The statistical eigenvector analysis technique (SEAT) for dip data analysis

Abstract

Abstract Dip 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-padded 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.