Bibliographic record
Abstract
SPE has been a leader in developing petroleum reserves and resources definitions that have become the industry standard for evaluating petroleum reserves, providing a sound basis for improving the consistency in reserves and resource estimation and reporting worldwide. Now SPE is taking another step forward, envisioning a universal standard that could be adopted by international financial, regulatory, and reporting bodies, as well as the oil and gas industry. Forming the foundation for this standard are the petroleum reserves definitions approved in 1997 by SPE and the World Petroleum Council (WPC) and the resources definitions approved by SPE, WPC, and AAPG in 2000. This effort is being led within SPE by its Oil and Gas Reserves Committee, of which I am honored to serve as Chairperson. Our goal is to review the existing set of definitions and guidelines in light of those current best practices that provide professionals with a common understanding of reserves and resource classification and terminology. Any potential changes should result in a more accurate global picture of current prospects and future energy supplies for the public. In the absence of a comprehensive and current code, individual countries and companies are using their own reserves evaluation systems, making global comparisons difficult. This update is part of SPE’s ongoing effort to achieve worldwide use of standard reserves definitions. SPE’s Oil and Gas Reserves Committee has helped create some of the most significant industry reference documents on reserves that are in use today (see documents at www.spe.org). This standing committee is made up of 11 members who have reserves expertise and represent a wide geographic cross section. Six observers from a diverse group of societies and agencies also provide other industry perspectives and guidance to the committee. The reserves committee completed the first phase of the definitions review by producing a glossary that provides a common and consistent understanding of terms. This glossary required several years of work to create, and it was posted on the SPE website in January 2005 (available at www.spe.org/spe/jsp/basic/ 0,,1104_1730,00.html). The committee is now focused on comparing or “mapping” the classifications and definitions that are used in other worldwide systems for regulatory government reporting or company internal resource asset management. These are the U.S. Securities and Exchange Commission; U.K. Statement of Recommended Practices; Canadian Security Administrators; Russian Ministry of Natural Resources; China Petroleum Reserves Office; Norwegian Petroleum Directorate; U.S. Geological Survey; and the United Nations Framework Classification. By understanding the relationships between these different sets, it will be possible to identify and leverage best practices that could potentially be incorporated into a new set of definitions. The committee made a report on the mapping process at the SPE Board of Directors meeting in October, and the Board approved posting the mapping document to the SPE website.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".