Multi-Lateral Well Construction by Directional Steel Shot Drilling: Optimisation of the (Mechanical) Specific Energy Utilisation
Bibliographic record
Abstract
Summary Multi-lateral well construction can be an effective tool to overcome challenges of reservoir heterogeneity and related uncertainties in production rates and projects economics. The drilling costs for multi-lateral well construction are high when performed with standard (mechanical) rotary steerable systems. Until recently, multi-lateral technology has rarely been considered for geothermal sites. The directional steel shot drilling (DSSD) technology developed by Canopus has the potential to enable multi-lateral drilling and lower costs by utilization of steel shot erosive action added to the drilling process. The energy available for rock removal is utilized more efficiently and a novel steering principle is introduced. The performance of the DSSD system has been investigated within the GEOTHERMICA ‘DEPLOI the HEAT’ project by full-scale lab experiments at TNO’s RCSG facility and field testing in Switzerland. The drilling tests have proven the optimized (mechanical) specific energy utilization and highlighted the improved rate of penetration during combined mechanical and steel shot drilling action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".