Optimizing Well Design and Delivery for Wellbore Stability Management by Minimizing Subsurface Uncertainties
Bibliographic record
Abstract
Abstract Modeling instability in shales is pertinent to managing risk and uncertainty associated with drilling troublesome formations. Design parameter such as Unconfined Compressive Stress (UCS) is usually estimated from sonic or other porosity logs and calibrated with results of triaxial test from core samples. However, sonic logs may not be available for some wells, making UCS estimation difficult thereby increasing uncertainty. In this work, a compositional approach was used to evaluate UCS and the Volume of Shale (Vsh) of the rock was modelled from the spectral gamma ray (SGR) or gamma spectrometry as a measure of shaliness instead of the gamma ray. This is because the sandstone sequences as obtained in Niger Delta fields contain some non radiactive clay which gamma ray log may not delinate clearly. Subsurface geological issues like bedding plane and faults were also modelled and the estimated UCS at various intervals and lithologies were calibrated with offset well data. Field results showed consistent and reliable estimates of UCS in the absence of Sonic logs with a percentage error of ± 0.0208. The predicted safe drilling mud weights must be accompanied by good drilling practice and proper hole cleaning. ECD and fracture gradient issues were captured to prevent excessive loss circulation. Instability risk were minimized and the case study well presented showed consistent result.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".