InSAR Natural Reflector Survey for Surface Heave and Steam Chamber Monitoring at a SAGD Site
Bibliographic record
Abstract
Abstract This paper presents a case study of the application of an Interferometric Synthetic Aperture Radar (InSAR) natural reflector (NR) survey to monitor surface heave and steam chamber growth at one of Suncor's steam assisted gravity drainage (SAGD) fields. InSAR is a satellite technology mainly used to monitor ground motion. An InSAR NR survey relies on back scattered signal from the ground surface itself as opposed to fixed corner reflectors (CRs) which are metallic trihedrals specially designed to reflect a radar signal. This allows a NR survey to cover large areas of ground compared to those carried out using corner reflectors, but at a lower accuracy. As such, InSAR NR surveys provide a potential way of monitoring surface heave that covers much of a SAGD field at very low cost. Results from the NR survey are very promising. First, the trends in the surface heave measured by the NR survey were almost as accurate as those measured by the CRs. In addition, the NR survey provided useful data over about 50% of the target area. This dense coverage was unexpected since the test site is covered by small trees and extensive areas of muskeg, which will interfere with the reflected radar signal. Reliable surface heave estimates could be made in topographically high areas and in areas with gentle slopes because of their drier nature. By contrast low lying areas that were generally wetter provided little to no reliable data, hence masking any surface heave. Benchmarking of InSAR NR data to temperature fall offs found that the NR measured heave compared well to steam chamber conformance on overall average trends. In addition, manually measured heave also compared well to steam chamber growth measured by 4-D seismic. In conclusion, results indicate that an InSAR NR surface heave survey provides a promising way of measuring surface heave and steam chamber growth over large areas of SAGD fields. It is complementary to 4-D seismic, but at as little as one hundredth of its cost. One of the limitations of a NR survey is that measurements must be taken in non-snow time. Areas of denser forest cover may not provide as much spatial coverage with NRs as was found at the target region discussed in this paper. InSAR coverage in these areas could be done using CRs.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".