Development of a Numerical Scheme for Simulation of Asphaltene Dependent Phenomena in Porous Media
Bibliographic record
Abstract
Abstract Asphaltene is the highest molecular weight fraction of crude oil that under some conditions can undergo deposition and adsorption and affect the properties of porous media. This work presents a mathematical model for fractionation of asphaltene content of crude oil into different parts that evolve as a result of mechanisms like precipitation, flocculation, adsorption and entrapment during pressure depletion and solvent injection tests in core samples. A fully coupled numerical scheme that bundles all nonlinear partial differential equations (PDEs) and pertinent relations is developed to compute the distribution of these fractions and other properties with respect to time and space. Flow of suspended asphaltene particles in the oil phase is modeled and phase behavior properties are predicted by the Peng-Robinson equation of state. A thermodynamic equation is derived to calculate the solubility parameter as an indication of asphaltene stability in the flowing system. The pressure distribution along the core is determined by combining the mass balance equations for oil, gas and asphaltene components into one PDE. In addition, a convection-dispersion PDE is developed to calculate the distribution of asphaltene concentration and include the effect of dispersion of asphaltene particles in the model. A reduction in transmissibility and large additional pressure drop due to asphaltene precipitation are used to infer the extent of damage to porous medium. Furthermore, an artificial neural network is trained using asphaltene deposition data and is then applied to calculate permeability evolution based on porosity. Finally, the modeling results are validated by experimental data. Interpretation of the obtained results and tracking of distribution for various fractions of asphaltene are useful to detect and evaluate the asphaltene dependent phenomena, their real cause and relative importance, and the locations where they may occur. Asphaltene is shown to affect the oil production rate and recovery efficiency. An enhanced knowledge of all relevant mechanisms and considering them in simulation and decision making will lead to the development of improved production schemes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".