Bibliographic record
Abstract
Abstract Oil and gas companies face great pressure to sustain and/or maximize the recovery of resources, in an environment of global competition, under mounting operating expense with stricter accountability to the environment, and public safety. Companies must be vigilant to technological innovation that will deliver a stepwise change to the way they conduct their business in order to deliver capital and operational improvements that produce a measurable financial impact. The Canadian oil and gas industry has been amongst the leaders in technical innovation and been equally quick to adopt innovations necessary to exploit and maximize reserves. This paper discusses how new communications technology and hardware can be linked with traditional business software solutions to deliver information specific to departments and individuals in an organization. The paper focuses on new technologies for data acquisition, well site monitoring, and data analysis where information is brought from a remote asset to the desktop. Modeling concepts will be reviewed that shows how secure and dependable data communications can disseminate information to personnel within an organization to make informed decisions and reduce response time. Data information can be used to "seed" other traditional software applications that will permit more in-depth analysis. Using the Internet to disseminate data within an organization allows collaboration between individuals and departments creating opportunities for integration of engineering services. Introduction Communications is one of the most rapidly growing and evolving technologies today. Words such as "satellite," "cellular" and "wireless communications" which were unknown terminology in the oil field vocabulary only a few years ago are now commonplace. New communications technology allows well site information to be communicated from almost any location in the world cost effectively, reliably, securely and almost instantly. The Internet is becoming the communication mechanism of choice for data and technology delivery, and dissemination of data and information within organizations. At Oracle's weeklong Open World 20001 conference, VP of product marketing and services Mr. Mark Jarvis told the 35,000 attendees that in five years' time, no one will buy software as a product --- it will only exist as a service over the Internet. Director of E&P services for EDS Energy Industries Group, Mr. Dick Standaert2, wrote that in 2 or 3 years, more than 50% of all oil and gas technical services and scientific information services will be Web-based. And by 2005 the Internet will be the accepted norm for all such services. Reference to such comments should not be dismissed as merely an attempt to elicit some form of "shock" value. The movement described is already underway. Imminent changes described are forcing oil and gas producing companies to reconsider the relationship between their equipment suppliers, consultants, partners and their employees. Consulting services, oil field service providers, hardware equipment vendors, and information providers are questioning how they will remain competitive as the delivery and marketing of their services' evolve. Oil and gas production companies are questioning whether they have prepared themselves and their employees to take full advantage of technology and service innovations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.297 | 0.245 |
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 source (direct Gemma or distilled Codex), 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".