ArticleScientific reports2025
Predicting asphaltene precipitation during natural depletion of oil reservoirs by integrating SARA fractions with advanced intelligent models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Machine learning based prediction of diesel engine emissions and performance using hemp biodiesel enriched with nano additives.Scientific reports · 2026Article
- White-box modeling of asphaltene precipitation during natural depletion of oil reservoirs.Scientific reports · 2026Article
- Control of sludge formation during acidizing through the investigation of acid and oil interactions and emulsion behavior in heavy oils using low salinity water.Scientific reports · 2026Article
- An innovative technique and laboratory protocol for COScientific reports · 2025Article
- Multi-indicator feature extraction and temperature prediction model for spontaneous coal combustion and oxidation.Scientific reports · 2025Article
- Application of Robust Neural Networks to Modeling Crude Oil Viscosity Reduction Using Nanoparticles.ACS omega · 2025Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Asphaltene precipitation during natural depletion can lead to substantial issues in oil transport and production systems, requiring close assessment of crude oil stability through thermodynamic and structural factors. This precipitation can alter wettability, reduce permeability, and block flow, causing increased pressure drops in wells, upstream facilities, and pipelines. Determining asphaltene precipitation experimentally is time-consuming and costly. Thus, it is essential to find fast and precise techniques for determining asphaltene precipitation. This research aims to accurately predict asphaltene precipitation values using an extensive databank containing 380 experimental data points. Pressure, bubble point pressure, temperature, oil °API gravity, and saturated-aromatic-resin-asphaltene (SARA) fractions of the crude oil samples were treated as input variables, while asphaltene precipitation was the output of the models. Four smart models, namely, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), cascade forward neural network (CFNN), and generalized regression neural network (GRNN) were constructed. To train the CFNN, the Levenberg-Marquardt (LM) algorithm was implemented. Various statistical and visual assessments were conducted to validate the reliability and precision of constructed models. Findings revealed that the developed LightGBM technique could outperform other models in estimating output values with the lowest absolute error value of 8.57% and the highest correlation coefficient (R
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