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Solvent Content Measurements of the Produced Stable Water-in-Oil Emulsions During Solvent-Aided Thermal Recovery Processes

2023· article· en· W4383894149 on OpenAlexafffund
Shadi Kheirollahi, Mohammad S. Khan, Bushra Kamal, Mabkhot Bin Dahbag, Hadi Bagherzadeh, Sayyedvahid Bamzad, Devjyoti Nath, Hassan Hassanzadeh

Bibliographic record

VenueEnergy & Fuels · 2023
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaImperial Oil LimitedCanadian Natural Resources LimitedCenovus EnergyUniversity of CalgaryKuwait Oil CompanySuncor Energy IncorporatedConocoPhillips
KeywordsSolventEmulsionChemistryChromatographyProcess engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

The in situ solvent-aided thermal recovery processes are promising methods for recovering unconventional oil resources due to their higher efficiency, lower energy and water consumption, and reduced environmental impacts. The produced stream is often a highly stable water-in-oil emulsion, where the oil phase comprises bitumen and solvent. Separating water from such a sample is challenging because conventional approaches result in solvent loss and sample contamination and render the solvent content measurement techniques currently employed by the industry invalid. Developing analytical techniques for solvent detection without sample dehydration, solvent loss, and contamination is essential for production surveillance, monitoring, process optimization, and economic evaluation. Solvent recovery and concentration measurement in produced streams have been considered the most important issues associated with the success and commercialization of solvent-assisted recovery processes. In this work, we implement a comprehensive chromatographic technique to measure the solvent concentration of the actual bitumen/solvent/water emulsions produced during a large 3D physical model experiment of the solvent-aided recovery process. We used combined gel permeation chromatography (GPC) and gas chromatography (GC) to obtain the full characterization of bitumen/solvent/water systems. After characterizing four produced emulsion samples, actual and synthetic multicomponent solvents are used to establish the necessary calibrations for rapid and accurate determination of the organic solvent content in the produced emulsion samples. The results demonstrated that the automated GC/GPC can be applied to actual minute amount emulsion samples for fast detection of solvent content in the pilot and field-scale projects of solvent-aided thermal recovery processes without sample dehydration while solvent loss and sample contamination are entirely avoided.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.041
GPT teacher head0.236
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations11
Published2023
Admission routes2
Has abstractyes

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