Innovation, Motivation, and Fear: A Novel Perspective for Unconventional Oil
Bibliographic record
Abstract
Abstract The focus of this paper is innovation in oil sands recovery technology. Canada hosts the third largest reserves of petroleum in the world, mostly in heavy oil/oil sands reservoirs. The two commercial in situ recovery technologies, Cyclic Steam Stimulation and Steam-Assisted Gravity Drainage, were both invented >30 years ago; both use large amounts of water and emit carbon dioxide. Industry is facing a critical point where it is imperative to find new technologies. It has been a significant challenge to find new processes with large reductions of water and carbon dioxide emissions. Another critical issue is adoption time scale – in the past, new technologies have taken 10–20 years to become commercial – this pace must be accelerated. The oil sands industry needs to improve the innovation cycle of oil sands extraction technologies. The objective here is to understand how to do this, to describe factors that encourage and discourage innovation, and to recommend strategies to enable and stimulate non-incremental innovation. It is interesting to note that despite only a few oil companies still having research laboratories and permanent research staff, abundant potentially inventive scientific, engineering, and management staff exist in oil companies. So the question becomes: what is preventing them from developing a plethora of inventions and bringing creativity to issues confronted by the oil sands industry? It does not appear to be only a technical issue but also a social one. Market, resource, and social issues lead to this result: most petroleum funding directed at near market iterations, short term incentives (increasing shareholder value), government funding matched to current industry activity and thus linked to market forces, low funding levels, culture of risk adversity and fear of risk, innovation curbed by regulatory factors or internal work overload, and high costs due to investment, variable resource quality, high capital costs, and oil price volatility.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".