Introduction of the Concept of Probabilistic Risk Assessment for Evaluating the Borehole Stability in Horizontal Directional Drilling
Bibliographic record
Abstract
Annular pressure analysis that determines the likelihood of hydraulic fracture occurrence is critically important for evaluating the feasibility of horizontal directional drilling (HDD). The traditional method of annular pressure analysis in HDD makes insightful comparisons between minimum required annular pressure, Pmin, and maximum allowable annular pressure, Pmax, along the bore path. However, such a method seems deterministic rather than considering the potential variability in estimates of annular pressure (both Pmin and Pmax) due to the uncertainties within the parameters and models used. This paper proposes a concept of probabilistic risk assessment that allows incorporating such uncertainties in the annular pressure analysis in HDD. For a better understanding, the newly proposed concept of probabilistic annular pressure analysis in HDD is discussed in relation to the probabilistic method used in other geotechnical engineering applications. Lastly, information required for developing risk criteria for evaluating the risk of hydraulic fracture in HDD is presented.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".