Deposition thickness and evaporative drying for oil sands tailings in northern Alberta
Bibliographic record
Abstract
Directive 074 issued by Alberta Energy Resources Conservation Board (ERCB) requires that fine tailings materials deposited each year must achieve a minimum undrained shear strength of 5 kPa within one year following deposition. Challenged with this requirement, oil sands owners and operators in Alberta, Canada, are evaluating several opportunities for tailings dewatering. One opportunity is taking advantage of the evaporation potential, following deposition, to assist with dewatering tailings. Three types of tailings, namely, non-segregated tailings (NST), mature fine tailings (MFT), and treated thickened tailings (TT) were generally produced by Shell Canada Limited’s Albian Sands Energy operation with different treatment technologies. These three types of tailings have various consolidation behaviours and initial solids contents, which imply that the tailings deposition thickness will be different from each other in order to achieve the ERCB-D 074 requirements while utilising evaporative drying to dewater the fluid tailings. The average annual precipitation and potential evaporation are approximately 470 and 640 mm, respectively in the northern Alberta area. A simplified methodology based on tailings consolidation properties, meteorological data, and initial state of tailings deposits is presented in this paper to determine appropriate fine tailings deposition thicknesses, in the context of the exceedance probability of achieving the desired solids content (and hence undrained shear strength), for different times of the year. The maximum yearly deposition thickness with 80% probability of exceedance is approximately 200 cm for NST with enhanced initial deposition solids content, approximately 160 cm for enhanced TT, and approximately 144 cm for enhanced MFT. The methodology presented in this paper can be used to develop understanding for potential tailings deposition thickness that will have a high probability of achieving the target solids contents and shear strengths due to evaporative drying. Site-specific conditions can then be used to optimise fine tailings management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".