Numerical model for predicting and evaluating sand production in weakly consolidated reservoirs
Abstract Sand production is a common phenomenon in oil and gas reservoirs, which occurs when reservoir fluids exert a sufficient drag force on reservoir rocks to erode the matrix. Numerical models for sand production have been used to understand the sanding mechanisms and forecast sand-production potential of formations to design well completion, optimize production, and prevent setbacks in future operations. This paper presents a mathematical model for defining the conditions of sanding onset as well as to predict and quantify the sand rate. We also introduce fluid-flow coupling and a geomechanical and sand-production model. By using the proposed model and a set of experimental data, sanding-related variables are analyzed, and a matching process for the simulated results and forecast analysis are performed. The results show that elastoplastic constitutive models are indispensable, and a clear relationship exists between the sanding and plastic strains.
Main Authors: | , , , |
---|---|
Format: | Digital revista |
Language: | English |
Published: |
Universidad Nacional de Colombia
2022
|
Online Access: | http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532022000100054 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
oai:scielo:S0012-73532022000100054 |
---|---|
record_format |
ojs |
spelling |
oai:scielo:S0012-735320220001000542022-09-19Numerical model for predicting and evaluating sand production in weakly consolidated reservoirsAraujo-Guerrero,Edson FelipeMorales-Monsalve,Cristhian BernardoAlzate-Espinosa,Guillermo ArturoArbelaez-Londoño,Alejandra geomechanics sanding numerical simulation elastoplasticity mathematical modeling Abstract Sand production is a common phenomenon in oil and gas reservoirs, which occurs when reservoir fluids exert a sufficient drag force on reservoir rocks to erode the matrix. Numerical models for sand production have been used to understand the sanding mechanisms and forecast sand-production potential of formations to design well completion, optimize production, and prevent setbacks in future operations. This paper presents a mathematical model for defining the conditions of sanding onset as well as to predict and quantify the sand rate. We also introduce fluid-flow coupling and a geomechanical and sand-production model. By using the proposed model and a set of experimental data, sanding-related variables are analyzed, and a matching process for the simulated results and forecast analysis are performed. The results show that elastoplastic constitutive models are indispensable, and a clear relationship exists between the sanding and plastic strains.info:eu-repo/semantics/openAccessUniversidad Nacional de ColombiaDYNA v.89 n.220 20222022-03-01info:eu-repo/semantics/articletext/htmlhttp://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532022000100054en10.15446/dyna.v89n220.97093 |
institution |
SCIELO |
collection |
OJS |
country |
Colombia |
countrycode |
CO |
component |
Revista |
access |
En linea |
databasecode |
rev-scielo-co |
tag |
revista |
region |
America del Sur |
libraryname |
SciELO |
language |
English |
format |
Digital |
author |
Araujo-Guerrero,Edson Felipe Morales-Monsalve,Cristhian Bernardo Alzate-Espinosa,Guillermo Arturo Arbelaez-Londoño,Alejandra |
spellingShingle |
Araujo-Guerrero,Edson Felipe Morales-Monsalve,Cristhian Bernardo Alzate-Espinosa,Guillermo Arturo Arbelaez-Londoño,Alejandra Numerical model for predicting and evaluating sand production in weakly consolidated reservoirs |
author_facet |
Araujo-Guerrero,Edson Felipe Morales-Monsalve,Cristhian Bernardo Alzate-Espinosa,Guillermo Arturo Arbelaez-Londoño,Alejandra |
author_sort |
Araujo-Guerrero,Edson Felipe |
title |
Numerical model for predicting and evaluating sand production in weakly consolidated reservoirs |
title_short |
Numerical model for predicting and evaluating sand production in weakly consolidated reservoirs |
title_full |
Numerical model for predicting and evaluating sand production in weakly consolidated reservoirs |
title_fullStr |
Numerical model for predicting and evaluating sand production in weakly consolidated reservoirs |
title_full_unstemmed |
Numerical model for predicting and evaluating sand production in weakly consolidated reservoirs |
title_sort |
numerical model for predicting and evaluating sand production in weakly consolidated reservoirs |
description |
Abstract Sand production is a common phenomenon in oil and gas reservoirs, which occurs when reservoir fluids exert a sufficient drag force on reservoir rocks to erode the matrix. Numerical models for sand production have been used to understand the sanding mechanisms and forecast sand-production potential of formations to design well completion, optimize production, and prevent setbacks in future operations. This paper presents a mathematical model for defining the conditions of sanding onset as well as to predict and quantify the sand rate. We also introduce fluid-flow coupling and a geomechanical and sand-production model. By using the proposed model and a set of experimental data, sanding-related variables are analyzed, and a matching process for the simulated results and forecast analysis are performed. The results show that elastoplastic constitutive models are indispensable, and a clear relationship exists between the sanding and plastic strains. |
publisher |
Universidad Nacional de Colombia |
publishDate |
2022 |
url |
http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0012-73532022000100054 |
work_keys_str_mv |
AT araujoguerreroedsonfelipe numericalmodelforpredictingandevaluatingsandproductioninweaklyconsolidatedreservoirs AT moralesmonsalvecristhianbernardo numericalmodelforpredictingandevaluatingsandproductioninweaklyconsolidatedreservoirs AT alzateespinosaguillermoarturo numericalmodelforpredictingandevaluatingsandproductioninweaklyconsolidatedreservoirs AT arbelaezlondonoalejandra numericalmodelforpredictingandevaluatingsandproductioninweaklyconsolidatedreservoirs |
_version_ |
1755932777378742272 |