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Record W2063669369 · doi:10.2118/02-04-05

Transport of Fine Sand From a Wellbore

2002· article· en· W2063669369 on OpenAlexafffundabout
Kerry A. Mazurek, Richard J. Chalaturnyk, N. Rajaratnam, J. D. Scott

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of AlbertaUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOil sandsSlurryAnnulus (botany)Petroleum engineeringChokeFlow (mathematics)Geotechnical engineeringOil wellEnvironmental scienceOil fieldWellboreGeologyEngineeringEnvironmental engineeringMechanicsMaterials scienceAsphalt

Abstract

fetched live from OpenAlex

Abstract This paper presents the results of an experimental study on the transport of fine sand in the entrance region of a progressing cavity pump. The study was performed to provide additional information on the behaviour of the sand slurry flow within a heavy oil well. Tests were carried out with both glycerine and water flows, with the addition of sand in the form of slugs or as a continuous feed to form a sand slurry. Several geometries of the pump intake were used. The different flow regimes observed suggest modes of operation of heavy oil wells that may be used to better avoid sanding or shutting in of wells. These results, in addition to being useful to industry, should also inspire more theoretical studies to explain the formation of the various flow features. Introduction The flow in a heavy oil well consists of a mixture of very viscous heavy oil, gas, water, and fine sand. For these wells, sand production can be a costly problem(1). Within the pump entrance area, there is potential for the sand to choke the flow within the well ("sanding") and production equipment. Sand may form bridges or arches within the annulus of the well to reduce flow tothe pump. Sand may also restrict flow at the intake to the pump, which can result in pump damage. It is possible that the problems associated with sand production are so costly that the well does not operate within economic limits, and is subsequently shut in or abandoned. Field monitoring of solids production in heavy oil wells typically classifies sand entrance into the wellbore to be either "continuous," where a reasonably constant value of sand is produced, or "sluggery," where slugs with high sand concentrations enter the wellbore(1). This paper presents the results of a visualization study to investigate the mechanics of slurry flow in a heavy oil well in the area around the entrance of a progressing cavity pump(2, 3). The purpose of the study was to investigate the flow regimes created in the pump entrance area by changes in the flow rate and properties of the sand slurry fed into the well, and to assess the impact of each on sand removal in the area around the pump intake. Both a very viscous glycerine-sand slurry and a water-sand slurry were used in the experiments to examine the effect of fluid viscosity. The transport of sand was observed in a single liquid phase, with the sand added in slug form or as a continuous feed. The study also investigated the effect of the geometry of the pump intake region on sand removal from the area. Several interesting flow phenomena were observed, and are described herein. Experimental Arrangement and Experiments The visualization model, shown in Figure 1, examined a 1.35 m section around the pump entrance and was close in size to typical heavy oil wells in Western Canada.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.165
Teacher spread0.160 · 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 designBench or experimental
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

Citations2
Published2002
Admission routes3
Has abstractyes

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