Bibliographic record
Abstract
Abstract Remote Arctic onshore exploration can be very costly, frequently exceeding the cost of a deepwater Gulf of Mexico well. This paper reviews the reasons for these high costs and a possible combination of new proven technologies and rig designs to significantly reduce these costs Logistics, mobilization, demobilization and a limited drilling season are factors that combine to cause high costs. Operation time requirements and the short drilling season normally results in a rig drilling one well per season. A significant reduction in exploration final hole size is the primary driver in reducing costs as this leads to a major reduction in rig size. Downsizing does not limit well evaluation due to recent developments in downsizing evaluation equipment. The majority of the required information can be obtained with this finder well or "scratch and sniff' approach. This downsizing allows the use of an innovative rig design; hybrid coil tubing drilling unit; that has significantly reduced mobilization and demobilization times. The reduction in drilling and mobilization/demobilization time can result in one rig drilling multiple wells in the drilling season. Combining new technologies, such as casing drilling and coil tubing drilling, reduces drilling time and allows the hybrid coil tubing rig to drill deeper. Casing drilling and coil tubing drilling are areas where ConocoPhillips is an industry leader. A significant reduction in exploration cost is predicted, estimated at 50%.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".