Characterizations on Geomechanical Properties of Colorado Shale Based on Well Logging and Laboratory Testing
Bibliographic record
Abstract
Abstract Geomechancial properties of shale are essential to drilling operation and well design for thermal enhanced oil recovery. In this research, well logging and laboratory testing data are integrated to characterize the geomechanial properties of Colorado shale in Cold Lake area, Alberta, Canada. Density and sonic logs were applied to estimate the dynamic deformation modulus, Poisson's ratio, internal frictional angle, and cohesion strength. Gamma ray log is applied to estimate shale's clay content. Triaxial tests and confined torsion tests were conducted on samples to investigate transversely isotropic stiffness parameters and strength at quasi-static condition. The micro fabric characteristics of the clay shale were obtained from SEM images to investigate shale's intrinsic anisotropy. Correlations between geomechanical properties derived from well logging and laboratory testing were generated. Values of deformation modulus and Poisson's ratio derived from laboratory testing and well logging were correlated by introducing empirical coefficients. The anisotropic ratio in rock's Young's modulus is correlated to shale's clay content. The values of log-derived internal frictional angle are consistent with the laboratory tested values. The shale's clay content is found to be an important factor affecting the rock's strength.
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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 | 0.000 | 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".