Preprint Article Version 1 This version is not peer-reviewed

Modeling Temperature Dependency of Oil-Water Relative Permeability in Thermal Enhanced Oil Recovery Processes Using Group Method of Data Handling and Gene Expression Programming

Version 1 : Received: 28 June 2019 / Approved: 1 July 2019 / Online: 1 July 2019 (11:12:44 CEST)

How to cite: Menad, N.A.; Noureddine, Z.; Hemmati-Sarapardeh, A.; Shamshirband, S.; Mosavi, A.; Chau, K. Modeling Temperature Dependency of Oil-Water Relative Permeability in Thermal Enhanced Oil Recovery Processes Using Group Method of Data Handling and Gene Expression Programming. Preprints 2019, 2019070004 (doi: 10.20944/preprints201907.0004.v1). Menad, N.A.; Noureddine, Z.; Hemmati-Sarapardeh, A.; Shamshirband, S.; Mosavi, A.; Chau, K. Modeling Temperature Dependency of Oil-Water Relative Permeability in Thermal Enhanced Oil Recovery Processes Using Group Method of Data Handling and Gene Expression Programming. Preprints 2019, 2019070004 (doi: 10.20944/preprints201907.0004.v1).

Abstract

In the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil - water systems is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on two-phase oil - water relative permeability is still a major challenge for precise studying and evaluation of TEOR processes. To reach the goal of this work, two promising soft-computing algorithms, namely Group Method of Data Handling (GMDH) and Gene Expression Programming (GEP) were employed to develop reliable, accurate, simple and quick to use paradigms to predict the temperature dependency of relative permeability in oil - water systems (Krw and Kro). To do so, a large database encompassing wide-ranging temperatures and fluids/rock parameters, including oil and water viscosities, absolute permeability and water saturation, was considered to establish these correlations. Statistical results and graphical analyses disclosed the high degree of accuracy for the proposed correlations in emulating the experimental results. In addition, GEP based correlations were found to be the most consistent with root mean square error (RMSE) values of 0.0284 and 0.0636 for Krw and Kro, respectively. Lastly, the comparison of the performances of our correlations against those of the preexisting ones indicated the large superiority of the introduced correlations compared to previously published methods. The findings of this study can help for better understanding and studying the temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes.

Subject Areas

Thermal enhanced oil recovery (TEOR); temperature effect on oil/water relative; group method of data handling (GMDH) and gene expression programming (GEP), machine learning, deep learning

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