InSar Monitoring In Heavy Oil Operations
Bibliographic record
Abstract
Abstract SAR Interfeometry (InSAR) provides high precision ground displacement measurements remotely, using Synthetic Aperture Radar (SAR) images acquired from satellites. Thanks to its effective provision of extensive information over wide areas with high acquisition frequency, InSAR monitoring is used routinely in the management of numbers of Enhanced Oil Recovery (EOR) projects. These include heavy oil Cyclic Steam Stimulation (CSS), Steam Flooding (SF) and Steam Assisted Gravity Drainage (SAGD) in Alberta and California. Steam injection recovery is generally operated in shallow reservoirs with low caprock thickness, where measuring the surface effects of pressure variations at depth is extremely useful to assess steam chest expansion and enhance safety. InSAR monitoring provides low-cost effective measurements over large areas and is capable of highlighting zones of excessive pressure or subsidence, as well as to control the integrity and safety of operations and infrastructures. This paper presents an overview of InSAR technologies and their recent enhancements. Some examples of InSAR application in EOR heavy oil projects are reported in order to highlight the advantages offered by these monitoring techniques in reservoir management and recovery optimization.
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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".