Optimization of Flow Parameters of Heavy Crude Oil-in-Water Emulsions through Pipelines

Accurate prediction of pressure losses using machine learning for the pipeline transportation of emulsions

One of the significant challenges to designing an emulsion transportation system is predicting frictional pressure losses with confidence. The state-of-the-art method for enhancing reliability in prediction is to employ artificial intelligence (AI) based on various machine learning (ML) tools. Six traditional and tree-based ML algorithms were analyzed for the prediction in the current study. A rigorous feature importance study using RFECV method and relevant statistical analysis was conducted to identify the parameters that significantly contributed to the prediction. Among 16 input variables, the fluid velocity, mass flow rate, and pipe diameter were evaluated as the top predictors to estimate the frictional pressure losses. The significance of the contributing parameters was further validated by estimation error trend analyses. A comprehensive assessment of the regression models demonstrated an ensemble of the top three regressors to excel over all other ML and theoretical models. The ensemble regressor showcased exceptional performance, as evidenced by its high R2 value of 99.7 % and an AUC-ROC score of 98 %. These results were statistically significant, as there was a noticeable difference (within a 95 % confidence interval) compared to the estimations of the three base models. In terms of estimation error, the ensemble model outperformed the top base regressor by demonstrating improvements of 6.6 %, 11.1 %, and 12.75 % for the RMSE, MAE, and CV_MSE evaluation metrics, respectively. The precise and robust estimations achieved by the best regression model in this study further highlight the effectiveness of AI in the field of pipeline engineering.

Investigating the Rheology and Stability of Heavy Crude Oil-in-Water Emulsions Using APG08 Emulsifiers

This work investigates the performance of isooctyl glucoside (APG08) as an emulsifier for the preparation of a Karamay heavy crude oil-in-water emulsion to facilitate its pipeline transportation. First, various factors affecting the rheology and stability of prepared emulsions were studied. The results revealed that the viscosity and stability of emulsions increased with increasing oil content, surfactant concentration, mixing speed, mixing time, and pH of the aqueous phase. Emulsion viscosity was initially unchanged with the increase in homogenization temperature and then increased while emulsion stability decreased. Meanwhile, the optimal values of key parameters were 75 wt % oil content, 0.5 wt % surfactant concentration, temperature of 30 °C, mixing speed of 750 rpm, mixing time of 10 min, and aqueous phase pH of 11.14, resulting in a viscosity reduction of 88.82% and emulsion stability up to 48 h at 96.27%. In addition, a qualitative relationship between the stability and rheology of emulsions was elaborated by analyzing the experimental results. The findings showed that an increase in emulsion stability was accompanied by an increase in emulsion viscosity. Therefore, emulsion viscosity cannot become very high while improving emulsion stability to ensure proper transportation.