Bibliographic record
Abstract
For the past quarter of a century, underbalanced drilling (UBD) has been a niche technology known to many and practiced by few. It has seen acceptance in the North Sea, where UBD is used to drill through depleted reservoirs. In the Middle East and North Africa, it has been recognized as the only economic way to reach total depth through ultrahard carbonates. And in recent years, UBD has started to show promise as a way to drill horizontal wells in tight rock and shale plays in the United States. However, the technology has not reached full acceptance. Part of the issue is that the drivers behind UBD’s use have been as heterogeneous as the unconventional formations that companies are using it in. In heavily fractured formations, users say the underbalanced method makes horizontal drilling easier and reduces mud costs by eliminating loss of circulation problems. This also tends to result in less rig time per well as it increases the rate of penetration (ROP). Rather than use UBD in the production section, some unconventional operators only use it for tophole drilling and then go overbalanced before reaching the reservoir. In optimal cases where UBD is used in the pay zone, enough oil can be produced and sold while drilling underbalanced that it can cover the entire cost of the drilling operation and then some. Shell has put its full weight behind the technology after using it to reduce drilling costs and nonproductive time (NPT) during the exploration and appraisal phases of all of its unconventional programs, which span the world from China to Canada, said Keith Smelker, a well engineer at Shell. “The reason why leadership is very bullish on the technology is because we have seen a lot of performance improvements from it,” he said. Those improvements include speeding up exploration drilling in some areas by as many as 15.days. Before starting each of its unconventional programs, Shell engineers evaluate the applicability of going underbalanced and more often than not, they decide to do so. Smelker estimated that the company used UBD on as much as 85% of the unconventional wells it drilled last year.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".