DEVELOPMENT OF ANALYSIS TECHNIQUES TO EXTRACT
QUALITATIVE/QUANTITATIVE INFORMATION FROM THREE-DIMENSIONAL
DIGITAL OUTCROPS WITH APPLICATION TO THE PENNSYLVANIAN
DEEPWATER DEPOSITS AT BIG ROCK QUARRY, ARKANSAS
Publication No ____
Mariana Iulia Olariu, Ph.D.
The University of Texas at Dallas, 2007
Supervising Professor: Carlos L. V. Aiken
This study makes effective use of the qualitative (facies distribution) and
quantitative (bed/channel dimensions) information incorporated in threedimensional virtual outcrops and together with other techniques such as groundbased remote sensing and three-dimensional analysis of terrain surfaces of the
outcrop provides new tools for geologic mapping and interpretation. The
deepwater deposits that crop out at Big Rock Quarry, Arkansas are used as a
test site to illustrate the potential of this approach, especially in highly
inaccessible areas that can benefit from remotely acquired information about
lithology.
Three-dimensional photo real mapping techniques have been recently
developed as effective tools for detailed outcrop studies, but still their potential
has not been fully exploited. Examination of the virtual model of an outcrop
allows the extraction of three-dimensional information, as well as geometries and
orientations which allow the accurate analysis of geology. The photo real outcrop
of the exposure at Big Rock with assigned lithologies is used to reconstruct the
bedding surfaces by interpolation between differently oriented exposures. The
three-dimensional model of sedimentary bodies allowed capturing the threedimensional spatial distribution of lithological units, which is fundamentally
important for understanding the internal architecture of erosional and depositional
features, in this case channelized features.
Multi-spectral analysis has been successfully used from space and
airborne platforms, but in this study it is used to remotely sense lithology at
outcrop scale, obliquely at close range with an increased spatial and spectral
resolution. Local outcrop level geology until recently has remained basically nondigital. The methods that have been developed define geometries, but what is
lacking is lithology. This multispectral approach provides this important aspect of
digital geologic mapping beyond the visible range.
Analyses of the spectral characteristics of sandstone and shale samples
from the outcrop at Big Rock within the visible and infrared portion of the
electromagnetic spectrum has identified spectral responses of these sedimentary
rocks. Multi-spectral images have been acquired using a thermal camera for the
infrared part of the spectrum and a conventional digital camera for the visible
range. Spectral images obtained from the outcrop are co-registered and
displayed in Red-Green-Blue color space to create false color images that are
useful for highlighting lithologic variation.
By developing a surface classification algorithm for fracture identification
and orientation it was possible to evaluate the fracture geometry, which is helpful
in interpretation of reservoir quality. High-density, high-accuracy terrestrial laser
scanners have been use to capture the surface morphology of the outcrop at Big
Rock. Visualization and analysis of the centimeter-resolution terrain data
provided a characterization of this fracture system.
Conventional outcrop information combined with new techniques such as
three-dimensional detailed geologic mapping, ground-based multispectral
imaging and 3-D analysis of terrain surfaces of the outcrop can add valuable
information for a better description and interpretation of deepwater sedimentary
successions in particular, but geology in general.
CHAPTER 1
INTRODUCTION
1.1 Overview
This dissertation comprises three papers (Chapters 2, 3, and 4) which
highlight the usefulness of integrating different techniques for a more
comprehensive understanding of the outcrop geology.
Deep-sea sediments have received considerable interest both for
research
purposes
and
economic
reasons
due
to
large
hydrocarbon
accumulations associated with turbidite deposits (Bouma et al., 2000). Ideally,
characterization
of
hydrocarbon
reservoirs
requires
information
about
heterogeneity at a submeter scale in three dimensions. Typically surface (wells
and wire logs) data provide very detailed, but much localized geologic
information while subsurface (2-D and 3-D seismic) data have limited vertical
resolution. One solution is to characterize shale-sandstone distribution using data
from outcrops (Coleman et al., 2000) since large, continuous 3-D exposures
provide quantitative lateral and vertical attributes of strata and their bounding
surfaces (Slatt, 2000). However, most of the time geologic interpretation is made
on 2-D photo panels of outcrops which are affected by distortions and parallax
effects. Interpreted measurements may also be influenced by the shape and
orientation of the exposure (Pringle et al., 2001). Recently developed threedimensional photo real mapping techniques (Xu et al., 1999; Thurmond et al.,
2000; Xu, 2000) are effective tools for detailed and accurate quantitative outcrop
studies.
