A Comparison between the Perturbed-Chain Statistical Associating Fluid Theory Equation of State and Machine Learning Modeling Approaches in Asphaltene Onset Pressure and Bubble Point Pressure Prediction during Gas Injection

Simin Tazikeh; Abdollah Davoudi; Ali Shafiei; Hossein Parsaei; Timur Sh Atabaev; Oleksandr P Ivakhnenko

doi:10.1021/acsomega.2c03192

A Comparison between the Perturbed-Chain Statistical Associating Fluid Theory Equation of State and Machine Learning Modeling Approaches in Asphaltene Onset Pressure and Bubble Point Pressure Prediction during Gas Injection

ACS Omega. 2022 Aug 16;7(34):30113-30124. doi: 10.1021/acsomega.2c03192. eCollection 2022 Aug 30.

Authors

Simin Tazikeh¹, Abdollah Davoudi², Ali Shafiei¹, Hossein Parsaei³, Timur Sh Atabaev⁴, Oleksandr P Ivakhnenko⁵

Affiliations

¹ Petroleum Engineering Program, School of Mining and Geosciences, Nazarbayev University, 53 Kabanbay Batyr Avenue, Nur-Sultan 010000, Kazakhstan.
² Department of Petroleum Engineering, School of Chemical and Petroleum Engineering, Shiraz University, Shiraz 71348-14336, Iran.
³ Department of Medical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz 71348-14336, Iran.
⁴ Department of Chemistry, Nazarbayev University, Nur-Sultan 010000, Kazakhstan.
⁵ Department of Petroleum Engineering, Kazakh British Technical University, Almaty 050000, Kazakhstan.

Abstract

Predicting asphaltene onset pressure (AOP) and bubble point pressure (Pb) is essential for optimization of gas injection for enhanced oil recovery. Pressure-Volume-Temperature or PVT studies along with equations of state (EoSs) are widely used to predict AOP and Pb. However, PVT experiments are costly and time-consuming. The perturbed-chain statistical associating fluid theory or PC-SAFT is a sophisticated EoS used for prediction of the AOP and Pb. However, this method is computationally complex and has high data requirements. Hence, developing precise and reliable smart models for prediction of the AOP and Pb is inevitable. In this paper, we used machine learning (ML) methods to develop predictive tools for the estimation of the AOP and Pb using experimental data (AOP data set: 170 samples; Pb data set: 146 samples). Extra trees (ET), support vector machine (SVM), decision tree, and k-nearest neighbors ML methods were used. Reservoir temperature, reservoir pressure, SARA fraction, API gravity, gas-oil ratio, fluid molecular weight, monophasic composition, and composition of gas injection are considered as input data. The ET (R ²: 0.793, RMSE: 7.5) and the SVM models (R ²: 0.988, RMSE: 0.76) attained more reliable results for estimation of the AOP and Pb, respectively. Generally, the accuracy of the PC-SAFT model is higher than that of the AI/ML models. However, our results confirm that the AI/ML approach is an acceptable alternative for the PC-SAFT model when we face lack of data and/or complex mathematical equations. The developed smart models are accurate and fast and produce reliable results with lower data requirements.