Improved Monitoring System for Heavy-Oil Steam-Assisted-Gravity-Drainage Wells
Bibliographic record
Abstract
This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 187441, “Improved Monitoring System for Heavy-Oil Steam-Assisted-Gravity-Drainage Wells,” by Christopher Baldwin, Weatherford, prepared for the 2017 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 8–11 October. The paper has not been peer reviewed. The complete paper provides an overview of the development of fiber-optic sensing for steam-assisted-gravity-drainage (SAGD) applications, including a review of more than 10 years of work in development and field applications in western Canada. Information provided in this paper is applicable beyond SAGD applications. The fiber-optic monitoring systems might also be used for intelligent, subsea, and unconventional wells. Introduction One area that has benefited from the unique advantages of fiber-optic sensing is thermal monitoring of SAGD wells. One particular fiber-optic technology that has proved successful in these thermal-monitoring applications is wavelength-domain multiplexing of Bragg-grating-based fiber-optic sensors. Commonly referred to as array temperature sensing (ATS), the Bragg-grating arrays can be manufactured splice-free for long-term reliability. The number of thermal sensing points can range from a few to well over 100 for a single cable. Each Bragg-grating sensing point provides a real-time, accurate measurement of temperature at that location along the wellbore. ATS systems have been used successfully to monitor the steam-injection and production processes in SAGD wells. ATS systems are capable of performing more-accurate temperature measurements and are able to resolve much smaller temperature features compared with distributed temperature sensing (DTS). Bragg-grating optical sensors not only provide temperature measurements but also can be packaged in a transducer to provide pressure and temperature (PT) also can be packaged in a transducer to provide pressure and temperature (PT) readings. The optical PT gauge is compatible with the surface instrumentation that records the ATS responses, allowing for the PT gauge to be integrated at the end of the ATS optical fiber for single-wellhead penetration, providing multiplexed DTS and PT measurements. Environmental Effects on Downhole Fiber-Optic Sensors The requirements for a successful monitoring system in SAGD applications are dictated by the harsh environments experienced downhole. In the past, the presence of hydrogen in these applications has caused issues with various fiber-optic sensing technologies. Improvements in optical-fiber design and downhole cable construction have helped to mitigate some of these issues, but any downhole monitoring system must be able to cope with the presence of hydrogen in relatively high-temperature environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".