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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. [ABSTRACT FROM AUTHOR]

An artificial intelligence-based system was developed to efficiently predict settling velocity (SV) using a large dataset comprised of 2726 samples. The ranges of particle size and fluid viscosity were 0.212 − 98.59 mm and 0.02 − 92800 mPa.s, respectively. Properties of particle and fluid were fed to a model as the inputs to obtain SV as the output. Six machine learning algorithms were tested for the prediction. The random forest (RF) performed better than other algorithms with a coefficient of determination of 0.98 and a mean square error of 0.0027. A simple decision support system was developed using the RF model. The current study demonstrates the complete methodology of modeling SV with ML. [ABSTRACT FROM AUTHOR]

Accuracy of prediction of pressure losses plays a vital role in the design of multiphase flow systems. The present study focused on the development of a computational fluid dynamics model to predict these parameters conveniently and accurately. The main objective was to validate the developed model through comparison of its simulation results with existing experimental data and empirical correlations. Both pipeline and annular geometries were considered for validation. Several data sets that involved a significant variation in process conditions were used for the validation. All three phases-liquid (water), gas (air), and solid (sand)-were taken into account. The simulations were conducted using the workbench platform of ANSYS Fluent 16.2. The Eulerian model of multiphase flow and the Reynolds stress model of turbulence closure available in Fluent were used for the present study. The average velocities and volumetric concentrations of involved phases were specified as the inlet boundary conditions. The stationary surfaces of the flow channels were hydrodynamically considered as either smooth or rough walls, and the outlets were regarded as being open to the atmosphere. The simulation results of pressure loss showed a good agreement with the corresponding measured values as well as with the predictions of well-established correlations. [ABSTRACT FROM AUTHOR]

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 R 2 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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)