Storage of oil sand waste in sub-surface salt caverns: a feasibility analysis based on experimental rock mechanics and geological setting
Bibliographic record
Abstract
In order to reduce the environmental impact of heavy oil production operations in East-Central Alberta, hydrocarbon energy companies have been disposing production-related waste in solution mined sub-surface salt caverns. These caverns are situated in two bedded salt layers separated by approximately 150 m of mudstones and a thinner salt layer. In order to optimize well usage and improve project efficiency, two salt caverns per well are proposed, one in each salt formation. This study aims to provide a feasibility analysis for the proposed sub-surface storage based on a study of the geological setting combined with rock mechanical experiments. Analysis of the local and regional geology of the proposed cavern operation reveals a simple stratigraphy and tectonic stability. This implies that the project area is suitable for a salt cavern operation from a geological perspective. A total of 59 geomechanical laboratory experiments were performed on core samples to constrain the geomechanical properties of the formations involved in the salt cavern operation. These results were implemented in a FLAC3D (Fast Lagrangian Analysis of Continua in three dimensions) numerical model to investigate cavern stability, creep rates and surface subsidence. The results from this model imply that surface subsidence will be minimal (<12.1 mm over 50 years), shear stresses around the cavern will be low (<5MPa) and that failure will not occur.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".