Experimental Study for the Validation of Drilling Optimization Model for Improved Performance in Hard Rock Formations
Bibliographic record
Abstract
Lower Rate of Penetration (ROP) is one of the key challenges while drilling through deeper hard rock formations to access geothermal reservoirs in an advanced geothermal system (AGS). Rotary percussive drilling (RPD) becomes crucial for such systems in increasing ROP. The transferability of hammer drilling operations in deeper and harder rock formations, the mechanisms behind the improved drilling performance, and a nuanced understanding of the drilling dynamics of the method remains to be a critical challenge for a full-scale application of RPD in advanced geothermal systems. This study addresses these challenges through experimental validation of a developed bit performance index (BPI) model. The BPI model was developed using a reward function, the outcome is either rewarded or penalized. The concept of reward function usually exists in optimization and machine learning models. Using the combination of bit geometry, RPM, and hammer frequency, the function predicts optimum drilling dynamic parameters to maximize the ROP under given conditions. A series of experiments, limited to atmospheric pressure and ambient confining stress, were conducted using state-of-the-art hammer drop equipment developed in the laboratory to validate BPI, using dome-shaped diamond percussion inserts on granite rock samples. Efficiency estimates and volume removed by impacting the rock surface at different locations while moving away from the center of the crater were determined through these experiments. The results followed the BPI model – maximum efficiency and volume removal at the edge of the crater as compared with impacts at the center and center-edge. This study provides a novel approach to the characterization of drilling performance using RPD and gives a pathway for improved drilling performance through optimization of drillstring dynamics in deeper, harder rock formations for geothermal well drilling.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".