An Exponential Solvent Chamber Geometry for Modeling the VAPEX Process
Bibliographic record
Abstract
Accurate simulation of the VAPEX process relies heavily on precise modeling of the solvent chamber propagation. In the previously developed models, the solvent chamber possesses either a linear, circular, or parabolic shape. In this study, an exponential solvent chamber model was considered to represent the propagation of the chamber throughout the spreading and falling stages of the VAPEX process. The tuning parameters of the proposed model include the exponential function coefficient and the transition region thickness. These parameters are altered by employing a MATLAB-based Genetic Algorithm (GA) to minimize the error between determined and measured cumulative produced oil in four experimental case studies presented in the literature. According to the outcomes, the proposed method can accurately adjust the cumulative produced oil to the measured values in both spreading and falling stages. Additionally, the thickness of the transition region obtained by this model is in reasonable agreement with the laboratory measurements. Accordingly, the average relative errors of all four cases for cumulative produced oil and transition region thickness are 7.73% and 5.12%, respectively. Consequently, the model estimates the oil production rate with reasonable precision and the predicted solvent chamber shapes are well-aligned with the experimentally observed chambers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".