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Record W4409283993 · doi:10.1021/acsomega.4c06638

Experimental Study on Oil Recovery from Oil Sands by dimethyl ether via the Displacement–Dissolution–Permeation Method

2025· article· en· W4409283993 on OpenAlexaboutno aff
Fan Yang, Y.‐Z. Zhu, Xinyi Wang, Jipeng Sun, Xin Wang, Yongjin Qian, Lu Wang, Wei Zhu

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsDissolutionPermeationDimethyl etherDisplacement (psychology)ChromatographyChemistryEtherPetroleum engineeringChemical engineeringOrganic chemistryGeologyEngineeringMembranePsychologyBiochemistry

Abstract

fetched live from OpenAlex

In response to high steam usage, significant greenhouse gas emissions, and secondary pollution risks associated with solvents in steam-assisted gravity drainage (SAGD) and solvent-assisted SAGD techniques for oil sands extraction, a new technology using liquid dimethyl ether (DME) for displacement-dissolution-permeation extraction (DME-DDP) has been proposed. Using a self-designed DME-DDP experimental apparatus, oil extraction experiments were conducted on consolidated oil sands samples to simulate the oil sand formations in the Athabasca region in Canada. The experimental results indicated that oil washing efficiency reached 84% under the optimal pressure gradient, with recoveries of >90% for saturates and aromatics, >60% for resins, and >50% for asphaltenes. The DME-DDP oil extraction process includes two stages, displacement and dissolution-permeation, each contributing to approximately 50% of the total oil recovery. The pressure gradient is the primary factor influencing oil washing efficiency, with higher recovery necessitating slower permeation or extended contact time between DME and crude oil in the formation. DME is a colorless, nontoxic, and nongreenhouse gas that can be recycled. These characteristics underscore the environmental sustainability and cost-effectiveness of the proposed method.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.846

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 head0.009
GPT teacher head0.288
Teacher spread0.279 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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