Optimization of perforation drilling for mitigating punch-through in multi-layered clays
Bibliographic record
Abstract
“Spudcan” foundations for mobile drilling rigs continue to exhibit a high failure rate in the offshore oil and gas industry. The more frequent use of larger jack-ups in highly stratified regions, such as the Sunda Shelf in Southeast Asia, contributes to this concerning increase in “punch-through” incidents, which can lead to buckling of a leg or even toppling of the rig. An industry practice known as “perforation drilling” is sometimes used to mitigate the punch-through risk in layered clays, extracting soil from the upper strong layer before the jack-up is installed. This paper reports results from centrifuge model tests exploring the efficiency of perforation drilling. The soil conditions tested simulate offshore strength profiles that have reported punch-through failures. An experimental method for “drilling” sites in an enhanced gravity centrifuge environment was developed and the installation responses of model spudcan foundations penetrating through multi-layered clays with interbedded stiff layers were recorded. The experimental results show that the removal of soil inside the spudcan perimeter, with an area of 9% perforated, eliminated rapid leg run and severe punch-through on the two- and four-layer seabed profiles tested. This confirms the effectiveness of perforation drilling and indicates how the offshore drilling plan may be optimized.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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 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".