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Natural Resources Research

, Volume 28, Issue 4, pp 1619–1637 | Cite as

ANN-Based Prediction of Laboratory-Scale Performance of CO2-Foam Flooding for Improving Oil Recovery

  • Seyedeh Raha Moosavi
  • David A. WoodEmail author
  • Mohammad Ali Ahmadi
  • Abouzar Choubineh
Original Paper
  • 64 Downloads

Abstract

Improving oil recovery by CO2 injection continues to gain momentum in mature oil fields due to its favorable industrial and environmental benefits. One remediation for the poor sweep efficiency of CO2 is co-injection of surfactants to generate CO2-foams in reservoirs. However, it is essential to minimize the expensive and time-consuming experiments required during the laboratory screening of this EOR process for a given reservoir. In this regard, methods to predict RF and Q from reservoir characteristics based on existing laboratory test data are worthwhile. In this paper, we develop the RF and Q prediction models involving optimized multi-layer perceptron (MLP) and radial basis function (RBF) neural networks. These models are applied to a compiled dataset of 214 data records of published CO2-foam injection tests into oil-reservoir cores. The RF and Q prediction derived applying these two models to the compiled dataset are compared. Statistical accuracy measures of the predictions achieved for an independent testing subset (20% of the data records) indicate for RF (MLP: RMSE = 0.0236, R2 = 0.9988; for RBF: RMSE = 0.0197, R2 = 0.9991) and for Q (MLP: RMSE = 0.0283, R2 = 0.9971; for RBF: RMSE = 0.0092, R2 = 0.9991) the excellent prediction performance of the developed networks.

Keywords

CO2-foam subsurface injection Enhanced oil recovery CO2 underground sequestration Multi-layer perceptron Radial basis function CO2 performance 

Supplementary material

11053_2019_9459_MOESM1_ESM.xlsx (21 kb)
Supplementary material 1 (XLSX 21 kb)
11053_2019_9459_MOESM2_ESM.xlsx (15 kb)
Supplementary material 2 (XLSX 15 kb)

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Copyright information

© International Association for Mathematical Geosciences 2019

Authors and Affiliations

  1. 1.Department of Chemical and Petroleum EngineeringShiraz UniversityShirazIran
  2. 2.DWA Energy LimitedLincolnUK
  3. 3.Department of Chemical and Petroleum Engineering, Schulich School of EngineeringUniversity of CalgaryCalgaryCanada
  4. 4.Department of Petroleum EngineeringPetroleum University of TechnologyAhwazIran

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