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Record W2094823664 · doi:10.2118/151034-ms

Performance Evaluation of a New Transient Two-Phase Flow Model

2012· article· en· W2094823664 on OpenAlexaff
Jeff Li, Mateus Teixeira, Paul H. Salim, Yiran Fan

Bibliographic record

VenueIADC/SPE Drilling Conference and Exhibition · 2012
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsConocoPhillips (Canada)
FundersConocoPhillips
KeywordsTransient (computer programming)Underbalanced drillingWorkoverMechanicsFlow (mathematics)Drilling fluidTwo-phase flowPetroleum engineeringPipeline (software)SimulationDrillingEngineeringMechanical engineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

Abstract Transient gas-liquid flow is a common phenomenon in the drilling, workover and gas/oil production processes. Any change in the operating conditions at the inlet or outlet will introduce a transient response. Operations such as liquid unloading, under balanced drilling with gasified fluid, well control, cementing, hole cleaning, pipeline startup and blowout may never reach a steady state. In order to simulate the flow system, several transient two-phase flow simulators have been developed in the past. However, these models are based on the two-fluid model approach. They are complicated and time consuming to run since they treat the gas and liquid phase separately in terms of pressure, temperature and velocity. In this paper a new transient two-phase flow model has been developed. In each time step, the two-phase flow regime, liquid holdup and pressure gradient are estimated with the empirical correlations which are well developed for the steadystate flow. A drift-flux equation was introduced to close the system. The model was validated against data collected from the public literature, field operations, and other transient software. Several field cases are used to illustrate the transient nature of pipeline production, underbalanced drilling (UBD), sand cleanout, and liquid unloading. The benefits of using the transient simulation for the operational design, training and job execution are also discussed.

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 score0.196
Threshold uncertainty score0.486

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 head0.045
GPT teacher head0.278
Teacher spread0.233 · 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

Citations8
Published2012
Admission routes1
Has abstractyes

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