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Record W2016335717 · doi:10.2118/04-02-04

Analysis of Sand Production in Unconsolidated Oil Sand Using a Coupled Erosional-Stress-Deformation Model

2004· article· en· W2016335717 on OpenAlexaff
Richard Wan, J. Wang

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeotechnical engineeringMatrix (chemical analysis)GeologyErosionStress (linguistics)PorosityStress fieldOil fieldDeformation (meteorology)Oil productionPetroleum engineeringFlow (mathematics)Coupling (piping)Finite element methodMechanicsMaterials scienceEngineeringGeomorphologyComposite material

Abstract

fetched live from OpenAlex

Abstract The paper presents a sand production model for a deforming oil sand matrix. The deformability of the oil sand matrix is an important issue given that in situ and well pumping induced stresses impact on the susceptibility of the oil sand to produce sand. The sand production model is formulated in a consistent manner within the framework of mixture theory with porosity as one of the main field state variables. The latter is split into two parts: one related to volume changes as a result of erosion in the oil sand matrix, and the other one due to deformations in the matrix subjected to a stress field. The coupling of the erosion model to a stress model is made through bulk volumetric strains. Also, the erosion constitutive law is intimately tied with material strengths that enter the stress model. Finally, some numerical examples of sand production restricted to a rigid matrix are given to illustrate the theory, pending the completion of the finite element implementation of a coupled stress-erosional model. Introduction Sand production during hydrocarbon production in oil wells is both a costly and prevalent phenomenon that is not well understood. Consequently, sand production and control has been a research topic for more than five decades. From a mechanistic viewpoint, sand production emanates from the progressive disaggregation of the poorly consolidated formation due to many factors: notably, stresses, fluid flow, thermal, solution gas drive, and reservoir heterogeneity in porosity. Once initiated, sand production can be progressive and may strike at varying degrees of severity, ranging from erosion and plugging of pumps, valves, and pipes to the development of large cavities or wormhole-like structures in the formation resulting in damage and casing collapse. A review of sand production issues, together with the development of a model describing the erosion of sand grains in a rigid oil sand matrix, were presented in Wan and Wang(1). In this paper, the model is further developed with the constraint of the rigid matrix removed so that both stress and strength characteristics enter formally into the formulation. It is well recognized that in situ stresses, as well as those induced during drilling, have an impact on sand production. In this paper, a framework that considers the rigorous coupling between stress/deformations and erosion is proposed within the continuum theory of mixtures(2). Such rigorous coupling is scarce in the current literature, except for the work of Stavropoulou et al.(3) in which the coupling between skeleton deformations and fluid transport was formulated, but was not done in a consistent manner Model Description Figure 1 illustrates the mechanics of sand production around a wellbore with sand grains being dislodged from the oil sand matrix. From a mechanistic point of view, sand production emerges as a result of an instability occurring in a viscous fluid saturated porous medium that undergoes mechanical deformation in the presence of fluid fluxes. This condition can be described within a three-phase system in which solid (s), fluid (f), and fluidized fluid (ff) interact.

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 score0.079
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0100.003
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 head0.008
GPT teacher head0.212
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

Citations33
Published2004
Admission routes1
Has abstractyes

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