Bibliographic record
Abstract
Horizontal drilling makes it possible to drill through the most-productive rock, but based on the following charts, oil companies may often be missing that opportunity. The heavy black bars show the most-productive rock in two producing wells—one unconventional onshore and, the other, offshore and conventional. The tangle of thin, colored lines on and around it are the well paths chosen by 329 competitors in a geosteering contest showing that many of them got lost on the way. The charts are from a paper that analyzed 10,000 geosteering decisions made during the 2021 Rogii Geosteering World Cup, an annual competition started by the maker of geosteering software. The work by Rogii and a team of Norwegian researchers studying drilling decision making was presented at this year’s Unconventional Resources Technical Conference (URTeC 3722510). The results showed about half the contestants earned scores of 30% or less based on the percentage of the well in the target zone—weighted 75%—and their rate of penetration—25%. Only 14 of the 329 participants scored 60% or more, with the balance somewhere in between. To some extent, this dim view of geosteering is a product of the contest rules. Competitors were asked to geosteer two difficult wells at breakneck speed—a decision every 2 minutes based on data from the last 100 m drilled—with minimal information upfront about the wells chosen because they were difficult to steer. In the contest, there was no professional downside for poor performance. Still, it is the rare public attempt to measure how well drillers do at adjusting well plans to maintain contact with the most-productive rock. While the nature of the competition suggests it is not trying to replicate actual drilling conditions, it does raise questions about how well oil companies are doing at maximizing reservoir contact. They observed that some top performers in one well did poorly on the next, and luck played a significant role in some of the results. “The inconsistent performances of players in the two rounds/poor average scores/and significantly different well trajectories planned by geosteerers represent a lack of unified guidelines for stratigraphic-based steering,” Yasaman Cheraghi, a PhD fellow in computational engineering at the University of Stavanger, wrote in an email. Geography was not a good predictor of performance. While 40% of the participants were working in North America, where the greatest number of horizontal wells have been drilled, their results were not better despite their experience. Those involved in the geosteering simulations, based on wells in the Duverney shale in Western Canada and the Xihu Sub-Basin (Yuquan discovery) of the East China Sea, would say they were using a standard method—stratigraphic-based steering. That approach uses the data gathered while drilling and other data about the formation to predict how the layers of rock in the reservoir—the stratigraphy—are likely to change ahead of the steering.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".