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Record W2327056449 · doi:10.1021/je101034s

Analysis of Downhole Asphaltene Gradients in Oil Reservoirs with a New Bimodal Asphaltene Distribution Function

2011· article· en· W2327056449 on OpenAlexaff
Julian Y. Zuo, Oliver C. Mullins, Denise E. Freed, Dan Zhang, Chengli Dong, Huang Zeng

Bibliographic record

VenueJournal of Chemical & Engineering Data · 2011
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsAsphalteneSolubilityMolar massColloidChemistryThermodynamicsMaterials scienceChromatographyMineralogyOrganic chemistry

Abstract

fetched live from OpenAlex

Downhole fluid analysis (DFA) has been successfully used to describe reservoir connectivity and fluid properties. DFA not only measures bulk fluid properties such as the gas−oil ratio (GOR), density, and light-end compositions of CO 2, CH 4, C 2 H 6, C 3 H 8 −C 5 H 12 fraction, and hexane and heavier (hexane +) fractions but also color (optical density) that is related to the heavy ends (asphaltenes and resins) in real time at downhole conditions. Therefore, color gradient analysis in oil columns becomes essential to determine reservoir complexities. In this paper, a new bimodal Γ-distribution function (asphaltene molecules + nanoaggregates and clusters) was proposed to characterize asphaltene components. A thermodynamic asphaltene-grading model was also developed to describe equilibrium distributions of heavy ends (heavy resins and asphaltenes) in oil columns using the multicomponent Flory−Huggins regular solution model coupled with a gravitational contribution. The variations of oil properties (such as molar volume, molar mass, solubility parameter, and density) with depth were calculated by the equation of state (EOS). The primary factors governing asphaltene distribution in reservoirs are the gravitational term, which is determined in part by the size of the asphaltene colloidal particle, and the solubility term, which is determined in large part by the GOR (composition). Consequently, it is critical to accurately measure both the fluid coloration and the GOR (composition) to understand the asphaltene gradient in oil columns. The results obtained in this work are in accordance with the Yen−Mullins model in asphaltene science. In particular, if asphaltenes are equilibrated in an oil reservoir, then a massive fluid flow through the reservoir had to have taken place, and permeable rocks are required, thereby implying connectivity, because asphaltenes necessarily enter the reservoir out of their ultimate equilibrium at the beginning of the reservoir charging. Therefore, a new powerful approach is established for conducting DFA color and GOR gradient analysis by coupling advanced asphaltene science with DFA technology (profiling of fluids) to address reservoir connectivity.

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 score0.242
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.231
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

Citations16
Published2011
Admission routes1
Has abstractyes

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