Mahnaz Hekmatzadeh
IOR Research Institute, Tehran, Iran

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Neuro-Simulation Tool for Enhanced Oil Recovery Screening and Reservoir Performance Prediction Soheil Bahrekazemi; Mahnaz Hekmatzadeh
Emerging Science Journal Vol 1, No 2 (2017): August
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (838.117 KB) | DOI: 10.28991/esj-2017-01116

Abstract

Assessment of the suitable enhanced oil recovery method in an oilfield is one of the decisions which are made prior to the natural drive production mechanism. In some cases, having in-depth knowledge about reservoir’s rock, fluid properties, and equipment is needed as well as economic evaluation. Both putting such data into simulation and its related consequent processes are generally very time consuming and costly.  In order to reduce study cases, an appropriate tool is required for primary screening prior to any operations being performed, to which leads reduction of time in design of ether pilot section or production under field condition. In this research, two different and useful screening tools are presented through a graphical user interface. The output of just over 900 simulations and verified screening criteria tables were employed to design the mentioned tools. Moreover, by means of gathered data and development of artificial neural networks, two dissimilar screening tools for proper assessment of suitable enhanced oil recovery method were finally introduced. The first tool is about the screening of enhanced oil recovery process based on published tables/charts and the second one which is Neuro-Simulation tool, concerns economical evaluation of miscible and immiscible injection of carbon dioxide, nitrogen and natural gas into the reservoir. Both of designed tools are provided in the form of a graphical user interface by which the user, can perceive suitable method through plot of oil recovery graph during 20 years of production, costs of gas injection per produced barrel, cumulative oil production, and finally, design the most efficient scenario.