Techbits: ATW Leads to Formation of Group To Promote In-well and Subsea-Based Fiber-Optic Monitoring Systems
Bibliographic record
Abstract
After the first in-well optical-sensing-system installation in 1993, the oil and gas industry has been pursuing optical sensing as a means of improving reliability and developing new capabilities for in-well reservoir-monitoring applications. Since that first installation, the industry has built a track record with more than 100 installations of permanent pressure and temperature gauges, optical flowmeters, and seismic arrays and hundreds of distributed-temperature-sensing (DTS) installations. But virtually all of these installations have been in dry-tree and land wells. In 2005, discussions began on soliciting SPE's help in organizing an Applied Technology Workshop (ATW) that would increase awareness in the upstream industry of the potential for widened application of in-well optical-sensing systems in subsea applications and help identify the challenges and barriers facing widespread market acceptance—and then possibly lead to a plan that addresses these obstacles. After a full year of preparation, an SPE ATW titled "In-Well Optical Sensing—Subsea Well Applications: Are We Ready?" was held in February 2006 in Galveston, Texas. Cochairing the event were Brian Drakeley (Weatherford International) and Brock Williams (BP), and they were joined on the Technical Program Committee by Lars Vinje (Statoil), Herbert Lescanne (Total), Brian Llewellyn (Chevron), Rod Fors (Shell), Garth Naldrett (now of FloDynamic), Ted Drell (FMC Technologies), and Matthew Smith (Deutsch), representing a cross section of companies involved with this emerging technology. The workshop was attended by approximately 130 people from Brazil, Canada, France, Germany, Indonesia, Japan, Norway, the United Kingdom, and the US. Informative educational sessions were held, starting with presentations covering the types of in-well optical-sensing systems currently available and those under development. These includedPressure and temperature sensorsFlow and fraction metersOptical accelerometers for seismic sensingDistributed temperature sensingDistributed pressure sensing A number of case histories for these systems were also presented, which varied from the good to the bad to the downright ugly! The openness and honesty of a number of the presentations and the ensuing discussions were very much appreciated by those present and set the ATW off to an excellent start.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.225 | 0.134 |
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".