Bibliographic record
Abstract
This article, written by Technology Editor Dennis Denney, contains highlights of paper OTC 17714, “Panel: Oil and Gas Reserves Estimates,” by R. Harrell, SPE, Ryder Scott Co.; R. Gajdica, SPE, BHP Billiton; D. Elliott, Alberta Securities Commission; T.S. Ahlbrandt, SPE, U.S. Geological Survey; and S. Khurana, JP Kenny Inc., prepared for the 2005 Offshore Technology Conference, Houston, 2–5 May. Copyright 2005 Offshore Technology Conference. Reproduced by permission. This article is a summary of a panel session at the 2005 Offshore Technology Conference. Oil and gas reserves estimates are further complicated with the expanding importance of the worldwide deepwater arena. These deepwater reserves can be analyzed, interpreted, and conveyed in a consistent, reliable way to investors and other stakeholders. Continually improving technologies can lead to improved estimates of production and reserves, but the estimates are not necessarily recognized by regulatory authorities as an indicator of “reasonable certainty,” a term used since 1964 to describe proved reserves in several venues. Solutions are being debated in the industry to arrive at a reporting mechanism that generates consistency and at the same time leads to useful parameters in assessing a company’s value without compromising confidentiality. Current Issues Reserves estimation has been under way for more than 100 years and has been discussed within the industry for more than 70 years. So why was this topic discussed? The reasons include several proved-reserves write-downs in 2003, evolving technology, new regulations (particularly in Canada), and the U.S. Sarbanes-Oxley Act of 2002. Reserves Write-Downs. Annual changes in estimates of proved reserves, both positive and negative, are a matter of fact in the industry. In many instances, they may be the result of reclassifications from proved to probable reserves solely related to economic factors and may not necessarily be a loss of reserves. However, 2003 write-downs were large and were outside generally accepted ranges. There are many reasons for these write-downs, but an overriding and common cause is the increasing industry awareness and understanding of the U.S. Security and Exchange Commission (SEC) reserves definitions and how the current SEC engineer-ing staff interprets them. New Technologies. Fewer appraisal wells are drilled, and 3D-seismic, core-analysis, wireline-testing, and logging data—all integrated into a detailed computer-generated reservoir model—are used for assessing reserves. The SEC issued a “will not object” opinion on 15 April 2004 for the reporting of reserves only from the deepwater Gulf of Mexico (GOM) without benefit of a traditional formation-to-surface flow test if there is overwhelming evidence of economically productive rates corroborated through analysis of seismic, logs, cores, and wireline-test data. Improving oilfield drilling and completion technology and access to markets has reduced the time to drain a reservoir efficiently, but has not necessarily improved the ultimate recovery of a reservoir or field. Improved ultimate economic recovery is being enhanced in many cases by strategically placing wells through use of seismics and by reducing project operating and capital costs. Such improved (or sometimes only accelerated) recovery should reflect positively on the value of booked proved reserves.
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 | 0.000 | 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".