Journal of Petroleum and Gas Engineering
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Article Number - 69B246066017

Vol.8(7), pp. 60-78 , September 2017
ISSN: 2141-2677

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Full Length Research Paper

Predicating Water-flooding Performance into Stratified Reservoirs Using a data driven proxy model

Ahmed I. Omar
  • Ahmed I. Omar
  • National Oil Corporation, Libya.
  • Google Scholar
Zhangxing Chen
  • Zhangxing Chen
  • NSERC/AI-EES/Foundation CMG Industrial Research Chair in Reservoir Simulation, University of Calgary, Canada.
  • Google Scholar
Abdulhadi E. Khalifa
  • Abdulhadi E. Khalifa
  • College of Engineering Technology - Janzour, Libya.
  • Google Scholar

 Received: 28 March 2016  Accepted: 06 December 2016  Published: 30 September 2017

Copyright © 2017 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0

Fundamentally, all mathematical models employed in analysis of water-flooding performance implied assumptions to exclude one or more forces to cope with the reservoir heterogeneity. In the beginning of the survey, a series of sensitivity investigations were undertaken to examine the parameters that affect the water-flooding performance in stratified reservoirs. The factors were designed to measure the impact of each force that contributed in water-flooding process. The forces are: viscous force, the force of gravity and capillary forces. Additionally, the cross flow phenomena which result from the viscosity and gravity segregation are investigated.  The parameters that affected performance to a high degree were sampled randomly to create a knowledge domain with specific inputs and target outputs. In this case, it was the final oil recovery factor by reservoir simulator tool. This domain is used as input (supplied solved problems) to the proxy model (artificial neural network) for adjusting the magnitude of the connections between the neurons during training process to generate a model that can predict the performance of the water-flooding in such reservoirs within a limited range with very minor percentage of error. This model can anticipate the performance of the water-flooding process in heterogeneous reservoir when supplied with 12 key parameters (mobility ratio, density of fluids, dipping angle, permeability ordering, heterogeneity degree, injection rate, reservoir thickness, porosity, and permeability in 3D and reservoir depth). The average absolute percentage of error is about 4.6% particularly and error standard deviation about 8.7% with correlation coefficient between result collected from simulation and ANN is about 99.1%, when the system parameters are within the range of data that was used during the training.


Key words: Secondary recovery techniques, water flooding, Neural Network, Stratified reservoirs.

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APA Omar, A. I., Chen, Z., & Abdulhadi, E. K. (2017). Predicating Water-flooding Performance into Stratified Reservoirs Using a data driven proxy model. Journal of Petroleum and Gas Engineering, 8(7), 60-78.
Chicago Ahmed I. Omar, Zhangxing Chen and Abdulhadi E. Khalifa. "Predicating Water-flooding Performance into Stratified Reservoirs Using a data driven proxy model." Journal of Petroleum and Gas Engineering 8, no. 7 (2017): 60-78.
MLA Ahmed I. Omar, Zhangxing Chen and Abdulhadi E. Khalifa. "Predicating Water-flooding Performance into Stratified Reservoirs Using a data driven proxy model." Journal of Petroleum and Gas Engineering 8.7 (2017): 60-78.

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