Long Term Water Management in Alberta’s Southern Athabasca Oil Sands Region – Using Modelling Tools to Evaluate Sustainability
Bibliographic record
Abstract
Extraction of natural resources in Canada’s Southern Athabasca Oil Sands (SAOS) region requires water in different processes. While every individual application to utilize water undergoes a strict approval process, cumulative impacts from multiple users are also a concern. Management of water resources is of primary concern for the regulators and the industry, which has formed the Canada’s Oil Sands Innovation Alliance (COSIA) to help lead such initiatives. Over the past 5-years, COSIA has undertaken the Regional Groundwater Solutions (RGS) project to evaluate the potential cumulative effects resulting from groundwater withdrawals and disposal associated with future in-situ bitumen production in the SAOS region. A numerical model of groundwater flow was developed and refined to evaluate potential impacts associated with future production growth scenarios, which explored operational uncertainty. Recognizing that hydrogeologic parameter values applied in the numerical model are based on spatially limited data and a regional conceptualization, prediction uncertainty from parameterization was explored using a null space Monte Carlo approach. A total of 300 realizations with independent parameter sets were developed to evaluate the likelihood of unacceptable cumulative drawdown and highlight areas of potential concern. This case study will provide an overview of the different methods and result that are helping decision-makers to quantify uncertainty to responsibly manage water resources in the SAOS region and help to ensure their sustainability into the future. Challenges associated with defining predictive scenarios and spatial visualization of the uncertainty will also be discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".