Recovery Rate of Vapor Extraction in Heavy Oil Reservoirs—Experimental, Statistical, and Modeling Studies
Bibliographic record
Abstract
The VAPor EXtraction process (also known as VAPEX) is a solvent-based enhanced oil recovery (EOR) technology that has great potential for the recovery of heavy oil and bitumen through mass transfer and gravity drainage mechanisms. In this study, laboratory tests, the multivariable regression technique, and the connectionist model optimized by a Genetic Algorithm (GA) were used to determine the oil production rate during the VAPEX process in homogeneous and fractured porous media. The smart technique and statistical models describe the VAPEX production rate in terms of three dimensionless numbers, namely the Schmidt number ( Sc ), the Peclet number ( Pe ), and a dimensionless parameter ( N S ) referred to as the VAPEX number. The developed smart model was constructed based on a large number of experimental data conducted under various process conditions in both training and testing phases. A comparison of results obtained from connectionist modeling, the regressive model, and the experimental VAPEX data exhibits an average absolute error lower than 7% between the predicted and actual values. Using both experimental and modeling results, the statistical analysis suggests that the Peclet number is the most important parameter affecting the oil production rate in the VAPEX, and also the smart technique is superior to the regression model developed. This study shows the effectiveness of connectionist model in prediction of VAPEX production in the absence of sufficient laboratory and/or field data, which may lead to a proper design of heavy oil recovery schemes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".