MétaCan
Menu
Back to cohort
Record W998963185

De-Oiling of Produced Water From Offshore Oil Platforms Using a Recent Commercialized Technology Which Combines Adsorption, Coalescence And Gravity Separation

2006· article· en· W998963185 on OpenAlexaff
M.J. Plebon, Marc A. Saad, Xue Jun Chen, Serge Fraser

Bibliographic record

VenueThe Sixteenth International Offshore and Polar Engineering Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsTornado Spectral Systems (Canada)
Fundersnot available
KeywordsProduced waterEnvironmental scienceSubmarine pipelineAPI gravityPetroleum engineeringSteam-assisted gravity drainageBarrel (horology)PetroleumPetroleum industryCoalescence (physics)SeawaterWaste managementCrude oilEnvironmental engineeringGeologyOil sandsEngineeringMaterials scienceOceanography
DOInot available

Abstract

fetched live from OpenAlex

In upstream oil and gas operations, saline water is co-produced with the crude oil. On a global spectrum, it is estimated that 3 barrels of water is produced for every barrel of crude oil. As the asset matures, the ratio of water produced vs. crude oil begins to increase. In North America, the ratio is approaching 10:1. Treatment and disposal of produced water is becoming a leading economic factor in the viability assessment of the asset. This is especially so with offshore platforms where produced water must meet and exceed environmental regulations. The Company has developed, validated and commercialized a technology to remove and recover dispersed crude oil in water 2 microns and larger. The technology is a combination of filtration, coalescence and gravity separation. Solutions for several challenging aspects of produced water properties have been developed and tested, through both field trials (onshore and offshore) and laboratory experimental simulations. Results obtained have been measured with an advanced video imaging particle size-distribution apparatus that measures samples on line and in real time. The results show that the technology has been successful in polishing produced water to oil-inwater concentrations of less than 10 mg/L without the need for chemicals or additional heat. The theory behind the technology will be explained and offshore field results will be presented to support the technology’s claims.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.653

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 head0.019
GPT teacher head0.270
Teacher spread0.251 · 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

Citations7
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueThe Sixteenth International Offshore and Polar Engineering ConferenceSame topicReservoir Engineering and Simulation MethodsFrench-language works237,207