Review of Water Use at Canada's Oil Sands Points Toward Environmental Sustainability
Bibliographic record
Abstract
This article, written by Editorial Manager Adam Wilson, contains highlights of paper SPE 156676, ’Water Use in Canada's Oil-Sands Industry: The Facts,’ by Stuart Lunn, Imperial Oil Resources, prepared for the 2012 SPE/APPEA International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Perth, Australia, 11-13 September. The paper has not been peer reviewed. Concerns have been expressed and published about the amount of water used in Canada’s oil-sands industry. The oil-sands deposits are geographically separated from population and agricultural centers in the province of Alberta and are within some of the most prolific river basins. Analysis shows that the amounts of water used by oil-sands operations are low and sustainable. A track record of continuous improvements at existing operations and the application of new technologies will maintain the sustainability into the future. Introduction Canada’s oil sands are in three deposits in northern Alberta (Fig. 1). The oil-sands deposits hold 1.8 trillion bbl of oil with 169 billion bbl of economically recoverable reserves. This represents 97% of Canada’s oil reserves, which are the third largest in the world. The term “oil sands” is used to describe unconsolidated bituminous sands. The oil saturation in the sands has very high viscosity and is commonly called bitumen or tar. The deposits are found within the McMurray, Clearwater, and Grand Rapids formations of the Mannville group. They are of varying depth, from near surface in some parts of the Athabasca deposit to more than 300 m deep in the Peace River and Cold Lake deposits. Where the oil sands are shallower than 70 m, they may be mined by surface strip mining. This represents 3% of the surface area of the oil sands and 20% of the reserves. The remaining 80% of reserves across all three deposits are accessible only by use of in-situ recovery methods. Both mining and in-situ methods are water based. How Water Is Used in Canada’s Oil Sands Because of the high viscosity of the bitumen within the oil sands (8–12°API, >50,000 cp), it does not flow easily and is difficult or impossible to recover with conventional oil methods. The vast majority of commercial operations rely on hot water or steam to reduce the viscosity of the bitumen to allow its recovery. For in-situ methods, this involves the injection of steam into the oil-sands reservoir and subsequent recovery of the bitumen once it is reduced in viscosity. For mining, the water is used to slurry the oil, transport it, and finally separate it from the ore.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".