The Effects of Pore Pressure and Temperature Difference Variation on Borehole Stability
Bibliographic record
Abstract
Considering that drilling fluid filtration and the temperature difference between borehole wall rock and drilling fluid can cause the stress variation of the borehole wall. The stress distribution model was derived under the effect of thermal-flow-solid coupling. The safe mud density window calculating model considering pore pressure and temperature difference variation was established according to Moore-Coulomb criterion and borehole wall rock tensile failure criterion. The result calculated by the model can be expressed as follow. (1) When the temperature difference between borehole rock and drilling fluid is constant, with the enhancement of fluid filtration, borehole rock pore pressure increasing, the collapse pressure increasing, breakdown pressure decreasing, the stability of the borehole becomes deteriorating. (2) When the borehole wall rock pore pressure is constant, if drilling fluid makes wall rock temperature decreasing, with the temperature difference increasing, both the collapse pressure and breakdown pressure decreasing, the stability of the borehole becoming deteriorating, it is not conducive to drilling safely. If drilling fluid make wall rock temperature increasing, with the temperature difference increasing, both the collapse pressure and breakdown pressure increasing, the borehole tending to stabilize, it is conducive to drilling safely. Key words: Pore pressure; Temperature difference; Borehole stability; Safe mud density window
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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 | 0.002 |
| 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 | 0.001 |
| 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".