Optimization of Gas Injection Process Using Artificial Neural Network


Abstract:

In the petroleum industry there have always been an urge to improve hydrocarbon recovery, specially in aging process in which the reservoir pressure is not high enough to satisfy the economic goals of the owners. Low reservoir pressure, unrecoverable oil trapped in the reservoir, etc. are amongst many reasons that make the use of recovery methods other than the primary techniques inevitable. Enhanced oil recovery (EOR) is a generic term for those techniques which are employed to increase the recoverable oil from such reservoirs. Gas injection has proved to be one the most effective and as result most common EOR methods in the petroleum industry. This method helps maintain the reservoir pressure and also would mix with the trapped oil and lower its viscosity and push it towards the production wells. As the gas injection project like any other EOR method is going to be rather expensive to be applied in a reservoir, optimization of the process in order to lower the expenses and achieve the best possible result is of great importance. In this paper artificial neural networks (ANN) are used to find meaningful relation between different properties and variables of an injection-production system simulated by Eclipse reservoir simulator based on a real reservoir in North Sea. Afterwards the optimization process is made using Genetic Algorithms (GA) in order to get the best injection scenario in the reservoir under study.

Keywords: Recovery, EOR, Gas Injection, Reservoir pressure, Viscosity.

PDF Format of Article

References:

  • “Enhanced Oil Recovery/CO2 Injection.”, United States Department of Energy, Washington, DC (2011).

  • “Enhanced Oil Recovery Scoping Study.”, Final Report, No. TR-113836, Electric Power Research Institute, Palo Alto, CA (1999).

  • “About EOR”, Clean Air Task Force (2009).

  • Kenneth S. Deffeyes, Hubbert’s Peak: The Impending World Oil Shortage (New Edition), Princeton University Press, pp. 107, ISBN 978-0-691-14119-0, (2012).

  • Carcoana, Aurel, Applied Enhanced Oil Recovery, Prentice Hall, ISBN 0-13-044272-0, (1992).

  • Baviere, M., Basic Concepts in Enhanced Oil Recovery Processes, London: Elsevier Applied Science, ISBN 1-85166-617-6, (2007).