Bibliographic record
Abstract
Horizontal wells are used in some geological settings in the petroleum industry to produce methane from coal seams. Horizontal directional drilling is used in the mining industry to enhance the effectiveness of coal degasification procedures and to aid in the delineation of coal reserves. Coals tend to be mechanically weak, hence they are prone to borehole instability related problems during drilling, completion, and production operations. This paper includes a review of the mechanical properties of selected coals and provides two empirical cross-plots that can be used for estimating coal strength from index tests and geophysical logs. Linear elastic borehole stability models are demonstrated to be appealing because they are easily implemented, require a minimum of input data, and are well suited to rapid parameter sensitivity analyses. Using experience obtained drilling vertical wells in a given setting, a methodology is described for calibrating linear elastic models to provide realistic borehole stability predictions. Furthermore, as demonstrated using a western Canadian example (a shallow well in the Ardley coal zone), relatively simple elastoplastic models can be used effectively for borehole stability analyses. The important effects of filter cake, coal depth, and rock strength anisotropy are demonstrated with two different elastoplastic models.Key words: borehole stability, coal, methane, directional drilling, strength, stress.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".