Asphaltene formation modeling using vapor-liquid-liquid equilibrium calculations by PC-SAFT for reservoir and surface conditions

Nazari, Farzaneh; Assareh, Mehdi; Asbaghi, Ehsan Vahabzadeh

doi:10.1016/j.petrol.2020.108209

Cited by 3 publications

(2 citation statements)

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“…Asphaltene is the most polar and the heaviest component of crude oils, having a sophisticated molecular architecture that is defined in terms of solubility in different substances. − Asphaltenes can be dissolved in aromatic hydrocarbons (e.g., toluene and benzene), and it is not soluble in paraffinic solvent with low molecular weight, including n -hexane, n -heptane, and n -pentane. ,, Asphaltene aggregation increases the crude oil viscosity and promotes the stability of water-in-oil emulsions, implying the need for removing them from crude oil. Asphaltene precipitation can result in undesired wettability alteration, plugging wellbores, and permeability reduction leading to flow assurance issues, equipment damage, increased capital/operation costs, and decreased production. , …”

Section: Introductionmentioning

confidence: 99%

“…Asphaltene precipitation usually occurs during some EOR methods such as miscible gas injection and solvent injection because of compositional modifications and pressure drop. ,,, The nature of solvent/precipitant injected into crude oil directly affects the amount of asphaltene precipitation. For instance, the naphthenic and resin groups in the solvent structure have the same effect as aromatic compounds and inhibit asphaltene precipitation.…”

Section: Introductionmentioning

confidence: 99%

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Asphaltene Precipitation Prediction during Bitumen Recovery: Experimental Approach versus Population Balance and Connectionist Models

et al. 2022

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Deasphalting bitumen using paraffinic solvent injection is a commonly used technique to reduce both its viscosity and density and ease its flow through pipelines. Common modeling approaches for asphaltene precipitation prediction such as population balance model (PBM) contains complex mathematical relation and require conducting precise experiments to define initial and boundary conditions. Machine learning (ML) approach is considered as a robust, fast, and reliable alternative modeling approach. The main objective of this research work was to model the effect of paraffinic solvent injection on the amount of asphaltene precipitation using ML and PBM approaches. Five hundred and ninety (590) experimental data were collected from the literature for model development. The gathered data was processed using box plot, data scaling, and data splitting. Data pre-processing led to the use of 517 data points for modeling. Then, multilayer perceptron, random forest, decision tree, support vector machine, committee machine intelligent system optimized by annealing, and random search techniques were used for modeling. Precipitant molecular weight, injection rate, API gravity, pressure, C 5 asphaltene content, and temperature were determined as the most relevant features for the process. Although the results of the PBM model are precise, the AI/ML model (CMIS) is the preferred model due to its robustness, reliability, and relative accuracy. The committee machine intelligent system is the superior model among the developed smart models with an RMSE of 1.7% for the testing dataset and prediction of asphaltene precipitation during bitumen recovery.

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