The Future of the Oil Sands Depends on One Thing: Totally Rethinking Everything
Bibliographic record
Abstract
Shell and Marathon have agreed to sell most of their oil sands operations. Exxon Mobil recently removed 3.5 billion bbl of the ultraheavy crude from its proved reserves because it no longer plans to develop them any time soon, following a similar move by ConocoPhillips. In a world with a surplus of oil production options, the oil sands suffers because the cost of development is high and the value of ultraheavy crude is low, with oil sands bitumen selling for USD 15/bbl less than the benchmark for light crude, and these projects require big financial commitments stretching out for decades. “They (oil sands) are the most expensive kid on the block and that is not a good place to be,” said Ron Sawatzky, a principal researcher for InnoTech Alberta, and chair of the SPE Canada Heavy Oil Technical Conference. “US shale producers are the biggest competitors for oil sands. We cannot match the nimbleness of those guys.” Among those competitors are ExxonMobil and ConocoPhillips, with large operations in unconventional plays in the US and Canada and significant oil sands operations. “We are seeing the same technologies and innovations driving lower costs and greater opportunities,” in the oil sands, Ryan Lance, chief executive officer at ConocoPhillips, said during a presentation at the March CERAWeek conference in Houston. But he keeps asking his oil sands team: “How can you do projects with a 3–5-year cycle time so you can adapt” to a business with big unexpected price swings. Those who work in the oil sands are learning how to react faster to prepare for when oil may again be in short supply. “At Suncor we will spend around USD 200 million on technology in 2017 because business as usual does not cut it,” said Gary Bunio, general manager, strategic technology for oil sands, at Suncor. “We are working on things we will put in over a couple years, and things that will take a decade.” The payoff for reviving future growth is huge—oil sands resources are estimated at 135 billion bbl according to the US Energy Information Administration—and there is cash flow to invest in technology. Efficient producers such as Suncor have reduced operating costs to turn a profit with oil prices as low as USD 35/bbl.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".