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Record W4253929648 · doi:10.1504/ijogct.2020.110387

Numerical simulation and three-phase pressure transient analysis considering capillary number effect – case study of a gas condensate reservoir

2020· article· en· W4253929648 on OpenAlexaff
Kambiz Davani, Shahin Kord, Omid Mohammadzadeh, Jamshid Moghadasi

Bibliographic record

VenueInternational Journal of Oil Gas and Coal Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Heat Transfer
Canadian institutionsSchlumberger (Canada)
Fundersnot available
KeywordsTransient (computer programming)MechanicsCapillary actionTransient analysisTransient flowCapillary pressureComputer simulationEnvironmental scienceMaterials scienceTransient responseThermodynamicsPhysicsComputer scienceEngineeringMeteorologyPorous medium

Abstract

fetched live from OpenAlex

When the wellbore pressure drops below the dew point pressure of gas in a gas condensate reservoir, there is the possibility of condensate bank build-up and wellbore blockage. These adverse processes result in development of different mobility zones around the wellbore which complicates the pressure transient test analysis. The focus of this study is on the analytical and numerical analysis of pressure testing data obtained from a well in a target gas condensate reservoir. First, the well test data were analytically interpreted through which several well and reservoir parameters were identified such as condensate bank radius, gas effective permeability, mechanical skin and skin due to non-Darcy flow. A three-phase compositional reservoir model was then built using the analytical solution, for numerical analysis of the pressure transient data as well as validation of the analytical results. This numerical model was then used to estimate well deliverability and predict future reservoir performance. [Received: August 17, 2018; Accepted: March 15, 2019]

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 score0.118
Threshold uncertainty score0.401

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 head0.288
Teacher spread0.273 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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