Journal of
Petroleum and Gas Engineering

  • Abbreviation: J. Petroleum Gas Eng.
  • Language: English
  • ISSN: 2141-2677
  • DOI: 10.5897/JPGE
  • Start Year: 2010
  • Published Articles: 93

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

Abstract

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.