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 (R2: 0.793, RMSE: 7.5) and the SVM models (R2: 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.