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Record W2147935227 · doi:10.5539/mas.v4n12p3

Development of an Intelligent System for the Analysis of both Static and Real-time Oil and Gas Well Drilling Data

2010· article· en· W2147935227 on OpenAlexafffundvenue
Mazeda Tahmeen, G. Hareland, B. Rashidi, Nasikul Islam

Bibliographic record

VenueModern Applied Science · 2010
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDrillingComputer scienceDrilling engineeringServerSoftwarePetroleum engineeringWeb serverDrilling fluidReal-time dataSystems engineeringOperating systemEngineeringMechanical engineeringThe Internet

Abstract

fetched live from OpenAlex

The growing demand of digital oilfield in oil and gas industry leads extensive research on the development of engineering software for the analysis of real-time oil and gas drilling data. The Intelligent Drilling Advisory system (IDAs) is an engineering software and being developed to provide a way for the drilling engineers to visualize, collaborate and analyze drilling operational data in real-time in an office environment. WITSML (Wellsite Information Transfer Standard Markup Language) is a web-based oil and gas industry standard used to conduct transfer of drilling data between the developing system (IDAs) and onsite or remote WITSML servers. IDAs uses WITSML SOAP interfaces to retrieve both static and real-time drilling data updating from the server and provide meaningful analysis using different engineering modules for enhanced decision-making to the ongoing drilling operation. The intelligent system is successfully tested with real WITSML servers to verify the functionalities of the engineering modules, as well as, the system itself.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.670
Threshold uncertainty score0.297

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.231
Teacher spread0.214 · 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

Citations6
Published2010
Admission routes3
Has abstractyes

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