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Record W2033789301 · doi:10.2118/2006-093

Numerical Investigation of Sand Production Under Realistic Reservoir/Well Flow Conditions

2006· article· en· W2033789301 on OpenAlexafffund
Alireza Nouri, Hans Vaziri, Ergün Kuru

Bibliographic record

VenueCanadian International Petroleum Conference · 2006
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaKillam Trusts
KeywordsPetroleum engineeringProduction (economics)Flow (mathematics)Reservoir simulationComputer scienceGeologyEnvironmental scienceGeotechnical engineeringMechanicsPhysics

Abstract

fetched live from OpenAlex

Abstract A new numerical model has been developed for investigation of sand production under realistic reservoir/well flow conditions. The model allows prediction of critical drawdown leading to the onset of sanding as well as the rate of sand production in real time. The model predictions have been validated by using laboratory data. The proposed numerical model has been embedded into ABAQUS, which is a finite element program capable of simulating interaction between fluid flow and mechanical deformation of the medium. The model has been designed to encompass a number of the factors that are influential in the process of sanding. This includes a time-dependent coupled fluid flow and deformation analysis of the rock, material disaggregation, sand removal, and operational conditions including drawdown, depletion, and water-cut. The model has been validated by using the experimental data on hollow cylinder specimens involving real time sand production measurements under various conditions. The results of the numerical modeling study show a good agreement with experimental data in terms of the operational conditions leading to the onset of sanding as well as an estimation of the sanding rate. The model presents a live picture of the ongoing alterations in the material at the wellface during the production which provides a deeper insight into the role of the various parameters involved. Introduction Several sand production prediction methods have been proposed using geomechanical models. These methods could be grouped into analytical (e.g., Risnes et al. 1982, Morita et al. 1989a, Weingarten & Perkins 1992, van den Hoek et al. 2003) and numerical models (e.g., Morita et al, 1987a, Stavropoulou et al. 1998, Papamichos & Malmanger 1999, Vaziri et al. 2002, Nouri et al. 2006). The analytical models provide formulations for the flow rate required to induce tensile failure. Tensile failure of the material due to seepage drag forces is taken as criterion for sand production. Their limitation is the general constraints with respect to geometry, boundary conditions, and implementation of intricate material behavior. Further, they fall short in providing an indication of the severity of sanding once it is triggered. Numerical models could overcome many of the limitations mentioned above. Some of these models work by modeling rock disaggregation at cavity face in post-peak strength phase of the rock (e.g. Nouri et al. 2006). Others tie sand production to the mobilized plastic strain level (e.g. Morita & Fuh 1998). Sand production in these models occurs once equivalent plastic strain exceeds a threshold. Some numerical models assume sand production to be in the form of sand erosion which is tied to mechanical damage of rock around a wellbore (e.g. Geilikman et al. 1997, Vardoulakis et al. 1996, Stavropoulou et al. 1998). A critical review of various sand production models was provided in Nouri et al. (2006). This paper presented a finite difference model which used continuum mechanics approach for modeling the process of sanding as a function of several parameters that have an effect on sand production. These include: operation conditions, i.e., drawdown and depletion, completion technique, formation strength and mechanical behavior, permeability, and a moving boundary due to solid material flow, i.e., sand production.

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 score0.136
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.205
Teacher spread0.192 · 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

Citations12
Published2006
Admission routes2
Has abstractyes

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