Stochastic programming models for optimal shale well development and refracturing planning under uncertainty

Autores
Drouven, Markus G.; Grossmann, Ignacio E.; Cafaro, Diego Carlos
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this work we present an optimization framework for shale gas well development and refracturing planning. This problem is concerned with if and when a new shale gas well should be drilled at a prospective location, and whether or not it should be refractured over its lifespan. We account for exogenous gas price uncertainty and endogenous well performance uncertainty. We propose a mixed-integer linear, two-stage stochastic programming model embedded in a moving horizon strategy to dynamically solve the planning problem. A generalized production estimate function is described that predicts the gas production over time depending on how often a well has been refractured, and when exactly it was restimulated last. From a detailed case study, we conclude that early in the life of an active shale well, refracturing makes economic sense even in low-price environments, whereas additional restimulations only appear to be justified if prices are high.
Fil: Drouven, Markus G.. University Of Carnegie Mellon. Department Of Chemical Engineering; Estados Unidos
Fil: Grossmann, Ignacio E.. University Of Carnegie Mellon. Department Of Chemical Engineering; Estados Unidos
Fil: Cafaro, Diego Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Materia
Shale Gas
Refracturing
Planning
Stochastic Programming
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/20626
Acceder
id CONICETDig_1052e7368bc2f2a98fc8cd3c9a459786
oai_identifier_str oai:ri.conicet.gov.ar:11336/20626
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Stochastic programming models for optimal shale well development and refracturing planning under uncertaintyDrouven, Markus G.Grossmann, Ignacio E.Cafaro, Diego CarlosShale GasRefracturingPlanningStochastic Programminghttps://purl.org/becyt/ford/2.4https://purl.org/becyt/ford/2In this work we present an optimization framework for shale gas well development and refracturing planning. This problem is concerned with if and when a new shale gas well should be drilled at a prospective location, and whether or not it should be refractured over its lifespan. We account for exogenous gas price uncertainty and endogenous well performance uncertainty. We propose a mixed-integer linear, two-stage stochastic programming model embedded in a moving horizon strategy to dynamically solve the planning problem. A generalized production estimate function is described that predicts the gas production over time depending on how often a well has been refractured, and when exactly it was restimulated last. From a detailed case study, we conclude that early in the life of an active shale well, refracturing makes economic sense even in low-price environments, whereas additional restimulations only appear to be justified if prices are high.Fil: Drouven, Markus G.. University Of Carnegie Mellon. Department Of Chemical Engineering; Estados UnidosFil: Grossmann, Ignacio E.. University Of Carnegie Mellon. Department Of Chemical Engineering; Estados UnidosFil: Cafaro, Diego Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaWiley2017-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/20626Drouven, Markus G.; Grossmann, Ignacio E.; Cafaro, Diego Carlos; Stochastic programming models for optimal shale well development and refracturing planning under uncertainty; Wiley; Aiche Journal; 6-20170001-1541CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1002/aic.15804info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/aic.15804/abstractinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:06:25Zoai:ri.conicet.gov.ar:11336/20626instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:06:25.297CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Stochastic programming models for optimal shale well development and refracturing planning under uncertainty
title Stochastic programming models for optimal shale well development and refracturing planning under uncertainty
spellingShingle Stochastic programming models for optimal shale well development and refracturing planning under uncertainty
Drouven, Markus G.
Shale Gas
Refracturing
Planning
Stochastic Programming
title_short Stochastic programming models for optimal shale well development and refracturing planning under uncertainty
title_full Stochastic programming models for optimal shale well development and refracturing planning under uncertainty
title_fullStr Stochastic programming models for optimal shale well development and refracturing planning under uncertainty
title_full_unstemmed Stochastic programming models for optimal shale well development and refracturing planning under uncertainty
title_sort Stochastic programming models for optimal shale well development and refracturing planning under uncertainty
dc.creator.none.fl_str_mv Drouven, Markus G.
Grossmann, Ignacio E.
Cafaro, Diego Carlos
author Drouven, Markus G.
author_facet Drouven, Markus G.
Grossmann, Ignacio E.
Cafaro, Diego Carlos
author_role author
author2 Grossmann, Ignacio E.
Cafaro, Diego Carlos
author2_role author
author
dc.subject.none.fl_str_mv Shale Gas
Refracturing
Planning
Stochastic Programming
topic Shale Gas
Refracturing
Planning
Stochastic Programming
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.4
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv In this work we present an optimization framework for shale gas well development and refracturing planning. This problem is concerned with if and when a new shale gas well should be drilled at a prospective location, and whether or not it should be refractured over its lifespan. We account for exogenous gas price uncertainty and endogenous well performance uncertainty. We propose a mixed-integer linear, two-stage stochastic programming model embedded in a moving horizon strategy to dynamically solve the planning problem. A generalized production estimate function is described that predicts the gas production over time depending on how often a well has been refractured, and when exactly it was restimulated last. From a detailed case study, we conclude that early in the life of an active shale well, refracturing makes economic sense even in low-price environments, whereas additional restimulations only appear to be justified if prices are high.
Fil: Drouven, Markus G.. University Of Carnegie Mellon. Department Of Chemical Engineering; Estados Unidos
Fil: Grossmann, Ignacio E.. University Of Carnegie Mellon. Department Of Chemical Engineering; Estados Unidos
Fil: Cafaro, Diego Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
description In this work we present an optimization framework for shale gas well development and refracturing planning. This problem is concerned with if and when a new shale gas well should be drilled at a prospective location, and whether or not it should be refractured over its lifespan. We account for exogenous gas price uncertainty and endogenous well performance uncertainty. We propose a mixed-integer linear, two-stage stochastic programming model embedded in a moving horizon strategy to dynamically solve the planning problem. A generalized production estimate function is described that predicts the gas production over time depending on how often a well has been refractured, and when exactly it was restimulated last. From a detailed case study, we conclude that early in the life of an active shale well, refracturing makes economic sense even in low-price environments, whereas additional restimulations only appear to be justified if prices are high.
publishDate 2017
dc.date.none.fl_str_mv 2017-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/20626
Drouven, Markus G.; Grossmann, Ignacio E.; Cafaro, Diego Carlos; Stochastic programming models for optimal shale well development and refracturing planning under uncertainty; Wiley; Aiche Journal; 6-2017
0001-1541
CONICET Digital
CONICET
url http://hdl.handle.net/11336/20626
identifier_str_mv Drouven, Markus G.; Grossmann, Ignacio E.; Cafaro, Diego Carlos; Stochastic programming models for optimal shale well development and refracturing planning under uncertainty; Wiley; Aiche Journal; 6-2017
0001-1541
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1002/aic.15804
info:eu-repo/semantics/altIdentifier/url/http://onlinelibrary.wiley.com/doi/10.1002/aic.15804/abstract
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Wiley
publisher.none.fl_str_mv Wiley
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842269957778309120
score 13.13397

Publicaciones similares