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Record W2326145329 · doi:10.1021/ie404424p

Experimental and Numerical Modeling Study of Gravity Drainage Considering Asphaltene Deposition

2014· article· en· W2326145329 on OpenAlexafffund
Rohaldin Miri, Sohrab Zendehboudi, Shahin Kord, Francisco M. Vargas, Ali Lohi, Ali Elkamel, Ioannis Chatzis

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of WaterlooToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsAsphaltenePetroleum engineeringHydrocarbonDeposition (geology)Saturation (graph theory)MethanePermeability (electromagnetism)DrainageChemistryGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

This paper presents an experimental investigation of gas-assisted gravity drainage (GAGD) performance while taking into account the effects of asphaltene deposition. The experiments were conducted at high pressures and high temperature (15–37 MPa and 102 °C) using carbonate cores and real reservoir fluids in the absence of water saturation. An 85% methane-enriched hydrocarbon gas mixture was employed as an injection fluid in the laboratory runs. The final recovery factor for a fairly tall core was 52% of the original oil at immiscible conditions during hydrocarbon gas injection when the operating pressure and the amount of injected gas were 28.3 MPa and 1.2 pore volume, respectively. In all of the experiments, the total amount of asphaltene deposition was less than 2 wt % of the original oil and no considerable reduction in permeability was found. A numerical two-phase (gas–oil) simulator coupled with a deposition model was also developed to evaluate the importance of different parameters contributing to the final recovery. There was a good agreement between the modeling and experimental results, showing an average error percentage lower than 5%. This study can aid the prediction of the performance of gas injection processes experiencing asphaltene deposition and also aid in proper design of gravity drainage-assisted enhanced oil recovery methods.

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)0.001
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 score0.225
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.062
GPT teacher head0.325
Teacher spread0.263 · 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

Citations24
Published2014
Admission routes2
Has abstractyes

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