An experimental and theoretical investigation of asphaltene precipitation in a crude oil from the Brazilian pre-salt layer under CO2 injection

Nascimento, Fábio P.; Pereira, Verônica J.; Souza, Renan; Lima, Rui; Costa, Glória Meyberg Nunes; Rosa, Paulo de Tarso Vieira e; Forca, Ana F.; Melo, S.A.B. Vieira de

doi:10.1016/j.fuel.2020.118968

Cited by 18 publications

(12 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Introductionmentioning

confidence: 99%

“…Clogging porous media, well plugging, permeability reduction, and wettability alteration are some of the significant consequences of asphaltene precipitation, which occur during different oil production stages. 2,3 Injection of gas (e.g., nitrogen, carbon dioxide, and natural gas) during EOR operations is one of the widely used and effective ways to recover residual oil after primary and secondary oil recovery methods. The injection gases can enhance the flow of crude oil via reducing its viscosity.…”

Section: Introductionmentioning

confidence: 99%

“…3 However, a combination of oil and gas changes the composition and behavior of reservoir fluids and provides a favorable situation for asphaltene precipitation. 2 Hence, estimation of asphaltene onset pressure (AOP) is vital to minimize asphaltene precipitation and the related problems. Accurate estimation of the AOP in various stages of oil production can help to have a correct long-term plan from the economic viewpoint.…”

Section: Introductionmentioning

confidence: 99%

“…Based on their solubility, asphaltenes dissolve in aromatic solvents (e.g., toluene, naphtha, and benzene) and are nonsoluble in n -alkanes. Clogging porous media, well plugging, permeability reduction, and wettability alteration are some of the significant consequences of asphaltene precipitation, which occur during different oil production stages. , …”

Section: Introductionmentioning

confidence: 99%

“…The injection gases can enhance the flow of crude oil via reducing its viscosity . However, a combination of oil and gas changes the composition and behavior of reservoir fluids and provides a favorable situation for asphaltene precipitation . Hence, estimation of asphaltene onset pressure (AOP) is vital to minimize asphaltene precipitation and the related problems.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Comparison between the Perturbed-Chain Statistical Associating Fluid Theory Equation of State and Machine Learning Modeling Approaches in Asphaltene Onset Pressure and Bubble Point Pressure Prediction during Gas Injection

et al. 2022

View full text Add to dashboard Cite

Predicting asphaltene onset pressure (AOP) and bubble point pressure (Pb) is essential for optimization of gas injection for enhanced oil recovery. Pressure-Volume-Temperature or PVT studies along with equations of state (EoSs) are widely used to predict AOP and Pb. However, PVT experiments are costly and time-consuming. The perturbed-chain statistical associating fluid theory or PC-SAFT is a sophisticated EoS used for prediction of the AOP and Pb. However, this method is computationally complex and has high data requirements. Hence, developing precise and reliable smart models for prediction of the AOP and Pb is inevitable. In this paper, we used machine learning (ML) methods to develop predictive tools for the estimation of the AOP and Pb using experimental data (AOP data set: 170 samples; Pb data set: 146 samples). Extra trees (ET), support vector machine (SVM), decision tree, and k-nearest neighbors ML methods were used. Reservoir temperature, reservoir pressure, SARA fraction, API gravity, gas–oil ratio, fluid molecular weight, monophasic composition, and composition of gas injection are considered as input data. The ET ( R 2 : 0.793, RMSE: 7.5) and the SVM models ( R 2 : 0.988, RMSE: 0.76) attained more reliable results for estimation of the AOP and Pb, respectively. Generally, the accuracy of the PC-SAFT model is higher than that of the AI/ML models. However, our results confirm that the AI/ML approach is an acceptable alternative for the PC-SAFT model when we face lack of data and/or complex mathematical equations. The developed smart models are accurate and fast and produce reliable results with lower data requirements.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%