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Record W2117161829 · doi:10.2118/04-12-01

Modelling Sand Production and Erosion Growth Under Combined Axial and Radial Flow

2004· article· en· W2117161829 on OpenAlexafffund
Richard Wan, J. Wang

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Calgary
FundersKU LeuvenUniversity of OttawaUniversity of AlbertaU.S. Department of EnergyUniversity of CalgaryÉcole Nationale des Travaux Publics de l'État
KeywordsDiscrete element methodPorosityGeotechnical engineeringMechanicsFlow (mathematics)GeologyErosionParticle (ecology)FluidizationCylinderPorous mediumFluid dynamicsStress (linguistics)Matrix (chemical analysis)Materials scienceEngineeringPhysicsFluidized bedMechanical engineeringComposite materialGeomorphology

Abstract

fetched live from OpenAlex

Abstract The paper investigates the phenomenon of sand production in both axial and radial flow conditions using a coupled particle erosion-fluid transport-stress model that has been developed recently by the authors. The motivation for such a study stems from the fact that perforations in a slotted oil well can modify both the local fluid flow and stress regimes, and hence influence sand production. Based on available lab test information, the authors examine sand production in a hollow cylinder test in which sand is being produced under combined axial and radial flow of oil in a sandstone specimen. Aspects such as sand flux and porosity evolutions, as well as erosion growth around the inner wall of the cylinder, are investigated using the numerical model in view of understanding the physics of sand production. Introduction Sand is produced in a porous granular material whenever sand particles are being dislodged from the matrix as a result of very high fluid pressure gradients, thereby leaving behind mechanically damaged zones. From a broader perspective, this phenomenon can be viewed as a particle fluidization with erosion process by which a sand matrix is disaggregated due to a combination of stress changes and fluid flow when fluid is aggressively pumped from a porous medium. By virtue of the complexity of the physics of the problem, several challenges are encountered in any numerical modelling endeavour. Various researchers, for instance Jensen et al.(1), have attempted to model the above mentioned physical phenomenon using numerical techniques based on the discrete element method. Alternatively, a continuum mechanics approach can also be adopted in which mass balance is applied to a three-phase system comprised of solid, fluid, and fluidized solid, as was first proposed by Vardoulakis et al.(2). This approach was subsequently extended by Wan and Wang(3, 4) to include the deformation of the solid matrix and address general initial boundary value problems. As such, a standard finite element technique combined with Newton-Raphson method was used with some success for the solution of resulting non-linear equations which involve fluidized solid concentration, fluid pressure, and porosity as primary variables. It was found that numerical results were corrupted with instabilities in the form of node-tonode oscillations or wiggles whenever the solved field variables suffered tremendous distortions with high gradients during sand production. In view of addressing the above mentioned numerical difficulty, Wan and Wang(5) have recently introduced new numerical techniques which are akin to stabilization methods, known as Streamline Upwind/Petrov-Galerkin (SUPG) and Galerkin Least Squares (GLS) formulations [see Brooks and Hughes(6) and Hughes et al.(7)]. In Wan and Wang(5), local field variables found in the governing equations, such as density, flux, and stress, are expanded into a Taylor series for a finite size domain. An optimized local mean technique was introduced based on concepts of Finite Increment Calculus(8) and second gradient theories(9). As such, the original form of the governing equations describing the physics of the problem is preserved, while additional terms leading to numerical stabilization naturally emerge during the numerical process.

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 score0.236
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
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 head0.006
GPT teacher head0.169
Teacher spread0.162 · 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
Published2004
Admission routes2
Has abstractyes

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