Taherdangkoo, Reza

[["dc.bibliographiccitation.issue","18"],["dc.bibliographiccitation.journal","Environmental Earth Sciences"],["dc.bibliographiccitation.volume","79"],["dc.contributor.author","Taherdangkoo, Reza"],["dc.contributor.author","Tatomir, Alexandru"],["dc.contributor.author","Sauter, Martin"],["dc.date.accessioned","2021-04-14T08:23:33Z"],["dc.date.available","2021-04-14T08:23:33Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1007/s12665-020-09170-5"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/80960"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","1866-6299"],["dc.relation.issn","1866-6280"],["dc.title","Modeling of methane migration from gas wellbores into shallow groundwater at basin scale"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","S0169772221000838"],["dc.bibliographiccitation.firstpage","103844"],["dc.bibliographiccitation.journal","Journal of Contaminant Hydrology"],["dc.bibliographiccitation.volume","242"],["dc.contributor.author","Taherdangkoo, Reza"],["dc.contributor.author","Liu, Quan"],["dc.contributor.author","Xing, Yixuan"],["dc.contributor.author","Yang, Huichen"],["dc.contributor.author","Cao, Viet"],["dc.contributor.author","Sauter, Martin"],["dc.contributor.author","Butscher, Christoph"],["dc.date.accessioned","2021-12-01T09:23:36Z"],["dc.date.available","2021-12-01T09:23:36Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1016/j.jconhyd.2021.103844"],["dc.identifier.pii","S0169772221000838"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94701"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.relation.issn","0169-7722"],["dc.title","Predicting methane solubility in water and seawater by machine learning algorithms: Application to methane transport modeling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","841"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","Water"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Taherdangkoo, Reza"],["dc.contributor.author","Tatomir, Alexandru"],["dc.contributor.author","Taherdangkoo, Mohammad"],["dc.contributor.author","Qiu, Pengxiang"],["dc.contributor.author","Sauter, Martin"],["dc.date.accessioned","2020-12-10T18:47:26Z"],["dc.date.available","2020-12-10T18:47:26Z"],["dc.date.issued","2020"],["dc.description.sponsorship","Horizon 2020 Framework Programme"],["dc.identifier.doi","10.3390/w12030841"],["dc.identifier.eissn","2073-4441"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17370"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/78762"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","2073-4441"],["dc.rights","CC BY 4.0"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Nonlinear Autoregressive Neural Networks to Predict Hydraulic Fracturing Fluid Leakage into Shallow Groundwater"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","185"],["dc.bibliographiccitation.journal","Advances in Geosciences"],["dc.bibliographiccitation.lastpage","192"],["dc.bibliographiccitation.volume","45"],["dc.contributor.author","Tatomir, Alexandru"],["dc.contributor.author","McDermott, Christopher"],["dc.contributor.author","Bensabat, Jacob"],["dc.contributor.author","Class, Holger"],["dc.contributor.author","Edlmann, Katriona"],["dc.contributor.author","Taherdangkoo, Reza"],["dc.contributor.author","Sauter, Martin"],["dc.date.accessioned","2019-07-09T11:46:01Z"],["dc.date.available","2019-07-09T11:46:01Z"],["dc.date.issued","2018"],["dc.description.abstract","Hydraulic fracturing for natural gas extraction from unconventional reservoirs has not only impacted the global energy landscape but has also raised concerns over its potential environmental impacts. The concept of “features, events and processes” (FEP) refers to identifying and selecting the most relevant factors for safety assessment studies. In the context of hydraulic fracturing we constructed a comprehensive FEP database and applied it to six key focused scenarios defined under the scope of FracRisk project (http://www.fracrisk.eu, last access: 17 August 2018). The FEP database is ranked to show the relevance of each item in the FEP list per scenario. The main goal of the work is to illustrate the FEP database applicability to develop a conceptual model for regional-scale stray gas migration."],["dc.identifier.doi","10.5194/adgeo-45-185-2018"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15379"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59364"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/636811/EU//FracRisk"],["dc.relation.issn","1680-7359"],["dc.subject.ddc","550"],["dc.title","Conceptual model development using a generic Features, Events, and Processes (FEP) database for assessing the potential impact of hydraulic fracturing on groundwater aquifers"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","58"],["dc.bibliographiccitation.journal","Journal of Contaminant Hydrology"],["dc.bibliographiccitation.lastpage","68"],["dc.bibliographiccitation.volume","221"],["dc.contributor.author","Taherdangkoo, Reza"],["dc.contributor.author","Tatomir, Alexandru"],["dc.contributor.author","Anighoro, Tega"],["dc.contributor.author","Sauter, Martin"],["dc.date.accessioned","2020-12-10T14:25:02Z"],["dc.date.available","2020-12-10T14:25:02Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1016/j.jconhyd.2018.12.003"],["dc.identifier.issn","0169-7722"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/72415"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","Modeling fate and transport of hydraulic fracturing fluid in the presence of abandoned wells"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","126"],["dc.bibliographiccitation.journal","Energy Procedia"],["dc.bibliographiccitation.lastpage","135"],["dc.bibliographiccitation.volume","125"],["dc.contributor.author","Tatomir, Alexandru"],["dc.contributor.author","Taylor, Robert W."],["dc.contributor.author","Sauter, Martin"],["dc.contributor.author","Taherdangkoo, Reza"],["dc.date.accessioned","2019-07-17T09:27:05Z"],["dc.date.available","2019-07-17T09:27:05Z"],["dc.date.issued","2017"],["dc.description.abstract","This study investigates the potential contamination as a result of hydraulic fracturing operations in the vicinity of a conductive fault zone. The general aim is to determine and rank key parameters with the most important influence on upward transport of fracking fluid from a hydrocarbon bearing formation (source) towards a shallow groundwater aquifer (receptor) via a conductive fault (pathway). Simulation results show that the fault zone properties and hydraulic fracturing pressure strongly dominate fluid transport towards the aquifer horizon. Nevertheless, the probability of contamination of shallow groundwater from fracturing fluid migration is low."],["dc.identifier.doi","10.1016/j.egypro.2017.08.093"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/61678"],["dc.language.iso","en"],["dc.relation.issn","1876-6102"],["dc.title","Numerical investigations of upward migration of fracking fluid along a fault zone during and after stimulation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]