Idle Oil Wells: Half Empty or Half Full?
Bibliographic record
Abstract
There are hundreds of thousands of oil and gas wells across North America that are cur-rently not producing oil or gas. Many of these wells have not been permanently decommis-sioned to meet environmental standards for permanent closure, but are in an inactive state that enables the well to be more easily reactivated. Some of these wells have been in this inactive state for more than sixty years which begs the question of whether the inactive wells can contribute significantly to our energy supply, or whether they are the result of operators avoiding their environmental obligations. By estimating a dynamic discrete choice model of operating state with data of production decisions from 84 thousand wells and estimates of expected recoverable reserves from 47 thousand pools this paper shows that a drastic increase in prices and production technology are necessary to see a notable increase in the number of inactive wells reactivated. Jel–Classification: C61, Q32, Q41 Keywords:dynamic structural estimation; environmental remediation; oil and gas ∗I thank my advisors John Rust and Marc Nerlove. I also thank the Alberta Energy and Resources Conservation Board for access to the data, and the Chicago-Argonne Institute on Computational Economics for the optimization
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".