MétaCan
Menu
Back to cohort
Record W4394794416 · doi:10.1016/j.petsci.2024.04.004

Molecular insights into oil detachment from hydrophobic quartz surfaces in clay-hosted nanopores during steam–surfactant co-injection

2024· article· en· W4394794416 on OpenAlexaff
Benjieming Liu, Xuantong Lei, Mohammad Ali Ahmadi, Zhangxin Chen

Bibliographic record

VenuePetroleum Science · 2024
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsKaoliniteIlliteMontmorilloniteChemical engineeringWettingQuartzClay mineralsPulmonary surfactantOil sandsAsphaltMaterials scienceAsphalteneSiloxaneEnhanced oil recoveryGibbsiteMineralogyChemistryComposite materialPolymer

Abstract

fetched live from OpenAlex

Thermal recovery techniques for producing oil sands have substantial environmental impacts. Surfactants can efficiently improve thermal bitumen recovery and reduce the required amount of steam. Such a technique requires solid knowledge about the interaction mechanism between surfactants, bitumen, water, and rock at the nanoscale level. In particular, oil sands ores have extremely complex mineralogy as they contain many clay minerals (montmorillonite, illite, kaolinite). In this study, molecular dynamics simulation is carried out to elucidate the unclear mechanisms of clay minerals contributing to the bitumen recovery under a steam-anionic surfactant co-injection process. We found that the clay content significantly influenced an oil detachment process from hydrophobic quartz surfaces. Results reveal that the presence of montmorillonite, illite, and the siloxane surface of kaolinite in nanopores can enhance the oil detachment process from the hydrophobic surfaces because surfactant molecules have a stronger tendency to interact with bitumen and quartz. Conversely, the gibbsite surfaces of kaolinite curb the oil detachment process. Through interaction energy analysis, the siloxane surfaces of kaolinite result in the most straightforward oil detachment process. In addition, we found that the clay type presented in nanopores affected the wettability of the quartz surfaces. The quartz surfaces associated with the gibbsite surfaces of kaolinite show the strongest hydrophilicity. By comparing previous experimental findings with the results of molecular dynamics (MD) simulations, we observed consistent wetting characteristics. This alignment serves to validate the reliability of the simulation outcomes. The outcome of this paper makes up for the lack of knowledge of a surfactant-assisted bitumen recovery process and provides insights for further in-situ bitumen production engineering designs.

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)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.004
GPT teacher head0.233
Teacher spread0.229 · 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

Citations15
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuePetroleum ScienceSame topicEnhanced Oil Recovery TechniquesFrench-language works237,207