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Record W2097002531 · doi:10.2118/06-04-01

Prediction of Volumetric Sand Production Using a Coupled Geomechanics-Hydrodynamic Erosion Model

2006· article· en· W2097002531 on OpenAlexaff
Richard Wan, Y. Liu, J. Wang

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2006
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGeomechanicsGeotechnical engineeringGeologyFlow (mathematics)DragPetroleum engineeringErosionPorosityMechanics

Abstract

fetched live from OpenAlex

Abstract The production of sand grains from an unconsolidated porous solid matrix under viscous fluid flow is inevitable. While sand production has been found to effectively increase well productivity in both heavy oil and conventional light oil reservoirs, it can also lead to geomechanical problems such as sand failure and cavity (wormhole) formation. The paper investigates the phenomenon of sand production in a thick wall cylinder test that mimics both axial and radial flow near an oil well perforation. Then, an actual well section with perforations is analyzed with respect to sand production, failure, and cavity formation in the form of a wormhole. The numerical analysis is based on a reservoir-geomechanics model developed by the authors over the past several years. The model considers oil, fluidized sand, and sand phases interacting together through mechanical stresses and hydrodynamics within the framework of mixture theory. Interesting sand production mechanisms emerge from the interaction between geomechanics and an erosion process by which sand grains are detached from the solid matrix due to both fluid and stress gradients, once a certain level of material failure is reached. Introduction Sand production is a costly and inevitable phenomenon that occurs whenever drag forces on sand particles, induced by fluid flow and/or solution-gas drive, exceed the inter-granular forces (related to macroscopic strength of formation) so as to lead to the loss of mechanical integrity. This pre-supposes that the material has to undergo yielding and reach incipient failure from a geomechanical viewpoint before sand production may occur. There could also be another mechanism by which the sand grains crush under extreme stresses and fragment into smaller particles that become mobile. In any of the above cases, the material collapses locally, and the sand fragments are carried into the wellbore where they can block the flow, damage pumps and pipes, and contaminate the produced oil. Sand production creates cavities in the formation that continually increase in size and eventually become unstable, leading to the collapse of the wellbore. Each year, sanding problems cost the oil industry hundreds of millions of dollars. Hence, it is pertinent to study the mechanics of sand failure and its interaction with hydrodynamics with the view of developing an efficient computational model that can be used to predict sand production during field operations. Vardoulakis et al.(1) were probably among the first to tackle the sand production problem as an erosion phenomenon, proposing a hydro-erosion model based on rigid porous media within a continuum mechanics framework in which mass balance is applied to a three-phase system comprised of solid, fluid, and fluidized solid using mixture theory(2). Subsequently, Wan and Wang(3–6) extended this pure erosion model to include the effect of the deformation of porous media, and more explicitly, material failure aspects. This approach results in solving a set of coupled non-linear time-dependent equations with fluidized solid concentration, fluid pressure, porosity, and deformation as main variables.

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 score0.106
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.002
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 head0.180
Teacher spread0.172 · 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

Citations14
Published2006
Admission routes1
Has abstractyes

Explore more

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