Development of a Robust Correlation to Determine an Oil Production Rate through Vapor Extraction
Bibliographic record
Abstract
Summary Due to high heavy oil viscosity, conventional oil recovery methods such as water flooding and gas flooding have an adverse mobility ratio between an injection fluid and a reservoir fluid; therefore, they malfunction in the production of heavy oil reservoirs. Hence, other oil recovery methods should be utilized to produce heavy oil. One of the robust methods in heavy oil recovery is vapor extraction (VAPEX). Huge efforts have been made to establish a novel approach to calculate an oil production rate in VAPEX method. In this work, a Gene Expression Programming (GEP) approach is carried out, and extensive experimental datasets reported in the literature are employed to develop, test and validate the correlation developed here. All the input variables of the new correlation are dimensionless numbers including Schmidt (Sc) and Peclet (Pe) numbers. The statistical criteria show the promising performance and accuracy of this correlation in the calculation of the oil production rate in VAPEX method. This correlation is capable to apply in a wide range of conditions and also can be incorporated in a heavy oil reservoir simulation software products.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".