Insights from Monitoring of Heavy Oil Production in Peace River, Canada
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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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 | 0.001 | 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 | 0.000 |
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".