Bibliographic record
Abstract
Abstract This paper provides an overview of the development of fiber optic sensing for SAGD applications including a review of more than 10 years of work in development and field applications in Western Canada. Development of a fiber optic monitoring system to withstand the harsh environments experienced in the SAGD application requires consideration of the materials used to protect the downhole optical fiber, the chemistry of the downhole optical fiber, the basic optical fiber sensing technology implemented, and the optical interrogator functionality. Aspects covering corrosion of materials, hydrogen darkening, and system performance for different monitoring applications will be explored. Design and technical information will be provided along with certain case histories providing the applications and use of downhole fiber optics sensing for the SAGD applications including steamflood monitoring, sub-cool monitoring, and well integrity monitoring. Dealing with a harsh environment experienced downhole in the Western Canada SAGD applications including temperatures exceeding 230° C in high hydrogen environments has meant that the fiber optic sensing technologies have had to adapt and improve to be suitable to function reliably. Various technologies have been implemented throughout the previous 10 to 15 years, from Raman scattering based DTS systems to highly multiplexed Bragg grating based thermal monitoring systems. Multiple optical PT gauges have been designed and implemented. These include Fabry-Perot based systems as well as the Bragg grating based PT gauges. This paper will provide much-needed information to practicing engineers in the field of downhole reservoir monitor introducing them to concepts such as multi-point fiber Bragg grating based thermal monitoring and reliability of the monitoring systems. Information provided in this paper is applicable beyond the SAGD applications. The fiber optic monitoring systems may also be used for intelligent wells, subsea wells, and unconventional wells.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".