Optimization of Asphaltene Deposition and Adsorption Parameter in Porous Media Search
Bibliographic record
Abstract
Abstract The deposition of asphaltene is considered to be one of the most difficult problems during oil production. The mechanism of Asphaltene precipitation and deposition is not fully understood until now. Asphaltene deposition in the reservoir is a complex problem and due to high depth of reservoir, it is impossible to do field experiments. Laboratory tests are expensive and time consuming. In order to study effects of asphaltene deposition, it is be necessary to rely on different models which are developed for Asphaltene adsorption and deposition. These models consist of several matching parameters. Iteration method was used to obtain an acceptable match for experimental data. Deposition equations are highly coupled therefore deposition parameters cannot be optimized independently. Due to dependency of parameters, ordinary optimization methods are not applicable for deposition. In this paper we introduce a new model for asphaltene deposition in porous media and corresponding numerical method was developed using implicit scheme, then a procedure for matching the asphaltene deposition and adsorption parameters was described and a new optimization method was introduced .In this method genetic algorithm was applied and the result of it was used as input for direct search to obtain an acceptable match for experimental data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".