1-6, 2023. Crossref, https://doi.org/10.14445/23497157/IJRES-V8I3P101
Abstract
In this paper, we considered the use of neural networks in the identification and prediction of a waterflooded reservoir consisting of eight injection wells and one production well with a 40% porosity. The data used for the non-linear identification was generated from a reservoir modelled in MATLAB Reservoir Simulation Toolbox (MRST). Likewise, in this study, the effect of the number of hidden neurons on the accuracy, Mean Squared Error and oil production prediction of the reservoir were investigated. The study asserted the efficacy of the neural networks as regards their predictive capacity. For the oil production rate, a mean squared error was recorded to be minimal for 2 hidden neurons as compared to the other three cases of neuron number. For water production rate, 8 hidden neurons were observed to be optimal compared to other cases. Oil and water production rate for a peak NPV value of 3 billion US dollars was recorded to be 2000m3/day and 4500m3/day, respectively. The response was optimal for all cases except for the net present value, which requires a more substantial amount of data for the neural network model.
Keywords
MRST, Artificial Neural Networks, Net Present Value, Oil Reservoir, Oil production, Water production.
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