How to Accelerate Drilling Learning Curves
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Given the current economic climate and continued volatility in oil and natural gas prices, extracting maximum benefit from drilling performance optimization is key to the economical exploitation of many resource plays around the world. Shell Upstream Americas is employing a highly effective performance improvement team to help drive performance optimization and the delivery of top quartile performance on its wells in North America and beyond. Central to this group is a dedicated team of optimization engineers that deliver drilling efficiency optimization techniques, the innovative use of real-time optimization centers, and value-adding root-cause failure investigations to various land and offshore well delivery teams in a fit-for-purpose way. Using the optimization approaches taken, it has been possible to help accelerate well delivery times and associated learning curves by as much as a factor of three, often in a minimum amount of time. A main conclusion is therefore that this approach is a highly effective way to bring performance optimization focus to field operations. This paper highlights the modus operandi of these optimization engineers, the techniques and tools they employ, and the remarkable results achieved. The workflow and organizational structure was applied to well delivery optimization with projects ranging from shale gas drilling in the Continental US and Canada as well as hard rock drilling in the Middle East.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it