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Record W4247550182 · doi:10.2118/2009-005

A Semi-Unsteady State Wellbore Steam/Water Flow Model for Prediction of Sandface Condition in Steam Injection Wells

2009· article· en· W4247550182 on OpenAlexaff
Mehdi Bahonar, J. Azaiez, Zhangxin Chen

Bibliographic record

VenueCanadian International Petroleum Conference · 2009
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWellborePetroleum engineeringSteam injectionFlow (mathematics)Injection wellEnvironmental scienceMechanicsGeology

Abstract

fetched live from OpenAlex

Abstract A numerical nonisothermal two-phase wellbore model is developed to simulate downward flow of a steam and water mixture in the wellbore. This model entails simultaneous solution of coupled mass and momentum conservation equations inside the wellbore with an energy conservation equation for the fluids within the wellbore, surrounding medium, and formation. A new drift-flux model that accounts for slip between the phases inside the wellbore is employed. In addition, a two-dimensional implicit scheme that allows for heat transfer in both the axial and radial directions in the formation is developed. Furthermore, a rigorous nonlinear temperature- and depth-dependent overall heat transfer coefficient is implemented. The model predictions are validated against real field data and other available models. The model is useful for designing well completion and accurately computing the wellbore/formation heat transfer, which is very important for estimating oil recovery by using steam injection. Introduction Modeling of steam injection wells for continuous estimation of pressure, temperature, and different phase velocities and densities as functions of depth and time is crucial for well design, steam injection projects planning and data gathering for continuous reservoir management and real time well monitoring. Once steam is injected in the well, both pressure and temperature of the injected steam and accordingly the densities of water and steam phases will change. These changes are due to the heat exchange between the steam and cold formation surrounding the well, the friction between the steam and inner tubing surface and the change of the hydrostatic pressure with respect to depth,. More importantly, the injected steam quality will drop due to the heat loss from the wellbore system towards the cold formation. The steam quality in the formation can be much worse than that in the wellhead because of an improper wellbore design, no tubular insulation, and/or the deep well location. The multiphase nature of the flow inside the wellbore, the complex heat transfer mechanisms between the wellbore and the surrounding medium, and the unsteady state nature of the flow and transport processes make the entire system intricately coupled and extremely difficult to solve. Numerous investigators have worked on the modeling of both injection and production wells. One of the first papers goes back to Ramey(1) in 1962, which has been referred to by many subsequent works modeling the wellbore heat loss and pressure drop. In that paper, the author simplified the heat balance equation to solve it analytically. The steady-state flow of incompressible single phase, with fixed fluid and formation properties with respect to depth and temperature, was analyzed. A simple procedure was presented to couple the steady-state heat loss of the wellbore fluid with a transient heat flow in the formation via an overall heat transfer coefficient. Moreover, it was assumed that the overall heat transfer coefficient was independent of depth, and the frictional loss and kinetic energy effect were neglected. In 1965, Satter(2) improved Ramey's analytical model by considering a depth-dependent overall heat transfer coefficient and phase- and temperature-dependent fluid properties.

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 score0.098
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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