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Neural Computing and Applications

, Volume 25, Issue 3–4, pp 815–824 | Cite as

Efficient screening of enhanced oil recovery methods and predictive economic analysis

  • Arash Kamari
  • Mohammad Nikookar
  • Leili Sahranavard
  • Amir H. Mohammadi
Original Article

Abstract

Oil demand for economic development around the world is rapidly increasing. Moreover, oil production rates are getting a peak in mature reservoirs and tending to decline in the near future, which has led to considerable researches on enhanced oil recovery (EOR) methods. Therefore, an efficient technical and economical screening to appropriate selection of EOR methods can make savings in time and cost. The purpose of this communication is to present a method to select an efficient EOR process and investigate its economic parameters. A database of reservoir parameters of rock and fluid properties along with successful EOR techniques has been collected and analyzed. First, an artificial neural network (ANN) was developed to classify the EOR methods technically. Then, an economical EOR screening model was designed, and then, future cash flows on the use of EOR methods were predicted. The results show that the ANN system can select proper EOR methods and classify them. Moreover, the obtained results indicate that the economic analysis performed in this study is efficient and useful to predict future cash flows.

Keywords

Artificial neural network Screening EOR data Economical study Rock Fluid characteristics 

Notes

Acknowledgments

The authors are grateful to IOR Research Institute; NIOC R&T for their support.

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Thermodynamics Research Unit, School of EngineeringUniversity of KwaZulu-NatalDurbanSouth Africa
  2. 2.Chemical Engineering DepartmentTarbiat Modares UniversityTehranIran
  3. 3.Institut de Recherche en Génie Chimique et Pétrolier (IRGCP)Paris CedexFrance

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