Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis

UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.grupoInvGrupo de Métodos Numéricos en Enxeñaría (GMNI)es_ES
UDC.issue4es_ES
UDC.journalTitleEnergieses_ES
UDC.startPage864es_ES
UDC.volume17es_ES
dc.contributor.authorSoage Quintáns, Manuel Andrés
dc.contributor.authorJuanes, Rubén
dc.contributor.authorColominas, Ignasi
dc.contributor.authorCueto-Felgueroso Landeira, Luis
dc.contributor.otherGrupo de Métodos Numéricos na Enxeñería (GMNE)es_ES
dc.date.accessioned2024-02-16T14:20:12Z
dc.date.available2024-02-16T14:20:12Z
dc.date.issued2024
dc.descriptionEste artigo pertence ao número especial: Integrated Approaches for Unconventional Oil and Gas Extraction and Explorationes_ES
dc.description.abstract[Abstract:] We present a methodology to determine optimal financial parameters in shale-gas production, combining numerical simulation of decline curves and stochastic modeling of the gas price. The mathematical model of gas production considers free gas in the pore and the gas adsorbed in kerogen. The dependence of gas production on petrophysical parameters and stimulated permeability is quantified by solving the model equations in a 3D geometry representing a typical fractured shale well. We use Monte Carlo simulation to characterize the statistical properties of various common financial indicators of the investment in shale-gas. The analysis combines many realizations of the physical model, which explores the variability of porosity, induced permeability, and fracture geometry, with thousands of realizations of gas price trajectories. The evolution of gas prices is modeled using the bootstrapping statistical resampling technique to obtain a probability density function of the initial price, the drift, and the volatility of a geometric Brownian motion for the time evolution of gas price. We analyze the Net Present Value (NPV), Internal Rate of Return (IRR), and Discounted Payback Period (DPP) indicators. By computing the probability density function of each indicator, we characterize the statistical percentile of each value of the indicator. Alternatively, we can infer the value of the indicator for a given statistical percentile. By mapping these parametric combinations for different indicators, we can determine the parameters that maximize or minimize each of them. We show that, to achieve a profitable investment in shale-gas with high certainty, it is necessary to place the wells in extremely good locations in terms of geological parameters (porosity) and to have exceptional fracturing technology (geometry) and fracture permeability. These high demands in terms of petrophysical properties and hydrofracture engineering may explain the industry observation of “sweet spots”, that is, specific areas within shale-gas plays that tend to yield more profitable wells and where many operators concentrate their production. We shed light on the rational origin of this phenomenon: while shale formations are abundant, areas prone to having a multi-parameter combination that renders the well profitable are less common.es_ES
dc.description.sponsorshipThis research has been partially funded by the Ministry of Universities of the Spanish Government (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 strategic projects oriented towards the ecological transition and the digital transition of the Ministry of Science and Innovation: GREEN-HUGS (grant #TED2021-129991B-C31 and grant #TED2021-129991B-C32) and NEPTUNE (grant #TED2021-129805B-I00), by the Department of Education and University Planning of the Xunta de Galicia (grant #ED431C 2022/06), and by the Group of Numerical Methods in Engineering of the Universidade da Coruña.es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2022/06es_ES
dc.identifier.citationSoage, A., Juanes, R., Colominas, I., & Cueto-Felgueroso, L. (2024). Optimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysis. Energies, 17(4), 864. https://doi.org/10.3390/en17040864es_ES
dc.identifier.doi10.3390/en17040864
dc.identifier.urihttp://hdl.handle.net/2183/35644
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/ Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-129991B-C31/ES/Proyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digitales_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/ Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-129991B-C32/ES/Proyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digitales_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/ Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/TED2021-129805B-I00/ES/Proyectos Estratégicos Orientados a la Transición Ecológica y a la Transición Digitales_ES
dc.relation.urihttps://doi.org/10.3390/en17040864es_ES
dc.rightsAtribución 3.0 Españaes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectUnconventional resources of hydrocarbonses_ES
dc.subjectEconomic geology of fossil fuelses_ES
dc.subjectNumerical decline curve analysises_ES
dc.subjectEconomic performance shale-gases_ES
dc.subjectShale-gas 3D production modeles_ES
dc.titleOptimization of Financial Indicators in Shale-Gas Wells Combining Numerical Decline Curve Analysis and Economic Data Analysises_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublicatione80b65d0-8fe1-40a8-8330-3b72a786d274
relation.isAuthorOfPublication338d0b0b-e58e-490d-aa25-bb0910154513
relation.isAuthorOfPublication.latestForDiscoverye80b65d0-8fe1-40a8-8330-3b72a786d274

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