Numerical Study of the Effects of Lean Zones on SAGD Performance in Periodically Heterogeneous Media
Bibliographic record
Abstract
Abstract Highly permeable lean zones that halt the growth of steam chambers through lateral spreading significantly affect the performance of steam-assisted gravity drainage (SAGD). Nexen's Long Lake and Suncor's Firebag SAGD projects reported existence of intersecting lean zones that behave as thief zones, causing the operating pressure to be lower than the desirable value to reduce heat loss in the lean zones. Given a high steam-oil ratio, long and continuous lean zones located vertically above or near a wellbore pose a challenge. Log analyses and core experiments reveal that the distribution of lean zones is close to being periodic in the upper and middle parts of the McMurray Formation in Athabasca. A simulation model with periodic lean zones is established to analyze the effects of these lean zones on SAGD performance. We investigate the effects of vertical distribution, horizontal spacing and sizes, and spatial relationship with SAGD horizontal wells. We classify the locations of lean zones as above an injector (AI), between an injector and a producer (BIP), and below a producer (BP). For each location, we assign different spacing for neighboring lean zones of different sizes. A lean zone must reach a critical size before this area can act as a thief zone, which is most significant in AI and BIP situations. In general, BP cases are hardly influenced by lean zones. We further control the vertical distribution of these zones using a triangular periodic function that depends on layer depth and connate water saturation. Changing the period of the dominant function yields different kinds of lean zone distribution patterns; we run simulations for each pattern. Results show that lean zones with small periods have significant effects on SAGD performance. We vary the injection pressure to obtain the highest net present value (NPV) based on the fact that more steam is released through the lean zones at a higher pressure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".