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Record W2157628356 · doi:10.2118/137453-pa

Three-Phase Equilibrium Study for Heavy-Oil/Solvent/Steam System at High Temperatures

2011· article· en· W2157628356 on OpenAlexafffund
Na Jia, Afzal Memon, Jinglin Gao, Julian Y. Zuo, Hongying Zhao, Heng‐Joo Ng, Haibo Huang

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2011
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsAlberta Innovates
FundersTsinghua UniversityAlberta Innovates - Technology Futures
KeywordsSolventVolume (thermodynamics)ChemistryEquation of stateThermodynamicsThermodynamic equilibriumPhase (matter)ViscositySteam-assisted gravity drainageHydrocarbonPetroleum engineeringHydrocarbon mixturesSteam injectionMaterials scienceOil sandsOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Summary Reservoir simulation and research activities of heavy oil production processes (steam-assisted gravity drainage, vapour extraction, or the steam/solvent combined hybrid process) require accurate values of equilibrium constants (K-values) at elevated temperatures. However, such data are rarely available in open literature either because of the temperature limitation of pressure/volume/temperature (PVT) equipment or difficulties in performing high-temperature three-phase vapour/liquid equilibrium (VLE) experiments. In this paper--with the assistance of a state-of-the-art fully visual PVT cell that was specifically developed for heavy viscous oils, is capable of performing fluid-phase-behaviour studies at temperatures up to 250°C, and that effectively mixes samples with viscosity up to 1,000 cp at test temperature--a heavy-oil/solvent/steam equilibrium case study is presented. The heavy oil was mixed with solvent and water at a specified weight ratio, temperature, and pressure in the heavy-oil PVT cell. After achieving equilibrium, the volumes of vapour, liquid, and water phases were measured. The samples from each phase were drawn for compositional analysis using gas chromatography (GC), and K-values of the hydrocarbon components were examined. The component compositions from this experiment were used for equation-of-state (EOS) model tuning. This case study shows that with proper care, the EOS can be characterized and used to match with experimental data for heavy oil.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.017
GPT teacher head0.242
Teacher spread0.225 · 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.

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

Citations17
Published2011
Admission routes2
Has abstractyes

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