Bibliographic record
Abstract
Abstract Process Control & Automation Technology (PCAT) has been well understood and utilized by the downstream petroleum industry for years. The upstream is a somewhat different story. Rig-site or wellsite automation, for instance, had been and still is perceived by many as impractical or an overkill. Undeniably, results of past experimentation with production automation had been far from conclusive. The primary reasons for such a checkered past are mostly technical and partially human. However, substantial technological advances in recent years are changing the picture. If the current trends hold, in a matter of say 5 to 10 years, the majority of wellsites in Canada will be automated. Operating wells and fields with automated control systems will become the norm, and PCAT will eventually evolve into yet another standard tool for the upstream oilpatch. As a result, production personnel - engineers, technicians, field operators, and even administration and supporting staffs must learn to adapt to survive. Production automation is a tedious, and at times, confusing subject because it combines a wide range of engineering disciplines from production to process design, to instrumentation, control, and electronics, with the constantly evolving computer technologies. This paper will attempt to shed some light on this PCAT subject from a user's and non-specialist's perspective, and explore some of the recent developments that will likely have a significant impact on the petroleum industry. Introduction Instrumentation and process control are standard technologies for any fluid flow operation in the oilpatch, from the reservoirs all the way to the consumers, passing through wellbores, wellheads, wellsites, pipelines and various levels of treating and handling facilities. Prior to the 1940s, all process control devices were mechanical (pneumatic or hydraulic) or electromechanical in nature. The invention of transistor in 1947 ushered in the electronic age, whereby sophisticated electronic sensors and control devices were quick to follow, and would eventually become indispensable in the industrial world. The petroleum industry has always been a leader in nurturing new technologies, and PCAT was no exception. By 1959, an oil refinery in Texas would turn out to be the first commercial deployment of a full-fledged computer-based control system in human history. With the heavy development of integrated circuit and mainframe computer during the 1960s and 1970s, and with the introduction of programmable logic controller (PLC) in 1969, automation quickly spread to all segments of the downstream business. All sorts of processing plants, oil refineries, petrochemical plants, pipeline and truck-based transportation networks, right down to the point-of-sale systems in our friendly neighbourhood gas stations would be automated in time. Nowadays, it would be a real oddity to find any such downstream facilities not operated by some forms of automation, and it is hard to imagine how such facilities could have been operated on what was largely a manual basis back then. On the upstream side though, progress had been very slow, and at times, nonexistent.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
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".