Modeling temperature dependency of oil - water relative permeability in thermal enhanced oil recovery processes using group method of data handling and gene expression programming

Abstract

In the implementation of thermal enhanced oil recovery (TEOR) techniques, the temperature impact on relative permeability in oil-water systems (K rw and K ro ) is of special concern. Hence, developing a fast and reliable tool to model the temperature effect on K rw and K ro is still a major challenge for precise studying 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 and simple to use paradigms to predict the temperature dependency of K rw and K ro . To do so, a large database encompassing wide-ranging temperatures and fluids/rock parameters, 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 correlations were found to be the most consistent with root mean square error (RMSE) values of 0.0284 and 0.0636 for K rw and K ro , respectively. Lastly, the performance comparison against the preexisting correlations indicated the large superiority of the newly introduced correlations. The findings of this study can help for better understanding the temperature dependency of K rw and K ro in TEOR. ARTICLE HISTORY

Experimental estimation of temporal and spatial resolution of coefficient of heat transfer in a channel using inverse heat transfer method

“…For future research employing artificial intelligence methods, in particular, machine learning techniques (e.g. Farzaneh-Gord et al, 2019;Ghalandari, et al, 2019aGhalandari, et al, , 2019bMenad et al, 2019;Mosavi, Shamshirband, Salwana, Chau, & Tah, 2019;Mou, He, Zhao, & Chau, 2017) is encouraged considering higher accuracy and lower computational costs.…”

Prediction of Permeability Using Group Method of Data Handling (GMDH) Neural Network from Well Log Data

“…Over the years, the GMDH concept has been widely applied in different areas, Menad et al [30] applied GMDH and gene expression programming (GEP) to establish reliable correlations for estimating temperature-based oil-water relative permeability. Shen et al [31], developed a GMDH model that can detect the various lithofacies using pre-processing techniques composed of dimensionality reduction (DR) and wavelet analysis (WA).…”

A combined support vector regression with firefly algorithm for prediction of bottom hole pressure

“…During the last years, data-driven through its different methods has been increasingly introduced in several fields of science and technology including petroleum engineering, to resolve practical problems [11][12][13]. Accordingly, very attractive frameworks such as Artap, which is based on machine learning and nature-inspired algorithms have been implemented for practical applications [14].…”