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Record W2900928341 · doi:10.3384/ecp18153162

Web Enabled High Fidelity Drilling Computer Model with User-Friendly Interface for Education, Research and Innovation

2018· article· en· W2900928341 on OpenAlex
Robert H. Ewald, Jan Einar Gravdal, Dan Sui, Roman Shor

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLinköping electronic conference proceedings · 2018
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
FundersNorges ForskningsrådUniversitetet i BergenUniversitetet i StavangerNorges Teknisk-Naturvitenskapelige UniversitetConocoPhillips
KeywordsComputer scienceWeb applicationFidelityUser interfaceInterface (matter)World Wide WebHuman–computer interactionUser FriendlyDrillingMultimediaOperating systemEngineering

Abstract

fetched live from OpenAlex

Next generation intelligent software for drilling control systems together with automated monitoring and analysis systems is expected to save costs for the drilling industry. However, the transition from monitoring a process, which today is controlled manually, to automating the process requires a step-change in education of personnel as well as in infrastructure for development and testing new technology. The lack of high quality field data from drilling and well operations is a major problem in research and innovation projects within the oil & gas and geothermal drilling sector, as well as in education within these areas. Since 2015, IRIS and the University of Stavanger have developed a web enabled high fidelity drilling simulator as part of the OpenLab Drilling project 1 .

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 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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.330
Teacher spread0.295 · 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