{"id":{"repo_id":"wvu","oai_identifier":"oai:researchrepository.wvu.edu:etd-2623"},"canonical_url":"https://search.dev.ndltd.org/etd/wvu/oai:researchrepository.wvu.edu:etd-2623","repository":{"repo_id":"wvu","name":"West Virginia University","base_url":"https://researchrepository.wvu.edu/do/oai/"},"display":{"title":"Reservoir characterization using intelligent seismic inversion","abstract":"Integrating different types of data having different scales is the major challenge in reservoir characterization studies. Seismic data is among those different types of data, which is usually used by geoscientists for structural mapping of the subsurface and making interpretations of the reservoir's facies distribution. Yet, it has been a common aim of geoscientists to incorporate seismic data in high-resolution reservoir description through a process called seismic inversion.;In this study, an intelligent seismic inversion methodology is presented to achieve a desirable correlation between relatively low-frequency seismic signals, and the much higher frequency wireline-log data. Vertical seismic profile (VSP) is used as an intermediate step between the well logs and the surface seismic. Generalized regression neural network (GRNN) is used to build two correlation models between; (1) Surface seismic and VSP, (2) VSP and well logs both using synthetic seismic data, and real data taken from the Buffalo Valley Field.","abstract_html":"Integrating different types of data having different scales is the major challenge in reservoir characterization studies. Seismic data is among those different types of data, which is usually used by geoscientists for structural mapping of the subsurface and making interpretations of the reservoir's facies distribution. Yet, it has been a common aim of geoscientists to incorporate seismic data in high-resolution reservoir description through a process called seismic inversion.;In this study, an intelligent seismic inversion methodology is presented to achieve a desirable correlation between relatively low-frequency seismic signals, and the much higher frequency wireline-log data. Vertical seismic profile (VSP) is used as an intermediate step between the well logs and the surface seismic. Generalized regression neural network (GRNN) is used to build two correlation models between; (1) Surface seismic and VSP, (2) VSP and well logs both using synthetic seismic data, and real data taken from the Buffalo Valley Field.","abstract_has_math":false,"creators":["Artun, F. Emre"],"institution":null,"degree_name":"MS","degree_level":"Thesis","degree_discipline":"Petroleum and Natural Gas Engineering","degree_department":null,"school":null,"contributors":["Shahab Mohaghegh."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2005,"date_issued":"2005-08-01T07:00:00Z","date_published":"2005-08-01T07:00:00Z","updated_at":"2026-07-24T06:15:55Z","subjects":["Petroleum engineering","Geology","Geophysics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["https://researchrepository.wvu.edu/etd/1620"],"render_values":[{"text":"https://researchrepository.wvu.edu/etd/1620","href":"https://researchrepository.wvu.edu/etd/1620","code":true}]}]},"links":{"outbound_url":"https://doi.org/10.33915/etd.1620","outbound_label":"DOI","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shahab Mohaghegh."]},{"key":"dc:creator","label":"Author","values":["Artun, F. 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Seismic data is among those different types of data, which is usually used by geoscientists for structural mapping of the subsurface and making interpretations of the reservoir's facies distribution. Yet, it has been a common aim of geoscientists to incorporate seismic data in high-resolution reservoir description through a process called seismic inversion.;In this study, an intelligent seismic inversion methodology is presented to achieve a desirable correlation between relatively low-frequency seismic signals, and the much higher frequency wireline-log data. Vertical seismic profile (VSP) is used as an intermediate step between the well logs and the surface seismic. Generalized regression neural network (GRNN) is used to build two correlation models between; (1) Surface seismic and VSP, (2) VSP and well logs both using synthetic seismic data, and real data taken from the Buffalo Valley Field."]},{"key":"dc:title","label":"Title","values":["Reservoir characterization using intelligent seismic inversion"]}]}],"canonical_facts":{"dc:contributor":["Shahab Mohaghegh."],"dc:creator":["Artun, F. Emre"],"dc:date.available":["2019-01-17T08:00:00Z"],"dc:description.abstract":["Integrating different types of data having different scales is the major challenge in reservoir characterization studies. Seismic data is among those different types of data, which is usually used by geoscientists for structural mapping of the subsurface and making interpretations of the reservoir's facies distribution. Yet, it has been a common aim of geoscientists to incorporate seismic data in high-resolution reservoir description through a process called seismic inversion.;In this study, an intelligent seismic inversion methodology is presented to achieve a desirable correlation between relatively low-frequency seismic signals, and the much higher frequency wireline-log data. Vertical seismic profile (VSP) is used as an intermediate step between the well logs and the surface seismic. Generalized regression neural network (GRNN) is used to build two correlation models between; (1) Surface seismic and VSP, (2) VSP and well logs both using synthetic seismic data, and real data taken from the Buffalo Valley Field."],"dc:identifier":["https://doi.org/10.33915/etd.1620","https://researchrepository.wvu.edu/etd/1620"],"dc:subject":["Petroleum engineering","Geology","Geophysics"],"dc:title":["Reservoir characterization using intelligent seismic inversion"],"thesis:degree_discipline":["Petroleum and Natural Gas Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["MS"]},"updated_at":"2026-07-24T06:15:55Z"}