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Record W2053976432 · doi:10.2118/2000-072

Modelling Sand Production Within a Continuum Mechanics Framework

2000· article· en· W2053976432 on OpenAlexaffabout
Richard Wan, J. Wang

Bibliographic record

VenueCanadian International Petroleum Conference · 2000
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Calgary
FundersU.S. Department of Energy
KeywordsContinuum mechanicsMechanicsEnvironmental scienceGeologyPhysics

Abstract

fetched live from OpenAlex

Abstract The paper presents a continuum mechanics framework in which sand production can be modelled on the basis of an erosional mechanism. A representative elementary volume comprised of three constituents namely solid, fluid, and fluidized solids is chosen upon which mass balance and particle transport equations are written. The erosional process is described by a particle generationconstitutive law. The coupled non-linear governing equations are finally solved using the finite element technique with a Netwon-Raphson scheme. In order to illustrate the capabilities of the model, the evolution of controlling field variables such as porosity, fluidized sand concentration and pressure distributions are computed for a wellbore subjected to a pressure gradient corresponding to fluid draw down during pumping. In the case of anisotropic permeability conditions, sand production is at its peak value at different points around the wellbore and there is a time lag between the times at which each point reaches its peak. This is due to the averaged fluid and fluidized sand fluxes being non radial. Introduction Sand production from unconsolidated formations occurs when the well fluid being produced under high pumping rate dislodges a portion of the formation solids leading to a continuous flux of formation solids. In Alberta, the formation from which fluid (hydrocarbon) is being produced is oil sand, while the solids coming out with the fluid is sand, though solids can be produced from a variety of other formation types such as sandstones. Sand in the well fluid can erode casing, pipes and pumps or plug the well if sufficient quantities are produced. The distinct periods of sand production in the well and reservoir life as pressure is being depleted were discussed by Morita et. al. (1987)[1]. There are a number of schemes to address sand production. Exclusion schemes involve the installation of gravel packs or screen filters in order to catch the sand as it enters the well. However, by doing so, the flow rate of heavy oil in the well can drastically fall from 7–15 m3/day to may be 0.5-5 m3/day. On the other hand, avoidance schemes include pressure and fluid rate control, selective perforations, and resin injection to coat sand. As operators are following more aggressive production schedules, this has led to a demand for understanding the phenomenon of sand production mechanisms so that correct predictions of the anticipated amount of produced sand as a function of time, applied stress, and fluid flow rates can be made. The challenge is to develop a mathematical tool with a predictive capability that will allow oil operators to devise pumping schemes that produce hydrocarbons at a flow-rate just below one that will produce sand. The physics of sand production is not clearly understood although that oil rate enhancement is often linked to solution gas effects, sand movement and growth of large high-permeability regions or cavities known as wormholes[2]. It is believed that wormhole formation is driven by fluid flux as it exerts a drag force strong enough on the oil sand matrix to overcome frictional forces that hold the grains together.

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: none
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.190
Teacher spread0.178 · 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

Citations22
Published2000
Admission routes2
Has abstractyes

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