An Analytical Production Model for Primary Production and Cyclic Solvent Injection in a Heavy-Oil Reservoir
Bibliographic record
Abstract
Summary In this paper, we formulated an analytical material–balance model (MBM) to predict cumulative heavy–oil and gas-production data, as well as the average reservoir pressures, during the primary production and subsequent cyclic solvent injection (CSI) in a heavy–oil reservoir. The theoretical MBM considers the nonequilibrium foamy–oil phase behavior and foamy–oil flow by invoking two kinetic equations with nucleation and decay coefficients. In addition, we conducted four laboratory sandpack tests of the primary production and subsequent CSI to validate the new production model. It was found that the predicted cumulative heavy–oil production data and average reservoir pressures agreed reasonably well with the measured data during the primary production and subsequent CSI. However, there were large discrepancies between the predicted and measured cumulative gas-production data in the CSI owing to its strong gas channeling, which is a major technical issue to be studied further. Moreover, dissolved CH4 in the heavy oil became dispersed CH4 bubbles more quickly when the nucleation coefficient was larger at a higher pressure–drawdown rate or in less–viscous heavy oil. The foamy heavy oil with dispersed CH4 bubbles was more stable when the decay coefficient was smaller at a higher pressure–drawdown rate or in more–viscous heavy oil. It was also found that the foamy–oil isothermal compressibility increased by 10 to 1,000 times and that the dispersed–gas percentage in the foamy oil could reach as high as 14 vol% during the primary production. The foamy–oil viscosity was reduced by 36 to 55%, and the solution CH4/heavy–oil ratio was decreased by 41 to 76% at the end of the CSI.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".