Bibliographic record
Abstract
Abstract Understanding the effects of lease-line drainage has always been a challenge facing natural gas producers. When approaching this issue, companies generally focus on gas migration caused by reservoir pressure gradients. However, ‘crossflow migration’ in multi-layered reservoirs also needs to be evaluated. Crossflow migration occurs when wells produce at a flowing pressure higher than the near-wellbore reservoir pressure of a commingled and depleted high permeability zone. This can result in unexpected crossflow of gas from a low-permeability, high-pressured, high-reserves zone to the depleted zone, and, through this ‘thief zone’, migration of the gas to adjacent lands. This study quantifies reserves and production impacts of crossflow migration, using a model of two neighbouring shallow gas properties in S.E. Alberta, and based on geological characteristics typical of the area around the Alderson, Suffield and Medicine Hat Fields (Figure 1). The analysis showed that historical lease-line drainage of 1.9 BCF (55 e6m3) occurred during 1960–2007 over a one-mile boundary between two operators, one of which had aggressively developed its property through reduced operating pressure and infill drilling. Of this amount, approximately 45% was through the permeable Medicine Hat A (MHA) formation. Drainage through the MHA was supported by approximately 0.6 BCF (18 e6m3) of crossflow into that formation from other zones completed in the commingled wellbores. If left unaddressed, further drainage of 0.7 BCF (20 e6m3) was predicted to occur over the 30-year forecast period. Using reservoir simulation, this study also identifies an optimal development strategy to minimize the impact of crossflow migration drainage.
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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 | 0.001 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".