Geomechanical Charaterization of Montney Turbidites for Secondary Carbon Storage in the Western Canada Sedimentary Basin
Bibliographic record
Abstract
Summary To meet global net-zero emission goals by 2050, Canada must make substantial contributions to reducing greenhouse gas emissions and identifying carbon dioxide (CO2) storage reservoirs. In the Western Canada Sedimentary Basin (WSCB), the Lower Triassic Montney Formation contains extensive turbidites that are promising reservoirs yet remain under-characterized geomechanically, especially under Supercritical (Sc-CO2) exposure. This study assesses the integrity of Montney turbidites which primarily consist of siltstones and sandstones using laboratory testing and log integration. Core plugs were conditioned to represent dry, brine-saturated, and Sc-CO2 environments and were evaluated by Micro-CT, Ultrasonic velocities, Brazilian tensile strength, Uniaxial compressive strength, and Multistage triaxial tests. Findings highlighted lithologic controls on stiffness and strength, and a consistent separation between dynamic and static moduli. Siltstones tend to be stronger yet more chemically responsive while sandstones displayed steadier behaviour across fluids. Exposure to Sc-CO2 and brine alters elastic response and failure behaviour in ways predictable from texture enabling calibration between core measurements and logs. The research concludes that the Montney turbidites are mechanically competent for storage, with sandstone-rich intervals offering greater resilience during injection. The research provides parameters to assess containment risk and model reservoir as a potential for carbon capture and underground storage in WCSB.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".