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Record W2971956063 · doi:10.1021/acs.jpcc.9b04247

Factors Governing the Enhancement of Hydrocarbon Recovery via H<sub>2</sub>S and/or CO<sub>2</sub> Injection: Insights from a Molecular Dynamics Study in Dry Nanopores

2019· article· en· W2971956063 on OpenAlexaboutno aff
Sakiru B. Badmos, Tai Bui, Alberto Striolo, David R. Cole

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2019
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
FundersH2020 EnergyBasic Energy SciencesPetroleum Technology Development FundLeverhulme Trust
KeywordsEnhanced oil recoveryNanoporeButaneHydrocarbonMolecular dynamicsChemical engineeringInertMuscoviteOxideChemistryChemical physicsMoleculeAcid gasPyrophylliteNatural gasMaterials scienceNanotechnologyMineralogyOrganic chemistryInorganic chemistryComputational chemistryComposite materialCatalysis

Abstract

fetched live from OpenAlex

Abstract Although enhanced oil recovery (EOR) is often achieved by CO2 injection, the use of acid gases has also been attempted, for example, in oil fields in west Canada. To design EOR technologies effectively, it would be beneficial to quantify the molecular mechanisms responsible for enhanced recovery under various conditions. We report here the molecular dynamics simulation results that probe the potential of recovering n-butane confined in silica, muscovite, and magnesium oxide nanopores, all proxies for subsurface materials. The three model solid substrates allow us to identify different molecular mechanisms that control confined fluid behavior and to identify the conditions at which different acid gas formulations are promising. The acid gases considered are CO2, H2S, and their mixtures. For comparison, in some cases, we consider the presence of inert gases such as N2. In all cases, the nanopores are dry. The recovery is quantified in terms of the amount of n-butane displaced from the pore surface as a function of the amount of gases present in the pores. The results show that the gas performance depends on the chemistry of the confining substrate. Whereas CO2 is more effective at displacing n-butane from the protonated silica pore surface, H2S is more effective in muscovite, and both gases show similar performance in MgO. Analysis of the interaction energies between the confined fluid molecules and the surface demonstrates that the performance depends on the gas interaction with the surface, which suggests experimental approaches that could be used to formulate the gas mixtures for EOR applications. The structure of the gas films in contact with the solid substrates is also quantified as well as the self-diffusion coefficient of the fluid species in confinement. The results could contribute to designing strategies for achieving both improved hydrocarbon production and acid gas sequestration.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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)0.0010.000

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 head0.007
GPT teacher head0.210
Teacher spread0.203 · 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 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

Citations21
Published2019
Admission routes1
Has abstractyes

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