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Record W4318715186 · doi:10.2118/1222-0024-jpt

A Study Suggests Geosteers Often Are Missing the Target

2022· article· en· W4318715186 on OpenAlexaboutno aff
Stephen Rassenfoss

Bibliographic record

VenueJournal of Petroleum Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsCONTESTCompetitor analysisDrillingPetroleum engineeringOperations researchOperations managementGeologyEngineeringBusinessMarketingPolitical scienceMechanical engineering

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.198
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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