Bibliographic record
Abstract
Abstract The effectiveness of heat injection into a target formation has a great impact on the efficiency of bitumen and heavy oil recovery and energy savings under many steam heating processes such as the startup phase of SAGD (Steam Assisted Gravity Drainage) (Butler 1991). However, this parameter is hard to calculate due to many unknown variables such as variations in operational conditions and steam saturation along the horizontal wellbores, heat return rates, and losses to the vertical section above the target formation. This paper proposes a new technique to estimate cooling time and formation thermal diffusivity by using thermal transient analysis (TTA) along the horizontal wellbore under a steam heating process. A novel concept of a heating ring is also introduced to measure the heat storage in the heated bitumen at the time of testing. Heating ring can be considered analogous to a drainage area in a conventional pressure transient analysis. The proposed cooling time and formation thermal diffusivity calculated along the horizontal wellbore can be used to assess the effectiveness of the conduction heating. Cooling time in this paper is defined as the theoretical time required to cool the heated formation to the initial formation temperature. A longer cooling time indicates a higher net heat gain in the formation while the calculated thermal diffusivity is used to predict wellbore conditions and the type of fluid saturation along the horizontal wellbore. Thus, a combination of cooling time and formation thermal diffusivity can be employed to assess the effectiveness of heat injection during various steam heating processes. By knowing the effectiveness of each heating scenario, the process can be selected and optimized not only to save heat energy and steam consumption but also to enhance bitumen recovery. This paper is limited to the new heating operation processes.
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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 | 0.000 |
| 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 | 0.000 |
| 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".