Uncertainty Analysis in Unconfined Rock Compressive Strength Prediction
Bibliographic record
Abstract
Abstract Lots of sources can be used to derive unconfined rock compressive strength (UCS) along the wellbore as a key input of failure criteria for wellbore stability analysis. Laboratory tests are the most accurate methods to obtain rock strength which mainly are destructive and result in a set of discrete data. On the other hand, non-destructive methods (log based approaches), deliver a continuous rock strength log, however, the accuracy of these techniques is still dubious and they should be calibrated with lab data. If the evaluated rock strength contains some errors, final mud weight window will be affected by these errors which lead to inaccurate mud weight selection for safe drilling operation and consequently increase in total well cost. Whole data in UCS log shall be multiplied or added by a number to match laboratory results. This calibration approach may superficially show good match between results from log and laboratory, though all data may not represent real UCS and show same uncertainty. In this paper, a modified calibration approach based on lithology, compaction, fluid content and porosity is proposed for calibration of UCS logs. Performed cross validation results show a good match between laboratory data and modified UCS profile.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".