Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 180726, “SAGD Production Observations With Fiber-Optic Distributed Acoustic and Temperature Sensing,” by Warren MacPhail and James Kirkpatrick, Devon, and Ben Banack, Bryan Rapati, and Alex Ali Asfouri, Halliburton, prepared for the 2016 SPE Canada Heavy Oil Technical Conference, Calgary, 7–9 June. The paper has not been peer reviewed. Distributed temperature sensing (DTS) is the most common fiberoptic measurement used for steam-assisted- gravity-drainage (SAGD) reservoir monitoring. In 2013, Devon Canada installed DTS in the trial production well at its Jackfish 2 asset. DTS was installed parallel to existing standard instrumentation. In early 2015, a distributed-acoustic-sensing (DAS) fiber-optic line was added in parallel along with multimode optic fiber, allowing simultaneous logging of DTS and DAS. DAS helped to improve confidence in and extend the definition of wellbore effects observed with DTS. Background DTS with a fiber-optic cable has been established as a reliable and tested method for monitoring thermal wellbores in heavy-oil production. For its part, DAS has been established as a valuable diagnostic tool in unconventional reservoirs as a completion, stimulation, and production-monitoring tool. DAS interrogators turn a fiber-optic line into an array of thousands of virtual microphones, picking up acoustic signatures across a broad range of frequencies. In SAGD production, DAS has generated significant interest throughout the industry as a potential new reservoir- monitoring tool.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".