Insights from Monitoring of Heavy Oil Production in Peace River, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Since 1979 Shell Canada has operated the Peace River heavy oil field in the province of Northern Alberta. In 2002 Shell Canada started an extensive and ambitious reservoir-surveillance programme with the aim to improve the understanding of dynamic behaviour of the reservoir produced by cyclic steam stimulation. Repeated seismic time-lapse, continuous microseismic and surface tilt meter data have been acquired since late 2002, and this surveillance is continuing to date. Earlier work with these data established that steam injection induces complex fracturing inside the reservoir that govern the transport of heat and fluids which control the efficiency of this thermal extraction process [1]. Since this earlier report, further work has focussed on developing our capability in two key areas. First, we aimed to identify the distribution of reservoir heating using time-lapse seismic data in a quantitative manner. This has been achieved by quantifying changes in seismic velocity via inversion and the development of a rock physics model to explain and separate the effects of pressure, temperature and fracturing. Second, we aimed to provide more cost-effective and reliable surface deformation monitoring. This is now being realised using space-borne Interferometric Synthetic Aperture Radar (InSAR). Following an 18-month long field trial, we demonstrate field-wide monitoring of all uplift and subsidence induced by the cyclic steam stimulation. These data allow us to map the areal distribution of injected fluid volumes through time anywhere inside the reservoir.
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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 it