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Evaluating the impact of deep-water channel architecture on the probability of correct facies classification using 3D synthetic seismic data

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dc.contributor.authorLangenkamp, Teresa Rose, author
dc.contributor.authorStright, Lisa, advisor
dc.contributor.authorHarry, Dennis, committee member
dc.contributor.authorEykholt, Richard, committee member
dc.date.accessioned2022-01-07T11:29:19Z
dc.date.available2022-01-07T11:29:19Z
dc.date.issued2021
dc.description.abstractModeling studies of bed-to geobody-scale architecture in deep-water channel deposits reveal that channel element stacking patterns and internal architecture strongly control connectivity. This architecture is critical to understanding hydrocarbon flow and recovery but is unresolvable in exploration-scale seismic-reflection profiles. Forward seismic reflectivity modeling of a digital outcrop models is commonly used to explore how depositional architecture is interpretable in a filtered seismic response. One limitation of forward seismic reflectivity modeling studies is that they often stop short of qualitatively assessing the link between underlying depositional architecture and seismic response. This study addresses the gap between qualitative interpretation and quantitative evaluation by calculating the prediction reliability of inverted seismic data. Specifically, this study uses synthetic 3D seismic modeling and inversion of a 3D outcrop model of deepwater channels in the Tres Pasos Formation of the Magallanes Basin of southern Chile. The model includes outcrop- (bed and geobody) to seismic- (reservoir to basin) scale architecture. The primary objective is to quantify where and when channel architecture is accurately predicted by seismic facies classification. Bayesian classification is used to test the probability of correct facies classification from P-impedance and if the classification results are dependent upon architectural styles (e.g., channel element stacking patterns). Model sensitivity variables include seismic frequency (ranging from 15 to 180 Hz) and deep versus shallow rock properties. Results show that prediction reliability increased for both channel element axis sandstone and mass transport deposits with increasing frequency. Deep reservoirs or faster seismic velocities more accurately predict facies than shallow reservoirs or slower seismic velocities due to the increasing contrast between sandstone and shale velocities. Channel axis sandstone is less easily interpreted where channel elements are vertically aggraded, reducing acoustic impedance contrasts with background shale. When channel elements are laterally stacked or disorganized, facies can be predicted from seismic attributes with a higher confidence, due to a strong contrast between channel element sandstone and background shale. This study highlights that architectural information strongly impacts 3D inverted seismic data and highlights conditions that either hinder or aid accurate interpretation from facies classification.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierLangenkamp_colostate_0053N_16984.pdf
dc.identifier.urihttps://hdl.handle.net/10217/234211
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.titleEvaluating the impact of deep-water channel architecture on the probability of correct facies classification using 3D synthetic seismic data
dc.typeText
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineGeosciences
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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