SAGD Production Observations Using Fiber Optic Distributed Acoustic and Temperature Sensing: "SAGD DAS - Listening To Wells to Improve Understanding of Inflow"
Bibliographic record
Abstract
Abstract Performance optimization of steam assisted gravity drainage (SAGD) well pairs requires awareness of unique and sometimes complex downhole processes. Reservoir monitoring tools commonly used to characterize the downhole pressure and temperature environments include thermocouples, pressure gauges, and discrete or distributed fiber-optic sensors. Distributed temperature sensing (DTS), the most common fiber-optic measurement used for SAGD reservoir monitoring, has been widely adopted for SAGD production monitoring due to its ability to accurately measure a wide variety of temperatures in harsh environments. High-measurement density along the entire SAGD well length has proven to be useful for both production optimization (Krawchick et al. 2006) and well-integrity applications. Though DTS monitoring is a primary downhole measurement tool for thermal production, other sensors may further characterize the nature of SAGD well performance when used in conjunction with DTS. Alone, temperature and pressure measurements may not yield a complete understanding of the inflow contribution in SAGD production wells. For instance, the effects of complex heat transfer may mask reservoir temperatures. Additionally, high temperatures are not always indicative of inflow and cooler liner temperatures may not signify the absence of production contribution. Distributed acoustic sensing (DAS), which is used to measure acoustic frequency and intensity in 1-m intervals along the length of a fiber-optic line, is another downhole measurement tool currently being evaluated for its ability to provide additional downhole wellbore information. Although DAS has been commonly used to characterize the acoustic environment in hydraulically fractured horizontal wells (MacPhail et al. 2012, Holley et al. 2015), it has not been extensively applied in SAGD well pairs. This paper shares select DAS and DTS monitoring data from a pilot well, the results of which improved the operator's understanding of the nature of the SAGD production. In late 2012, Devon Canada installed DTS multi-mode fiber in several production wells at SAGD assets in the McMurray Oil Sands. Single-mode fiber utilized for DAS were deployed in conjunction with multi-mode fiber, allowing simultaneous logging of DTS and DAS data throughout the wellbore. Temperature and acoustic datasets were obtained at different representative flow conditions, including stable production, rate step-down, early time shut-in, and well startup. The combined analysis of DTS, DAS, and surface production data shows that DAS was able to identify steam flashing and qualitatively define production inflow contribution and gas/liquid composition. Due to the complex, bi-directional flow in the trial well, some of these conclusions would not have established without the observations obtained from DAS monitoring.
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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 | 0.000 | 0.001 |
| 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".