On the ratio of energy produced to energy injected in SAGD: Long-term consequences of early stage operational decisions
Bibliographic record
Abstract
Steam-Assisted Gravity Drainage (SAGD) is a recovery process used to extract bitumen from underground reservoirs. The efficiency of SAGD is determined by the volume of steam injected (reflecting cost and greenhouse gas emissions) relative to the volume of oil recovered (revenue). Another way to look at this relationship is as a ratio of cumulative energy produced to energy injected (EPEI). First described in this study, EPEI ratios offer a novel perspective on SAGD because they reduce the inter-well comparison bias caused by operational choices such as steam temperature, steam quality and well length. There are numerous studies on SAGD from an experimental, analytical or numerical simulation perspective, but all require assumptions and constraints specified in advance, and can only return an approximation of field performance. Studies that work exclusively with field data have typically been used for prediction modeling only. In contrast, this study employs a novel methodology by working within the existing constraints and operational choices of SAGD field wells to infer the number of steam-oil performance relationships present, and how long they last. One by one, we eliminate parameters that cannot cause the performance differences seen in the field using only deductive reasoning and multi-parameter sensitivity analysis, and arrive at possible reasons for the performance relationships found. 1520 SAGD well pairs in Alberta, Canada are used, which is more than 90% of the wells in Alberta over the last 20 years, and to the best of our knowledge, the largest field well set of any SAGD study to date. This comprehensive data set ensures that findings are applicable across the industry. Results reveal a new and surprising finding that the EPEI ratio is set during the first few months on production, and is very difficult to change thereafter. Hence, operators have a short, time-limited opportunity to influence each well's recovery ratio (and therefore its profitability and greenhouse gas emissions). This reflects the importance of process start-up and the first few months after start-up when the near well region of the reservoir is conditioned. Finally, the stability of the EPEI ratio permits a simple, new model capable of predicting cumulative recovery months in advance.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".