Experimental study and a proposed new approach for thermodynamic modeling of wax precipitation in crude oil using a PC-SAFT model

Abstract

A powerful method is necessary for thermodynamic modeling of wax phase behavior in crude oils, such as the perturbed-chain statistical associating fluid theory (PC-SAFT). In this work, a new approach based on the wax appearance temperature of crude oil was proposed to estimate PC-SAFT parameters in thermodynamic modeling of wax precipitation from crude oil. The proposed approach was verified using experimental data obtained in this work and also with those reported in the literature. In order to compare the performance of the PC-SAFT model with previous models, the wax precipitation experimental data were correlated using previous models such as the solid solution model and multi-solid phase model. The results showed that the PC-SAFT model can correlate more accurately the wax precipitation experimental data of crude oil than the previous models, with an absolute average deviation less than 0.4 %. Also, a series of dynamic experiments were carried out to determine the rheological behavior of waxy crude oil in the absence and presence of a flow improver such as ethylene-vinyl acetate copolymer. It was found that the apparent viscosity of waxy crude oil decreased with increasing shear rate. Also, the results showed that the performance of flow improver was dependent on its molecular weight.

Avaliação De Asfaltenos Precipitados Em Diferentes Condições De Composição, Temperatura E Pressão

“…1,4,5 Estudos indicam que a precipitação de asfaltenos também pode ocorrer espontaneamente nos reservatórios, levando à formação de nanopartículas, principalmente quando essa fração está presente em baixas concentrações (< 0,1 g L -1…”

“…2,3 Outros fatores como cisalhamento e efeitos eletrocinéticos aparentemente também exercem este efeito. 1,4,5 Estudos indicam que a precipitação de asfaltenos também pode ocorrer espontaneamente nos reservatórios, levando à formação de nanopartículas, principalmente quando essa fração está presente em baixas concentrações (< 0,1 g L -1 ). [6][7][8] Esse fenômeno, no entanto, normalmente está relacionado à produção/recuperação do petróleo, principalmente devido a variações de temperatura e à despressurização, comum nesses processos.…”

Strategy of Coupling Artificial Intelligence with Thermodynamic Mechanism for Predicting Complex Polymer Viscosities

“…PC-SAFT has shown good accuracy and predictive capability for a wide range of fluids and conditions. 33,34 Therefore, the PC-SAFT parameters were selected as part of the features of artificial intelligence algorithms in this study.…”

“…After preliminary screening, this paper chooses to use extreme gradient boosting (XGBoost), convolutional neural network (CNN), and multilayer perceptron (MLP) to predict the viscosities of polymer aqueous solutions and polymer melts. PC-SAFT has shown good accuracy and predictive capability for a wide range of fluids and conditions. , Therefore, the PC-SAFT parameters were selected as part of the features of artificial intelligence algorithms in this study.…”