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Record W2002846618 · doi:10.1002/cjce.21944

An approach to model three‐phase flow coupling during steam chamber rise

2013· article· en· W2002846618 on OpenAlexaffvenue
Muhammad Murtaza, Zhongxue He, Hassan Dehghanpour

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSteam-assisted gravity drainageMechanicsCoupling (piping)Flow (mathematics)Steam injectionVolumetric flow rateTwo-phase flowDisplacement (psychology)Flow velocityPhase (matter)Petroleum engineeringEngineeringMaterials sciencePhysicsMechanical engineeringOil sands

Abstract

fetched live from OpenAlex

During steam assisted gravity drainage (SAGD) process, a steam chamber forms due to continuous steam injection. This chamber first moves upward to the top of the reservoir and then spreads sideways. The upward chamber displacement is one of the key factors for optimising the steam injection rate. It is necessary to determine the accurate chamber rise velocity for predicting the oil recovery rate. Recent experiments show that oil flow is coupled to water flow during three‐phase gravity drainage in water‐wet systems. In this paper, we argue that this type of flow coupling can be significant in SAGD operations. We extend Butler's [Butler, J. Can. Petrol. Technol . 1987 , 26, 70] and Gotawala and Gates [Gotawala and Gates, Can. J. Chem. Eng . 2008 , 86, 1011] analytic models for the rise of interfering steam chambers to account for three‐phase flow and flow coupling. We also show the importance of flow coupling by solving a simple numerical example. We observe that by including three‐phase drainage and the flow coupling at the steam finger edge, the vertical rise velocity of a steam chamber increases. Moreover, the rise velocity is very sensitive to the coupling term introduced in the new models. Even a small value of the coupling term increases the rise velocity significantly. Furthermore, we compare the model predictions with the values measured in six fields.

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 score0.088
Threshold uncertainty score0.806

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 integrity0.0000.001
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 head0.008
GPT teacher head0.200
Teacher spread0.192 · 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

Citations20
Published2013
Admission routes2
Has abstractyes

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