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Record W2084683726 · doi:10.2118/83978-ms

Promoting Real-Time Optimization of Hydrocarbon Producing Systems

2003· article· en· W2084683726 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsConocoPhillips (Canada)
Fundersnot available
KeywordsSloganDeliverableProcess (computing)Petroleum industryComputer sciencePlan (archaeology)Field (mathematics)Fossil fuelRisk analysis (engineering)TimelineOperations researchEngineeringSystems engineeringBusinessWaste management

Abstract

fetched live from OpenAlex

Abstract The term "Real-Time Optimization" (RTO) has rapidly found its way into common usage in the oil and gas industry, as it already has in many others. However, RTO in the oil and gas industry is usually used more as a slogan rather than describing a system or process that truly optimizes anything at all, let alone does so in real-time. In this paper, we describe what RTO means in the exploitation of hydrocarbons and what technologies are available now and are likely to be available in the future. We discuss how it is misunderstood and what real financial benefits await those who adopt it. Furthermore, we are working toward developing a method of classification to allow us to establish where a field operation lies on the RTO ladder, and to help plan a strategy to generate the benefits that moving up the RTO ladder can offer on specific fields and assets. The paper also describes a new SPE Technical Interest Group (TIG), explaining why it has been formed, and outlining its objectives and some planned deliverables.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.445
Threshold uncertainty score0.357

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 science0.0000.000
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 head0.011
GPT teacher head0.234
Teacher spread0.222 · 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