Experimental, modelling and optimisation of asphaltene deposition and adsorption in porous media
Bibliographic record
Abstract
Abstract The deposition of asphaltene is considered to be one of the most difficult problems during oil production. Asphaltene deposition in the reservoir is a complex problem; and, due to the depth of reservoirs, it is impossible to do field experiments. Thus, to study the effects of asphaltene deposition, it is essential to rely on different models that are developed for asphaltene adsorption and deposition on the basis of the laboratory experimental results. In this manuscript, the focus is on the adsorption and deposition of asphaltene in porous media. A new model for asphaltene adsorption and deposition in porous media and a corresponding numerical method are developed using an implicit scheme. A procedure for estimating the asphaltene deposition and adsorption parameters is described, and a new optimisation method is introduced. This method applies the genetic algorithm to investigate an initial set of parameters as an input for the direct search optimisation method. The direct search estimates the asphaltene deposition and adsorption parameters, which results in an acceptable match with the experimental data. To validate the model, both the available experimental data in the literature and data from this study are examined with the developed model; and, reasonable results are obtained. © 2011 Canadian Society for Chemical Engineering
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".