Deployment of the Reelwell Drilling Method in a Shale Gas Field in Canada
Bibliographic record
Abstract
Abstract The Reelwell Drilling Method (RDM) is a multi-purpose system incorporating a unique flow arrangement. It employs conventional drillpipe into which is fitted an inner string to form a concentric drill string. This arrangement allows the return fluid containing drill cuttings from the bottom of the well to be transported back through the inside of the drill string. The technique enables improved hole cleaning and improved downhole pressure control, and has unique features for application to managed pressure and extended reach drilling operations. Development of the RDM started in 2004 and has since then been through several full scale tests. In the fall of 2010 it was deployed in a shale gas well in Canada. The main goal was to demonstrate the system in a live gas well and to gain field experience of the technology. The system was used in both the vertical and horizontal sections of the well. The concentric drill string was used for drilling the whole well in several bit runs, with circulation in both conventional and concentric circulation modes. The well was drilled to 4250 m MD in 8¾" hole size. The operation validated the concept and demonstrated a practical implementation on conventional drilling rigs. The operation is an important step in the evolution of a technology, which has a significant potential to improve operational efficiency and thereby the recovery of petroleum resources.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".