Statistical assessment of the financial performance of shale-gas wells coupling stochastic and numerical simulation

UDC.coleccionInvestigación
UDC.departamentoEmpresa
UDC.endPage4511
UDC.grupoInvGrupo de Métodos Numéricos en Enxeñaría (GMNI)
UDC.journalTitlePetroleum Science
UDC.startPage4497
UDC.volume21
dc.contributor.authorSoage Quintáns, Manuel Andrés
dc.contributor.authorRamírez, Luis
dc.contributor.authorJuanes, Rubén
dc.contributor.authorCueto-Felgueroso Landeira, Luis
dc.contributor.authorColominas, Ignasi
dc.date.accessioned2025-10-27T12:15:13Z
dc.date.available2025-10-27T12:15:13Z
dc.date.issued2024
dc.description.abstract[Abstract]: We present a new methodology to statistically determine the net present value (NPV) and internal rate of return (IRR) as financial estimators of shale gas investments. Our method allows us to forecast, in a fully probabilistic setting, financial performance risk and to understand the importance of the different factors that impact investment. The methodology developed in this study combines, through Monte Carlo simulation, the computational modeling of gas production from shale gas wells with a stochastic simulation of gas price as a geometric Brownian motion (GMB). To illustrate the methodology's validity, we apply it to an analysis of investments in shale gas wells. Our results show that gas price volatility is a key variable in the performance of an investment of this type, in such a way that at high volatilities, the potential return on an investment in shale gas increases significantly, but so do the risks of economic loss. This finding is consistent with the history of shale gas operations in which huge investment successes coexist with unexpected investment failures.
dc.description.sponsorshipThis research was partially funded by Goverment of Spain, Ministry of Science, Innovation and Universities (grant: RTI2018- 093366-B-I00), by Goverment of Spain, Ministry of Universities (grant: Subsidies to Public Universities for the Requalification of the Spanish University System, “Margarita Salas” Grants Modality for the Training of Young Doctors, RD 289/2021 of April 20), by the Xunta de Galicia, Consellería de Educación e Ordenación Universitaria (grant:#ED431C 2018/41), and by the Group of Numerical Methods in Engineering of the Universidade de A Coruna.
dc.description.sponsorshipXunta de Galicia; ED431C 2018/41
dc.identifier.citationSoage, A., Ramírez, L., Juanes, R., Cueto-Felgueroso, L., & Colominas, I. (2024). Statistical assessment of the financial performance of shale-gas wells coupling stochastic and numerical simulation. Petroleum Science, 21, 4497–4511. https://doi.org/10.1016/j.petsci.2024.07.018
dc.identifier.doi10.1016/j.petsci.2024.07.018
dc.identifier.issn1672-5107
dc.identifier.issn1995-8226
dc.identifier.urihttps://hdl.handle.net/2183/46108
dc.language.isoeng
dc.publisherKeAi
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018- 093366-B-I00/ES/ NUEVOS METODOS SIN MALLA PARA LA SIMULACION NUMERICA DE FLUJOS TURBULENTOS Y PROBLEMAS DE MULTIFISICA. APLICACION AL DESARROLLO DE SISTEMAS DE GENERACION DE ENERGIA RENOVABLE
dc.relation.urihttps://doi.org/10.1016/j.petsci.2024.07.018
dc.rightsAttribution 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectGas volatility
dc.subjectShale gas
dc.subjectNet present value
dc.subjectInternal rate return
dc.subjectStochastic model
dc.subjectFinancial estimator
dc.subjectMonte Carlo simulation
dc.subjectKernel density function
dc.titleStatistical assessment of the financial performance of shale-gas wells coupling stochastic and numerical simulation
dc.typejournal article
dc.type.hasVersionVoR
dspace.entity.typePublication
relation.isAuthorOfPublicatione80b65d0-8fe1-40a8-8330-3b72a786d274
relation.isAuthorOfPublicationc4cc7129-537d-4f52-a790-089d5159d041
relation.isAuthorOfPublication338d0b0b-e58e-490d-aa25-bb0910154513
relation.isAuthorOfPublication.latestForDiscoverye80b65d0-8fe1-40a8-8330-3b72a786d274

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