Remote Monitoring of SAGD Operations with Satellite based InSAR
Bibliographic record
Abstract
Abstract Surface deformation, measured from space, provides a means to remotely monitor Enhanced Oil Recovery (EOR) activities. Using Interferometric Synthetic Aperture Radar (InSAR) to measure surface deformation allows for a common measuring stick with no access issues. Currently surface deformation is measured using many alternative approaches, some of which are less economical and not feasible at the regional scale when compared to InSAR. Information collected from InSAR could be used to develop an informed planning mechanism. Space based SAR data from January 2009 to August 2011 was examined over a SAGD site in Alberta, Canada. The data was extracted from the satellite archives and advanced InSAR processing techniques were applied to measure the surface deformation over the site. The InSAR results show the extreme variability in the ground conditions that are to be expected in the region. The surface deformation was measured using a combination of corner reflectors and infrastructure targets. The highly variable ground conditions make monitoring of subtle deformation signals (2.5 – 5 cm/yr) difficult to measure without the use of installed targets. The challenges of InSAR monitoring without installed targets are addressed to emphasize the importance of on-site ground control points. Furthermore, the surface changes observed with the InSAR results are related to reservoir changes. Geomechanical simulation is dependent on many rock physics parameters and complex geological frameworks. History matching with InSAR observation provides enhanced prediction and estimation of reservoir growth, which can inform decisions related to reservoir performance and caprock integrity. This paper is a result of a collaboration between the Alberta Energy Regulator, MDA Geospatial Services Inc. (MDA), and the Canadian Space Agency (CSA) to examine the requirements for rapid-turnaround measurement of ground deformation over enhanced oil recovery sites in the Alberta Oil Sands. Funding for this effort was partially provided by the CSA.
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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 | 0.002 |
| 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".