Oil Reservoir Simulation via Deep Learning: Mini Review

Oil Reservoir Simulation via Deep Learning: Mini Review

  IJRES-book-cover  International Journal of Recent Engineering Science (IJRES)          
  
© 2022 by IJRES Journal
Volume-9 Issue-5
Year of Publication : 2022
Authors : Thlama Mperiju Mainta, Yahi Ali Dzakwa, Yakubu Ishaku
DOI : 10.14445/23497157/IJRES-V9I5P101

How to Cite?

Thlama Mperiju Mainta, Yahi Ali Dzakwa, Yakubu Ishaku, "Oil Reservoir Simulation via Deep Learning: Mini Review," International Journal of Recent Engineering Science, vol. 9, no. 5, pp. 1-10, 2022. Crossref, https://doi.org/10.14445/23497157/IJRES-V9I5P101

Abstract
Reservoir engineers are faced with constitutive reservoir estimation due to monumental data. In some cases, these datasets are difficult to analyze and extrapolate. Geological uncertainties can also affect the way reservoir data are managed. This intricacy has, in one way or the other, created many discrepancies in data utilization, determining how this reservoir data are incorporated into production forecasting. For this reason, data generated from these reservoir operations are used to obtain surrogate models via smart systems (Deep learning). This review aims to evaluate the systemic application of deep learning models to oil reservoir processes by considering various models, such as the Time series models. We first looked at the current trend of technological innovation in the oil and gas sector. We reviewed work done by several authors in different areas of reservoir modelling and simulation and how their works impact the global production of oil and gas by implementing smart technology. With the tremendous applicability of smart systems, oil reservoir management has become less complex due to automation. Artificial Neural Networks have been shown to improve the production efficiency of oil reservoirs even though geological uncertainties are inherent.

Keywords
Artificial Neural Networks, Data Driven Modelling, Enhanced Oil Recovery, Reservoir Simulation.

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