Bibliographic record
Abstract
Technology Focus Benjamin Franklin once said, “When you are finished changing, you’re finished.” Our industry must continue to improve and evolve to face the challenges of economic and commercial viability, greater transparency, environmental responsibility, and social acceptance. The papers in this month’s Reserves Management feature explore new and novel ideas to maintain social and environmental relevance, maximize value from unconventional projects, and ensure that exploration and appraisal activity is designed to optimize decision-making in resource project management.• Paper SPE 195298 describes a methodical way to show resource project movement through the recently updated (2018) Petroleum Resources Management System (PRMS) and Canadian Oil and Gas Engineering Handbook (COGEH). Not only does this facilitate a consistent approach for entities to classify their resources, but also provides guidance about how to subclassify resources through a technical and commercial analysis. Subclassification on the basis of project maturity is now recommended by the PRMS 2018 to provide greater transparency of the chance of commerciality, particularly for contingent resources. Moreover, it is important to be able to describe the associated chance of commerciality and defend the reasonableness of development assumptions and resource assignment; otherwise, projects should be considered Unrecoverable. Each entity will need to develop the process to their own specific needs. Paper SPE 191455 is an excellent case study using data analysis to evaluate how the industry has evolved through continuous improvement over the last 12 years. The study was conducted using multivariate analysis tools to try and look objectively at the data, and with 12,000 wells to draw from, there was a significant amount of data to analyze. It is a lesson in what is required in determining the best completion technique for the right reservoir; one needs to drill a great many wells. While the study is related to improvements of projects already in the Reserves class, it illustrates the principles of the Technology Under Development process as it applies to Contingent•resources. Paper OTC 28312 is an excellent Australian example of the oil and gas industry collaborating with scientific research institutions to collect data that can be used not only to assess the integrity of subsea infrastructure to guide decommissioning strategies, but also to contribute to a broader understanding of our oceans and marine ecology. In an increasingly complex and uncertain global environment, it makes sense for oil and gas entities to look at ways of collaborating with scientific institutions and other industries for mutual benefit. This should facilitate greater community acceptance and can demonstrate that industry is undertaking responsible scientific, social, and environmental management. A win-win! Recommended additional reading at OnePetro: www.onepetro.org. SPE 193717 Economic Benefits of Implementing Alternative Energy:• A Heavy Oil Fields Case Study by Mohammad Al-Yatama, Kuwait Oil Company, et al. IPTC 19506 Total Well Management: Maximizing Well Life Cycle Value—A Regulator Perspective by Ryan Guillory, Petronas, et al.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".