Study of Temperature and Pressure Fall-Off during Shut-In and Slow-Down for SAGD Wells with Top Water
Bibliographic record
Abstract
Abstract In in-situ oil sands operations using Steam Assisted Gravity Drainage (SAGD), understanding the temperature and pressure responses during periods of shut-in and slow-down is crucial for effective steam chamber management. The corresponding operating strategy should be based on the temperature and pressure performance. At Nexen’s Long Lake in-situ project, there are a number of pads with extensive top water in contact with the target bitumen interval. Along with the rest of the wells in operation at Long Lake, these SAGD wells have experienced several periods of shut-in and slow-down due to maintenance operations and other surface constraints. Surface and downhole sensors and other real-time monitoring technologies provide opportunities to monitor temperature and pressure during these time periods, which help to better understand the impact of surface activities on steam chamber development, particularly in areas with extensive top water present. This paper presents the temperature and pressure responses over time at different elevations within the reservoir. It compares temperature and pressure responses for the wells with top water and without top water and 4D seismic is also integrated to analyze the causes for the different temperature and pressure responses. Review of the data shows that shut-in has a more significant impact on temperature and pressure in the steam chamber than slow-down. Wells with top water experience severe temperature drops while slow-down wells do not. After long periods of shut-in, areas with thicker top water may take additional time to heat up and reach steam conditions again, resulting in higher steam injection requirements. The collected data suggests that continuous steam injection is crucial to maintain steam chamber with top water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".