2. Seismic Properties of Heavy Oils — Measured Data
Bibliographic record
Abstract
Introduction With a high demand of hydrocarbon worldwide, conventional oil production is quickly approaching its peak. Inevitably, heavy oil and bitumen (ultraheavy oil) will emerge as “new” (so-called unconventional) hydrocarbon resources because of their tremendous potential. Currently, more than 50% of Canada's oil production is from heavy oils (Alboudwarej et al., 2006; Hinkle and Batzle, 2006). Such heavy oils are highly viscous, difficult to move in reservoirs, and much more expensive to produce. In addition to mining and other cold production methods, many different techniques (e.g., thermal, chemical, or in situ combustion, etc.) have been applied to mainly reduce viscosity and assist the heavy-oil production. None of these techniques have matured completely yet, and engineering developments are occurring rapidly. These techniques remain expensive in terms of energy and resources used (lots of water) and in terms of efficiency and overall environmental impact. The steam-assisted gravity drainage (SAGD) technique is a current popular technique. In a steam chamber, more than 60% of oil in place can be produced (Caruso, 2005; Gupta, 2005). However, on a reservoir scale, efficiency can be low (approximately 15% with different resources). Clearly, seismic techniques hold great potential for assisting reservoir characterization and recovery monitoring. Monitoring has been demonstrated successfully in several fields [Cold Lake (Eastwood et al., 1994) and Duri Field, Indonesia (Jenkins et al., 1997)]. However, to be effective, we must understand the seismic properties of the heavy oils and the heavy-oil sands. This understanding of in situ properties is the key to bridging the seismic response to reservoir properties and changes. Schmitt (2004) provided a general review of rock physics as related to heavy-oil reservoirs. Here, we examine the seismic properties of heavy oils in detail.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".