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Since knowing the effect of salt type is a vital parameter during smart-water injection for enhanced oil recovery (EOR) purposes, surface-free energies on the carbonate surface contacted by crude o...

Sulfate anion is well-known for being one of the most active agents to be injected into the oil reservoirs and being capable of not only altering the interfacial properties of crude oil but...

Although several investigations have studied the low-salinity water injection (LSWI) performance during the past decades, the effect of crude oil type on the interfacial tension (IFT) and wettabili...

Although rotating beds show promise for intensified separations, the fundamentals of their hydrodynamics are still poorly understood. In many operating conditions, pressure drop in an irrigated bed can be considerably lower than that across a dry bed. Previously published correlations don’t provide good prediction of these phenomena. In this research, a semi-empirical correlation is developed to predict the pressure drop across the rotating packed beds. The results show the agreement of predicted pressure drop with the experimental data is acceptable. This model can also predict the pressure drop unexpected phenomena of higher pressure drop in dry beds than in wet beds.

Prediction of efficiency of chemical inhibitors to mitigation of deposition thickness is a key to developing crude oil transportation process. In this work, a feed-forward artificial neural network (ANN) algorithm has been applied to predict the influence of the mitigation effect of ethylene-co-vinyl acetate (EVA) copolymer and its combination with chloroform (C), acetone (A), P-xylene (PX), and petroleum ether (PE) on the deposition thickness in the pipeline. An optimized three-layer feed-forward ANN model using properties of the oil pipeline such as: inlet oil temperature, environmental (coolant mixture) temperature, oil Reynolds numbers; properties of injected inhibitor such as molecular weight, boiling point, and amount of injection; and time is presented. Different networks are considered and trained using 62661 data sets; the accuracy of the network is validated by 20888 testing data sets. To verify the network generalization, 29 different experiment data sets of four different set of inhibitors hav...

Modeling and prediction of deposition thickness is a key to developing crude oil transportation process. Usually, the heat transfer method has been used to calculate deposition thickness through the pipeline, but this method requires vast information and cumbersome calculations. To eliminate this problem, a feed-forward multilayer perceptron neural network (MLPNN) algorithm has been applied to predict the deposition thickness through the pipeline. We present an optimized one-layer feed-forward neural network using properties of the oil pipeline such as inlet and outlet oil temperatures, environmental (mixture of water and ethylene glycol) temperature, and oil Reynolds’ numbers. Different networks are considered and trained using 15313 data sets; the accuracy of the proposed network is validated by 5104 testing data sets. Using validating data set, the network that is having the lowest mean square error (MSE) and average absolute relative deviation percent (AARD%) and the highest regression coefficient (R2...

Transportation of waxy crude oil in a cold environment can result in wax depositions, which are solid deposits, on the pipe wall. The formation of paraffin in the bulk of the petroleum is a crystallization process. Deposition in Bangestan crude oil pipelines can cause reduction in flow area and thus decrease in the crude oil feed of Ahvaz-3 production unit. Several techniques have been used to minimize the wax deposition in crude oil flowed through the pipelines, howeverthermal insulation of pipelines is considered as an efficient method. The main objective of this study was to determine the influence of insulation thickness on wax deposition for Bangestan crude oil pipeline of Ahvaz-3 production unit with the OLGA simulator. The results of simulation show that the maximum reduction in wax deposition was obtained by using 3.4 inch of insulation polypropylene.

Several techniques have been used to minimize the wax deposition in crude oil pipelines, among them the continuous addition of wax crystal modifier inhibitors is considered as an attractive technological alternative. The main objective of this study was to determine the influence of the ethylene-covinyl acetate (EVA) copolymer and its combination with chloroform (C), acetone (A), P-xylene (PX), and petroleum ether (PE) on the wax deposition thickness of an Iranian crude oil (Paydar-East Reservoir). A new facility was designed and constructed to conduct the experimental part of this study. In this study, heat transfer method was used to measure the deposition thickness at different experimental conditions. Also, samples of the deposits were analyzed at the end of each test for their wax and asphaltene + resin contents. Comparison of the heat transfer and chemical analysis methods show consistent results. The experiments were performed with and without wax chemical prevention to determine the effects of the...

In this article, the effect of temperature, pressure, and flow rate on deposition rate has been investigated by using chemical analysis method for calculating the deposit thickness and comparing to heat transfer and pressure drop methods. To do so, an experimental high pressure flow loop setup was designed and constructed to conduct various experiments. Caliper readings at the end of each test showed the chemical analysis method in comparison to the heat transfer and pressure drop methods is superior for deposit thickness calculation.