Effect of High Temperature on Physical and Mechanical Properties of Clay Shales (Caprock)
Bibliographic record
Abstract
The complex structure of heavy crude oil and bitumen requires enhanced oil recovery methods such as cyclic steam stimulation and steam-assisted gravity drainage to recover effectively. These methods are operated at high temperature ranges. Thus, it is crucial to understand the effect of high temperatures on such operations. This thesis aimed to investigate the changes in physical and mechanical properties of clay shale caprock due to pre-heating under high temperatures. For this purpose, intact clay shale cores were recovered from a site near Long Lake in Alberta. To investigate the effect of pre-heating on clay shale's strength and deformation characteristics twelve samples were trimmed and prepared. Samples were pre-heated up to 20, 150, 300 and 600 °C and thereafter cooled down to ambient temperature naturally. The variation of mechanical properties of clay samples due to the pre-heating was examined in triaxial compression tests. Changes in the composition and crystalline structure of the clay samples were determined through X-ray fluorescence and X-ray diffraction analysis. The experimental results indicated clay samples becoming stiffer (with increased Young’s modulus and lowered Poisson’s ratio) with increasing temperature. Also, the friction angle and cohesive strength of the clay sample increased with temperature. These findings indicate an increasing trend in the mechanical strength of clay samples with temperature. The failure of the clay samples at low confining pressure was mainly due to shear dilation, while compression was the cause of failure at higher confinement pressure.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".