Technical Revisions Reveal Overconfidence in US and Canadian Reserves Estimates
Bibliographic record
Abstract
Summary In this paper, we present methodology to quantify biases in reserves estimates using technical revisions (TRs) listed in reserves-reconciliation reports filed with regulators in the US and Canada. Using this methodology, we assessed the reliability of reserves estimates for 34 companies filing in Canada and 32 companies filing in the US from 2007 to 2017. Filers in both Canada and the US overestimated proved (1P) reserves, and US filers overestimated 1P reserves (51% positive TRs instead of 90%) more often than Canadian filers (72% positive TRs). Canadian filers underestimated proved-plus-probable (2P) reserves slightly (54% positive TRs instead of 50%). Considering the entire reserves distribution, Canadian filers were moderately overconfident (underestimated uncertainty) and slightly pessimistic. US filers, who report only 1P, were somewhere between the combination of extreme overconfidence and neutral directional bias (DB) and the combination of moderate overconfidence and extreme optimism. Three groups of professionals can benefit from this study: estimators, who can use the methodology to track their TRs over time, calibrate them, and use this information to improve future estimation procedures; investors, who can analyze reported reserves estimates to compare volumes fairly; and regulators, to whom the paper provides quantitative methodology to suggest to filers to help them ensure compliance with appropriate criteria for 1P and 2P reserves and avoid significant reserves write-downs later.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".