Bibliographic record
Abstract
Discover a Career The substantial reduction in global oil prices has put the oil and gas community in a challenging and yet vaguely familiar position. It is once again reminded of the effect oil and gas prices have on companies working to profitably exploit these resources. When there are changes in prices, reserves estimation can dictate the profitability an operator can ultimately obtain. Larry Mizzau, principal for reserves and resources governance at Cenovus Energy reflects on his 30-plus years of experience in the industry and shares his thoughts on how commodity prices impact operators, reserves consultants, and young professionals (YPs) looking to establish a career in reserves estimation. What is reserves estimation? Reserves estimation is a key step in understanding an oil and gas company’s resource base and the opportunities it affords. It is found at the crossroads between asset management and financial stewardship. It involves the estimation of remaining volumes of hydrocarbons economically recoverable from an oil and gas operator’s subsurface assets using current technology. Given that reserves exist deep in the ground, they cannot be determined with absolute certainty and, as such, can only be estimated. To assist investors in understanding this uncertainty, reserves estimates are typically determined at different confidence levels. In Canada and the United States, public operating companies must disclose an updated estimate of their remaining oil and gas reserves on a yearly basis as part of their yearend financial reporting. Specifically in Canada, operators are required to disclose assessments prepared or audited by independent qualified reserves evaluators (IQREs) who can be externally or internally retained by the company.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".