A utilização da tecnologia InSAR na caracterização da deformação superficial do terreno no campo petrolÃfero de Canto do Amaro-RN
Bibliographic record
Abstract
The Canto do Amaro field is the largest actively producing onshore hydrocarbon reservoir in Brazil, and has been in operation since its discovery in 1985. The field is undergoing enhanced oil recovery activities to support production. A total of 30 COSMO-SkyMed SAR images acquired over a period of 15 months were processed for this analysis. TRE Canada Inc. (TRE) in partnership with PETROBRAS, Rio de Janeiro State University (FGEL-UERJ) and Geomath Applied Remote Sensing Ltd. have prepared the following article as a demonstration of the capabilities of InSAR technology for monitoring surface deformation over an actively producing oil field. The following points summarize the key features of this article: (i) Surface displacement over the Canto do Amaro field was comprehensively characterized by the high density of measurement points obtained from the SqueeSARTM analysis; (ii) Several distinct features of uplift were identified over areas that appear to be undergoing active operations with up to +87 mm of uplift observed; (iii) Mild subsidence was also observed within the area; (iv) The time series of the measurement points provided an indication of non-linear changes in movement trends over time, and can be used to isolate unique displacement patterns within the results. The time series could also be compared to other monitoring results, including DGPS measurements and; (v) Cross sections and average time series over several features of movement within the area of active operations provide additional insight into the results over multiple spatial scales.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".