Neuro-Simulation Tool for Enhanced Oil Recovery Screening and Reservoir Performance Prediction

Soheil Bahrekazemi, Mahnaz Hekmatzadeh


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.


Neuro-Simulation; Enhanced Oil Recovery; Neural Network; Reservoir Simulation; Screening Tool; Gas Flooding.


Lake, Larry W. "Enhanced oil recovery." (1989): 17-39.

Al Adasani, Ahmad, and Baojun Bai. "Analysis of EOR projects and updated screening criteria." Journal of Petroleum Science and Engineering 79, no. 1 (2011): 10-24.

Parada, Claudia Helena, and Turgay Ertekin. "A new screening tool for improved oil recovery methods using artificial neural networks." In SPE Western Regional Meeting. Society of Petroleum Engineers, 2012.

Trauth, Martin H., Robin Gebbers, Norbert Marwan, and Elisabeth Sillmann. MATLAB recipes for earth sciences. Vol. 34. Berlin: Springer, 2007.

Moore, Holly. MATLAB for Engineers. Pearson Prentice Hall, 2007.

Manual, C. M. G. "Computer Modeling Group." Component Adsorption and Blockage in Appendix D 7 (2008).

Green, D. W., and G. P. Willhite. "Enhanced oil Recovery, vol. 6." SPE Textbook Series, TX, USA (1998).

Fausett, Laurene, and Laurene Fausett. Fundamentals of neural networks: architectures, algorithms, and applications. No. 006.3. Prentice-Hall, 1994.

Samuel Armacanqui, J., and Ahmed Mohamed Hassan. "The Use of an Operational Filter Boosted Artificial Neural Network for Selection of Enhanced Oil Recovery Technique." In SPE North Africa Technical Conference and Exhibition. Society of Petroleum Engineers, 2015.

Surguchev, Leonid, and Lun Li. "IOR evaluation and applicability screening using artificial neural networks." In SPE/DOE Improved Oil Recovery Symposium. Society of Petroleum Engineers, 2000.

Alvarado, Vladimir, Aaron Ranson, Karen Hernandez, Eduardo Manrique, Justo Matheus, Tamara Liscano, and Natasha Prosperi. "Selection of EOR/IOR opportunities based on machine learning." In European Petroleum Conference. Society of Petroleum Engineers, 2002.

Ayala, Luis F., Turgay Ertekin, and Michael Adewumi. "Optimized Exploitation of Gas-Condensate Reservoirs Using Neuro-Simulation." In SPE Asia Pacific Oil and Gas Conference and Exhibition. Society of Petroleum Engineers, 2004.

Olufemi, Odusote, Turgay Ertekin, Duane H. Smith, Grant Bromhal, W. Neal Sams, and Sinisha Jikich. "Carbon Dioxide Sequestration in Coal Seams: A Parametric Study and Development of a Practical Prediction/Screening Tool Using Neuro-Simulation." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2004.

Gorucu, Fatma Burcu, Turgay Ertekin, Grant S. Bromhal, Duane H. Smith, W. Neal Sams, and Sinisha A. Jikich. "A Neurosimulation Tool for Predicting Performance in Enhanced Coalbed Methane and CO2, Sequestration Projects." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 2005.

Al-Dousari, Mabkhout M., and Ali A. Garrouch. "An artificial neural network model for predicting the recovery performance of surfactant polymer floods." Journal of Petroleum Science and Engineering 109 (2013): 51-62.

Siena, Martina, Alberto Guadagnini, Ernesto Della Rossa, Andrea Lamberti, Franco Masserano, and Marco Rotondi. "A new Bayesian approach for analogs evaluation in advanced EOR screening." In EUROPEC 2015. Society of Petroleum Engineers, 2015.

Eghbali, Sara, Shahab Ayatollahi, and Ramin Bozorgmehry Boozarjomehry. "New expert system for enhanced oil recovery screening in non-fractured oil reservoirs." Fuzzy Sets and Systems 293 (2016): 80-94.

Le Van, Si, and Bo Hyun Chon. "Evaluating the critical performances of a CO2–Enhanced oil recovery process using artificial neural network models." Journal of Petroleum Science and Engineering 157 (2017): 207-222.

Taber, Joseph John, F. D. Martin, and R. S. Seright. "EOR screening criteria revisited-Part 1: Introduction to screening criteria and enhanced recovery field projects." SPE Reservoir Engineering 12, no. 03 (1997): 189-198.

Brooks, Royal Harvard, and Arthur Thomas Corey. "Hydraulic properties of porous media and their relation to drainage design." Transactions of the ASAE 7, no. 1 (1964): 26-0028.

Minakowski, Claudia Helena Parada. An artificial neural network based tool-box for screening and designing improved oil recovery methods. The Pennsylvania State University, 2008.

Full Text: PDF

DOI: 10.28991/esj-2017-01116


  • There are currently no refbacks.

Copyright (c) 2017 Soheil Bahrekazemi, Mahnaz Hekmatzadeh