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Record W2075808267 · doi:10.2118/172345-pa

A Pseudobubblepoint Model and Its Simulation for Foamy Oil in Porous Media

2014· article· en· W2075808267 on OpenAlexaff
Zhangxin Chen, Jian Sun, Ruihe Wang, Xiaodong Wu

Bibliographic record

VenueSPE Journal · 2014
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPetroleum engineeringPorous mediumCompressibilityOil in placeOil productionEnhanced oil recoveryFossil fuelGas oil ratioOil fieldVolume (thermodynamics)Environmental sciencePorosityPetroleumThermodynamicsChemistryGeologyEngineeringGeotechnical engineeringWaste managementPhysics

Abstract

fetched live from OpenAlex

Summary This is the second paper of a series in which we study heavy oil in porous media. The first paper dealt with an experimental study (Wang et al. 2008), whereas a mathematical and simulation study is presented here. The research program stems from the need to predict the field performance of a class of heavy-foamy-oil reservoirs. These reservoirs show a better-than-expected primary performance: lower production gas/oil ratios (GORs), higher-than-expected production rates, and higher oil recovery. A mechanism used to account for the observed performance is that the liberated solution gas is entrained in the oil when the reservoir pressure falls below the thermodynamic equilibrium bubblepoint pressure. The presence of entrained gas increases the effective compressibility of the oil phase and prevents gas from becoming a free phase. Hence, the foamy oil behaves as if it had a pseudobubblepoint pressure below the usual equilibrium bubblepoint pressure. This paper describes a pseudobubblepoint model and a methodology that can be used to compute foamy-oil fluid properties from conventional laboratory pressure/volume/temperature (PVT) data. The techniques developed are then used to study foamy oil in the Orinoco belt, Venezuela. The present mathematical model is validated by comparing numerical and experimental results.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.330

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 head0.015
GPT teacher head0.251
Teacher spread0.235 · 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 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

Citations42
Published2014
Admission routes1
Has abstractyes

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