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Record W1982222600 · doi:10.2118/02-04-01

A Model for Sand Transport Through a Partially Filled Wormhole in Cold Production

2002· article· en· W1982222600 on OpenAlexfundno aff
Jiaguo Yuan, A. Babchin, B. Tremblay

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsWormholeSlurryBingham plasticRheologyMechanicsGeotechnical engineeringYield (engineering)Flow (mathematics)Petroleum engineeringMaterials scienceGeologyPhysicsComposite materialClassical mechanics

Abstract

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Abstract We propose a simple model of flow in a partially filled wormhole, where layers of oil, slurry and immobile sand can coexist. The slurry at the bottom of the wormhole is assumed to behave as a Bingham material, i.e., it yields when the shear stress exceeds the yield stress. The yield stress is assumed to increase; with depth due to the weight of the overlying sand. A Mohr-Coulomb equation is used to calculate the yield stress within the slurry and immobile sand layers. Linearized Navier- Stokes equations are solved to calculate the oil and mobile sand flow rates. Different characteristic oil and sand flow patterns are studied. Oil and sand flow rates through the wormhole are calculated as functions of pressure gradient and rheological parameters. This simple model could be used to estimate sand transport in a partially filled wormhole, complementing a sand transport model for uniformly filled wormholes(4). These wormhole flow models can be incorporated into a field scale model for cold production. Introduction Cold production is a non-thermal primary heavy oil recovery process in which sand production is encouraged. Previous field and laboratory studies(1–3) suggest that high permeability channels (wormholes) develop within the formation starting at the perforations in a cold production well. These wormholes provide high effective fluid mobility, leading to a higher oil recovery as comparedto conventional primary recovery without sand production. According to previous models we developed(4, 5), in the first few months of cold production, wormholes grow rapidly, forming a wormhole network. The produced sand cuts are high during this period. Based on laboratory experiments, during this early stage, most wormholes are uniformly filled with slurry, a mixture of oil and liquefied sand. At later stages of cold production, however, the expansion of the wormhole network zone slows down, resulting in reduced sand cuts. In previous experiments conducted at ARC, we observed the creation and extension of a sand-free zone in the upper part of a wormhole after the wormholes stopped growing(3). The flow behaviour under this circumstance is very different from that in a uniformly filled wormhole. In this work, we use a simplified description of the layered flow in partially filled wormhole. A comparison with experimental results indicates that this simple model could be used for the estimation of flow properties in a partially filled wormhole, complementing the sand transport model for a fully filled wormhole and the wormhole network model. Backgroound The actual geometry of a wormhole is complicated. Therefore, we assumed a plane geometry, as is shown in Figure 1. In the region 0 ≤y ≤h, we assume that sand and oil flow with at the same velocity; Equation (Available In Full Paper) The rheology of the slurry is described by the Bingham Equation(8): Equation (Available In Full Paper) where, sign(π) = 1 for positive π, and -1 for negative π. The righthand side of this equation is zero if the magnitude of the shear stress is less than the yield stress of the slurry.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.202
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2002
Admission routes1
Has abstractyes

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