Investigating Hole Opener Stinger Length on Drilling Performance and Founder Point in Soft and Hard Rock Formations
Bibliographic record
Abstract
Abstract Drilling efficiency is crucial in reducing operational time and costs, primarily in high-cost environments where even minor improvements can lead to significant savings. Drilling bit configuration is one of the factors that improves drilling efficiency. Pilot holes in particular, have shown enhanced performance by improving the rate of penetration (ROP). However, previous studies have primarily focused on ROP improvements but have often neglected the impact on Mechanical Specific Energy (MSE), and have largely been limited to soft rock formations. This research aims to fill these gaps by investigating the influence of pilot hole length on drilling performance under different hole cleaning efficiencies for sandstone and gabbro as representative of soft and hard rock formations respectively. Experiments were conducted using a Large Drilling Simulator (LDS) with a hole opener configured with a 2-cutter Polycrystalline Diamond Compact (PDC) stinger bit and a 4-cutter PDC hole opener (HO) bit with 3 stinger length configurations: Full Face (FF), 1 cm pilot hole (1-PH), 2 cm pilot hole (2-PH), and 3 cm pilot hole (3-PH). Drilling performance was evaluated by varying Weight on Bit (WOB) from 3 kN to 15 kN and flow rates of 6 L/min (representing poor hole cleaning) and 30 L/min (representing efficient hole cleaning). The study focused on the impact of stinger length (and corresponding pilot hole length) on Rate of Penetration (ROP), Mechanical Specific Energy (MSE), and founder point. The results show that drilling with a stinger (as compared to full-faced drilling) improves ROP, reduces MSE, and delays reaching the founder point, highlighting the enhancement of the overall drilling performance. Varying stinger lengths impacted relative improvements in performance likely due to challenges clearing cuttings from deeper pilot holes.
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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) | 0.000 | 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) | 0.001 | 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".