An Analytical Approach for Optimizing the Subcool of Non-Condensable Gas (NCG) Assisted Heavy Oil Production: Predictions & Limitations
Bibliographic record
Abstract
Abstract NCG is increasingly being co-injected with steam in heavy oil production systems to reduce heat loss and greenhouse gas emissions, as well as to maintain reservoir pressure. Given increased use of NCG co-injection, the validity of conventional subcool models must be revisited since they assume that the steam chamber is comprised of water alone. The current study makes modifications to the pure-steam hydrostatic subcool model, as well as the Yuan & Nugent (2013) subcool model to account for the presence of NCG in the steam chamber. Using typical values from the Athabasca oilfield, the study then compares the liquid-height predictions made by the original and modified models and proposes rules-of-thumb that correct for the presence of NCG. In general, increasing NCG in the steam chamber results in a reduction in subcool relative to pure steam. According to modified hydrostatic model, to achieve a liquid-pool height equal to that of pure steam injection, the subcool must be increased by 0.60K per 1% increase in the vapor-phase molar fraction. In contrast, over a wide range of production rates and drawdowns, the modified Yuan & Nugent (2013) model predicts that to achieve a liquid-pool height equal to that of the pure steam case, the subcool must be increased by 0.66K per 1% increase in the vapor-phase molar fraction. Despite the rule-of-thumbs being qualitatively in line with expectations, they suffer from the inability to accurately calculate subcool from field data. The final section of the paper reviews limitations of subcool as a well performance metric and proposes an alternative method of assessment that relies on data that are more readily available to operators.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".