No end in sight: End-of-life management of oil wells in Alberta
Bibliographic record
Abstract
Development of oil and gas resources while maximizing production has been the primary objective of policymakers and regulators in Alberta, Canada for many decades. When oil prices were sufficiently high, environmental risks and other concerns received little attention. When oil prices collapsed in 2014, Alberta’s inventory of inactive, decommissioned, and orphaned wells grew dramatically. It is now a complex problem for operators, regulators, and policymakers and the return of high oil prices has not resolved the problem. This article uses a real options model to evaluate end-of-life decisions of oil wells in Alberta subject to mean-reverting oil prices, with low or high average prices, to understand which oil wells will be reclaimed at the end of their useful lives versus those left unreclaimed. Results under our baseline parameters show that firms operating a typical oil well will extract over 95 per cent of the reserves in place and reclaim the well. When the cost to decommission or reclaim a well is larger that the cost of maintaining an inactive well, the firm will still extract over 95 per cent of reserves, but will leave the well in a decommissioned state and never reclaim the well. This suggests that some of the oil and gas wells that have been left unreclaimed have high decommission or reclamation costs. If those cleanup costs are correlated with environmental risks (groundwater contamination, gas migration, etc.) then the inventory of inactive oil and gas wells could be populated with the riskiest wells, adding an additional level of complexity to the issue of unreclaimed oil and gas wells in Alberta. We also examine the effect of a time limit on inactivity or a bond on end-of-life decisions. Our results suggest that a combination of a time limit on inactivity and a bond could be useful policy instruments to help ensure high-cost oil and gas wells are reclaimed at the end of their life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".