On the Relationship between Completion Design, Reservoir Characteristics, and Steam Conformance Achieved in Steam-based Recovery Processes such as SAGD
Bibliographic record
Abstract
Abstract With continuing decline of conventional crude oil reserves, there is higher demand for developing and producing heavy oil and bitumen resources. In Alberta, Canada, there are over 170 billion barrels of recoverable bitumen in oil sands. The two main thermal technologies being used to produce are Steam Assisted Gravity Drainage (SAGD) and Cyclic Steam Stimulation (CSS). For Athabasca reservoirs, SAGD is the method of choice since the oil has low solution gas. One key factor that controls the success of these methods is steam conformance – the ability to control the distribution of steam within the oil column. In this research, thermocouple data from the Surmont SAGD pilot project together with reservoir geology are analyzed to examine steam conformance and its impact on process performance (oil rate, recovery factor, and steam to oil ratio). The data is analyzed to generate a simple correlation between reservoir geology and operating pressure to predict vertical steam conformance versus time. The results demonstrate that the reservoir geology, operating pressure, and well completion design all affect steam conformance in the reservoir. Also, the distribution of steam flow and pressure within the well impacts steam conformance in the reservoir. An analysis of the A wellpair of the Surmont SAGD pilot suggests that the distribution of steam flow in the well contributes largely to the non-uniform steam conformance along this wellpair.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".