The Use of Satellite-Based Radar Interferometry to Monitor Production Activity at the Cold Lake Heavy Oil Field, Alberta, Canada
Bibliographic record
Abstract
Abstract The Cold Lake heavy oil field has been studied by geoscientists for more than 30 years and has been producing bitumen for 20 years using the cyclic steam stimulation (CSS) process. Future development options can be improved by the resolution of steam movement and the avoidance of areas of faults and fractures. To locate these features, remote sensing has recently been investigated as a cheaper alternative to four-dimensional seismic surveys. Advances in satellite and radar technology have made it possible to measure very small movements of the earth's surface found in earthquake zones and volcanic regions. The technique uses synthetic-aperture radar interferometry (InSAR), allowing the measurement of deformation using a vertical resolution, in optimal conditions, on the order of millimeters between repeat acquisitions. This accuracy has only been achieved over dry areas without significant vegetation growth. At the Cold Lake oil field, it has been reported previously that the injection of steam to mobilize the bitumen causes the pump jacks to heave and subside by as much as 30 cm during the first steam cycle. The present project was instigated to determine if such small positive and negative vertical movements could be resolved over the field. Data from three satellite radar sensors were selected: ERS (European remote sensing satellite), JERS (Japanese remote sensing satellite) and Radarsat (the Canadian radar satellite). The present article has provided the first results of repeat-pass InSAR using JERS SAR data for the land subsidence application area. Contrary to the widespread belief that this technology can only be used for dry areas, we show that accurate results, on the order of a centimeter in resolution, can be obtained in a forested area such as Cold Lake using the JERS L-band spaceborne SAR system.
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 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 | 0.000 |
| 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".