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Effect of water presence on asphaltene-precipitation behavior of CO2 and crude oil mixtures during CCUS processes

2024· article· en· W4404171397 on OpenAlexafffund
Zhuo Chen, Qi Wang, Jing Zhang, Sirui Li, Ying Zhou, Ruixue Li, Liangliang Jiang, Huazhou Li

Bibliographic record

VenueApplied Thermal Engineering · 2024
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsAsphaltenePetroleum engineeringCrude oilMaterials sciencePrecipitationEnvironmental scienceWaste managementChromatographyChemical engineeringProcess engineeringEngineeringChemistryMeteorology

Abstract

fetched live from OpenAlex

• First study to draw PT and Px phase diagrams involving VLAS four-phase equilibria. • Reveal the effect of water on asphaltene precipitation during CO 2 injection. • Water poses a minor effect on asphaltene precipitation behaviour. Water can extensively exist in reservoirs. Since the solubility of CO 2 in water is not negligible under reservoir conditions, the aqueous phase should also be considered during the phase-behavior simulations of the CO 2 flooding process. With the presence of an asphaltene phase and an aqueous phase, four phases (i.e., a vapor phase, a hydrocarbon phase, an asphaltene phase, and an aqueous phase) can coexist at a given thermodynamic equilibrium. In this study, a robust and efficient four-phase vapor–liquid-liquid-aqueous equilibrium calculation algorithm is applied to conduct four-phase vapor–liquid-asphaltene-aqueous equilibrium calculations by assuming asphaltene as a dense liquid phase. The algorithm is first validated by comparing the calculated asphaltene precipitation data with the presence of water against the experimental asphaltene precipitation data documented in the literature. The validation shows that the calculation results agree well with the experimental data, indicating that such algorithm can make reliable predictions of asphaltene precipitation with the presence of water. The validated algorithm is subsequently used to construct pressure–temperature ( PT ) and pressure-composition ( Px ) phase diagrams to study the effect of water on asphaltene precipitation under different pressure/temperature conditions and under the injection of CO 2 . We can conclude from the calculated results that although adding water to the reservoir fluid can cause obvious changes in the PT phase diagrams, it does not have a noticeable influence on the asphaltene precipitation onsets. However, it can be seen from the calculated Px phase diagrams that the presence of water during the CO 2 flooding process can make asphaltene precipitated more easily. The maximum amount of asphaltene precipitation is decreased by the presence of water due to the fact that a considerable amount of CO 2 is dissolved in the aqueous phase instead of the liquid hydrocarbon phase.

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 score0.030
Threshold uncertainty score0.424

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 head0.003
GPT teacher head0.210
Teacher spread0.207 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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