Application of Temperature Fall-Off Interpretation Method in Superheavy Oil or Oil Sand SAGD Process
Bibliographic record
Abstract
SAGD technology has been successfully and widely applied in the development of superheavy oil and oil sand projects. Before normal SAGD process, some preheating ways are often needed to realize interwell hydraulic connection, and this means that determining reasonable SAGD conversion timing from the preheating stage is an essential precondition for good performance. Previous numerical simulations or qualitative analysis of temperature fall-off data are often adopted in the industry, but they have deficiencies in terms of dependent on static geological model or insufficient data utilization. Therefore, on the basis of the temperature and pressure monitoring process comparison in China’s superheavy oil and Canada’s oil sand projects, this paper proposed a temperature fall-off interpretation model to obtain thermal diffusivity and preheating radius at different measurement points along the horizontal section by combining an unsteady thermal conduction model under constant heating power of wellbores in the radial coordinate system and approximately unsteady thermal conduction model with constant wellbore temperature and Fourier’s law of thermal conduction. Besides, the duration time, interpretation method, and application flow chart of temperature fall-off test were presented. Then, it was validated to successfully determine the timing of SAGD conversion from the preheating stage by an example combining with tracking numerical simulation, temperature inflection point analysis, and index analysis during the partial-SAGD and initial SAGD stages. The findings of this study can help determine the SAGD conversion timing from the preheating stage simpler and faster especially for the case of long horizontal well section deployed with more temperature measurement points.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".