Show simple item record

dc.contributor.authorRuehlicke, Bernd
dc.contributor.authorCarter, Matthew J.
dc.contributor.authorOttesen, Christian G.
dc.date.accessioned2019-12-10T21:16:44Z
dc.date.available2019-12-10T21:16:44Z
dc.date.issued2019-07-19
dc.identifier.citationRuehlicke, B., Carter, M. J., & Ottesen, C. G. (2019). The statistical eigenvector analysis technique (SEAT) for dip data analysis doi:https://doi.org/10.1016/j.marpetgeo.2019.07.027en_US
dc.identifier.issn0264-8172
dc.identifier.issn1873-4073
dc.identifier.urihttps://dspace.allegheny.edu/handle/10456/50163
dc.description.abstractDip data acquired from image logs (i.e. the dip/azimuth of planar features) are extensively used in the oil and gas industry to assist in interpreting subsurface geology. Gradual and/or abrupt variations in dip data with depth may indicate different structural features (e.g. faults, folds, etc.) or changes to sedimentary systems (e.g. unconformity). The statistical eigenvector analysis technique (SEAT) and accompanying computer application are useful in statistically calculating the orientation and geometry of subsurface structures from dip data. Poles to planes on a spherical projection over a given depth interval may reveal cluster or girdle distributions, which can be interactively analyzed using statistical distribution functions on the unit sphere. SEAT provides a mathematical solution for as few as two data points, and has the potential to be applied to dip data at the meter-scale. The orientation and geometry of various subsurface geological features (e.g. folds, fault drag deformation, etc.) can be interactively calculated over a chosen depth interval. SEAT is applied to a dip dataset acquired from a modern, 8-pad electronic borehole image log and a previously studied dipmeter data from the discovery well of the Railroad Gap Field to illustrate its higher accuracy in determining the orientation and geometry of subsurface structures compared to older methods. SEAT allows for a vastly improved interpretation of subsurface geological features when combined with high-resolution borehole image logs and accurate dip data. Improved understanding of subsurface geometries may allow for better business decisions in future exploration and appraisal of existing assets.en_US
dc.description.sponsorshipDip data from the discovery well of the Railroad Gap Field was made available by the Chevron Corporation.en_US
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.relation.ispartofMarine and Petroleum Geologyen_US
dc.relation.isversionofhttps://www.sciencedirect.com/science/article/pii/S026481721930337X#!en_US
dc.rights© 2019 Elsevier Ltd. All rights reserved.en_US
dc.subjectBorehole image logen_US
dc.subjectDipmeteren_US
dc.subjectRailroad Gap Fielden_US
dc.subjectStatistical curvature analysis technique (SCAT)en_US
dc.subjectSubsurface geologyen_US
dc.subjectEigenvectoren_US
dc.subjectAxesen_US
dc.subjectJavaen_US
dc.titleThe statistical eigenvector analysis technique (SEAT) for dip data analysisen_US
dc.description.versionPublished articleen_US
dc.contributor.departmentGeologyen_US
dc.citation.volume110en_US
dc.citation.spage856en_US
dc.citation.epage870en_US
dc.identifier.doi10.1016/j.marpetgeo.2019.07.027
dc.contributor.avlauthorCarter, Matthew J.


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record