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Record W2916652682 · doi:10.2118/1205-0038-jpt

Overview: Production/Facilities (December 2005)

2005· article· en· W2916652682 on OpenAlexaff
Simon Richards

Bibliographic record

VenueJournal of Petroleum Technology · 2005
Typearticle
Languageen
FieldEnergy
TopicOil, Gas, and Environmental Issues
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsDispose patternProduction (economics)SustainabilityBusinessProduced waterPetroleum industryCommodityNatural resource economicsEnvironmental scienceWaste managementEngineeringEnvironmental engineeringEconomicsFinance

Abstract

fetched live from OpenAlex

This year’s selection of papers is a mixed bunch that I thought would appeal to a wide audience. I deliberately chose papers to encourage as many as possible to read them. The broad theme is environment and sustainability. We have a duty to limit gas flaring as much as practicable to limit emissions and conserve fuel. A little-known effect of excessive flaring is addressed in the first paper. It’s a common saying that oil and water don’t mix. Well, the truth is that sometimes they do. The food-processing industry spends a lot of time mixing oils and water that they sell us as food. However, in this industry, our problem is often finding ways to separate oil and water. No two sets of oil and water are the same, and the second paper looks at experiences in a Saudi Arabian field. As our supply of high-quality light sweet crude oil is depleted, we start to produce the heavier sour grades of crude oil around the world. One of the problems with these fields is disposal of the large quantities of elemental sulfur that are recovered during the processing. Until now, it has often been stockpiled with, in my view, huge safety and environmental risks. Our third paper presents some novel means to dispose of this sulfur. Our final paper deals with the end time for a production facility. Whether onshore or offshore, the production and processing facilities must be removed in accordance with local regulations. This task is clearly more difficult offshore, and this paper explains how it was handled offshore California. I hope that you enjoy reading them as much as I enjoyed choosing them. Available from the SPE eLibrary: www.spe.org SPE 93653 “ExxonMobil’s Advanced Gas-to-Liquids Technology—AGC-21,” by R.A. Fiato, ExxonMobil Research and Engineering Co., et al. SPE 93817 “Restoring Integrity to Aged Petroleum Production Facilities,” by S.W. Ciaraldi, BP Indonesia, et al. SPE 96403 “Turning a North Sea Oil Giant Into a Gas Field—Depressurization of the Statfjord Field,” by R. Boge, SPE, Statoil, et al. SPE 96504 “Canopy Bridges Along a Rainforest Pipeline in Ecuador,” by M. Thurber, SPE, Walsh, et al.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score1.000

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)0.0010.000

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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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.

Study designNot applicable
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

Citations2
Published2005
Admission routes1
Has abstractyes

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