Modeling asphaltene precipitation during natural depletion of reservoirs and evaluating screening criteria for stability of crude oils

Evaluation of different thermodynamic models in predicting asphaltene precipitation: A comparative study

“…Among different models, only FH fails to predict the behavior trend of asphaltene precipitation in these oil samples. These oil samples were studied by Hemmati-Sarapardeh et al [6]. They used intelligent tools and also FH model for estimating asphaltene precipitation.…”

“…Oil-rich part of the oil, and asphaltenes represent the solvent and polymer solute in the liquid-liquid equilibrium (LLE), respectively [24,25]. In Flory-Huggins theory, which was initially put forward by Hirschberg et al, the mixture Gibbs energy may be derived by [6,26]:…”

“…Spectroscopic techniques have implied the poly-nuclear aromatic structure of asphaltene molecules with heteroatoms such as sulfur, oxygen, and nitrogen, and alkyl chains [2]. Any change in a number of parameters including pressure, temperature, and oil composition can result in the phenomenon of asphaltene precipitation, and further drives acute issues such as absolute and relative permeability reduction, rise of pressure drop, and undesirable wettability alteration [3][4][5][6]. In addition, gas-based enhanced oil recovery approaches such as nitrogen or carbon dioxide flooding may lead to precipitation of asphaltene [7].…”

“…The proposed scaling equation was then extended by Kord et al to be applicable for estimating asphaltene precipitation due to water and gas injection, and also pressure variation [22]. Hemmati-Sarapardeh et al [6] applied two intelligent techniques including radial basis function, and multilayer perceptron neural network optimized with several algorithms such as genetic algorithm, differential evolution, ant colony optimization, gravitational search algorithm, particle swarm optimization, imperialist competitive algorithm, scaled conjugate gradient, resilient back propagation, Levenberg-Marquardt, and Bayesian regularization to predict asphaltene precipitation as a function of crude oil characteristics such as temperature, pressure, API gravity, bubble point pressure, and SARA (saturate, aromatics, resin, asphaltene) fractions as the input parameters. The obtained results were compared with those based on Flory-Huggins thermodynamic model.…”

“…The pseudocomponent, HC, includes normal paraffin, iso-paraffin, cycloalkanes, poly-nuclear-aromatic, and all resins. Asphaltene pseudo-component mole fractions in various oils are found based on the results of SARA analysis performed by different researchers[1,6,21,45,46]. It must be noticed that HC participates in cross-associating with asphaltene.…”