The long‐distance pipeline transportation of heavy crudes demands optimal pumping procedures to abate overhead expenditures in the midstream phase. This can be facilitated by reducing both the oil viscosity and the frictional losses induced by the turbulent flow zone. The dualistic approach of reducing both viscosity and drag requires the optimization of several parameters such as concentrations and types of diluents and additives. This manuscript critically reviews various technologies being undertaken to facilitate the pipeline transportation of heavy crude oils by highlighting the technique of dilution coupled with the addition of drag reducing agents (DRA). DRA such as surfactants, nanoparticles, bio‐additives, polymers, and fibres are blended with the diluted crudes to suppress the proliferation of turbulent eddies which in turn assist in pumping the oil at a higher flow rate under constant pressure conditions. Several investigations have reported that drag reduction is significantly enhanced by varying the molecular structure of the DRA. In addition, an optimum flow rate coupled with a specific surfactant concentration at an ideal temperature can significantly reduce head losses and pumping expenditures. Further research is imperative to study the effect of the polymer structure with respect to the induced shear degradation and dissolution behavior of the crude.
Gas–liquid flow is a significant phenomenon in various engineering applications, such as in nuclear reactors, power plants, chemical industries, and petroleum industries. The prediction of the flow patterns is of great importance for designing and analyzing the operations of two-phase pipeline systems. The traditional numerical and empirical methods that have been used for the prediction are known to result in a high inaccuracy for scale-up processes. That is why various artificial intelligence-based (AI-based) methodologies are being applied, at present, to predict the gas–liquid flow regimes. We focused in the current study on a thorough comparative analysis of machine learning (ML) and deep learning (DL) in predicting the flow regimes with the application of a diverse set of ML and DL frameworks to a database comprising 11,837 data points, which were collected from thirteen independent experiments. During the pre-processing, the big data analysis was performed to analyze the correlations among the parameters and extract important features. The comparative analysis of the AI-based models’ performances was conducted using precision, recall, F1-score, accuracy, Cohen’s kappa, and receiver operating characteristics curves. The extreme gradient boosting method was identified as the optimum model for predicting the two-phase flow regimes in inclined or horizontal pipelines.
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