Integral Study of the Influence of Well Trajectory on the SAGD Performance
Bibliographic record
Abstract
Abstract Due to various operational realities including the precision of the guiding tools that navigate the wells, actual SAGD well pairs in the field are seldom drilled perfectly parallel. And different patterns of trajectory of SAGD wellpair have a significant impact on the steam chamber development and overall recovery factor [1-4]. In this work, the focus is on the quantification of well trajectory on the SAGD performance. Based on the typical in-situ oil viscosities and reservoir properties of 11 SAGD pairs located in the Fengcheng heavy-oil region in Xinjiang Oilfield, different non-parallel patterns of injection-production-well-trajectory combinations including non-parallel vertically (upward inclination, downward inclination), horizontally and both directions between injector and producer well trajectories have been evaluated by using a detailed, 3D, flexible-wellbore reservoir-simulation model. TTthe vertical and plane offsets in the horizontal segment both cause the smaller regional distance between injector and producer, resulting in better heat communication during preheating phase, while after converting to SAGD production phase, the better communication segment will cause faster steam chamber growth, leaving steam chamber in other segment developing poorly, therefore the SAGD peak oil rate and ultimate oil recovery factor are also affected. Especially, the case of plane and vertical offsets simultaneously cause the plane and vertical unparalleled, which causes the ultimate oil recovery factor 26.2% less than that of the ideal case without offset.
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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".