This project proposes to study the 3-D geometry of the sedimentary
bodies and facies distribution, as well as channel morphology of the turbidite
deposits that crop out at Big Rock Quarry, Arkansas. Outcrop sedimentological
information combined with new techniques such as mapping geology in threedimensions on the virtual outcrop, facies discrimination based on ground-based,
close range, oblique infrared photography and three-dimensional analysis of
terrain surfaces of the outcrop provided new tools for quantitative mapping of
sedimentary facies and interpretation. Making effective use of the 3-D methods
developed in this study we were able to evaluate the potential improvement over
classical 2-D methods that have been used to build reservoir analog data bases.
Big Rock outcrop belt exposes a three-dimensional view of the lower part
of the upper Jackfork Group (Jordan et al., 1993) along the north bank of the
Arkansas River in North Little Rock, Arkansas. In the study area Jackfork Group
is divided in lower Jackfork (Irons Fork Mountain Fm.) and upper Jackfork
(Brushy Knob Fm.). The Jackfork Group was dated on the base of correlative
units on the shelf and it is Pennsylvanian (Morrowan) in age.
The horse-shoe shape of the quarry provides an oblique strike view
toward southeast and an oblique dip view toward northwest. The quarry walls are
up to 60 m high and about 1200 m long. Excellent cliff faces exposed at Big Rock
were interpreted as proximal deep-water fans within slope channels canyons
(Jordan et al., 1993), but the geometry of these deposits was difficult to be
interpreted due to highly variability of bed thickness and lateral discontinuities in
three dimensions.
The first manuscript effectively uses 3-D qualitative (lithology, lateral
continuity of sedimentary bodies, channel morphology) and quantitative (bed and
channel dimensions) information contained in the virtual outcrop at Big Rock to
reconstruct the submarine channel complex architecture. Oblique close-in
photography acquired with a digital camera is integrated with terrain data and
converted into a 3-D digital photo real model of the outcrop. Digital mapping or
photo real mapping techniques were developed at UT at Dallas for outcrop
studies (Xu et al., 1999; Xu, 2000; Xu et al., 2000). A real time kinematic (RTK) –
global positioning system (GPS) combined with a robotic reflectorless laser
rangefinder captured 3-D terrain data of the outcrop in global coordinates within
centimeter accuracy. Digital photography was taken at the same time for a 3-D
photo real model.
Examination of the digital model of the outcrop with assigned lithologies
allows for extraction of 3-D accurate geometric information and geologic
interpretation. Key stratigraphic features such as bed/channel boundaries are
highlighted on the 3-D photo real model making possible correlation of
strata/channels exposed on the two distinct sides of the quarry. Reconstruction of
body geometry was accurate in areas where lithologies were highly distinct and
where surfaces/bodies can be correlated on the two walls of the quarry.
The second manuscript uses multispectral imaging to assess how well
sandstone may be detected versus shale in a deep-water sedimentary
succession. Remote sensing analysis for lithologic mapping is common, but here
it is applied to digital acquisition from the ground at close range, obliquely to
demonstrate its use in detailed outcrop mapping. Using a combination of visible
spectrum (red, green, and blue bands) and a wide thermal infrared band (2-14
μm) false color images are created that are useful for highlighting lithological
units on outcrop. The thermal channel has been used because surface
temperature distribution may contain relevant information about rock distribution
in the scene and also because the three visible bands are highly correlated to
each other. Discrimination of different lithologies based on the spectral
characteristics has been made using image processing techniques, such as
principal component analysis.
The third manuscript develops an automated and objective method for
fracture identification with applicability especially in inaccessible outcrops.
Determination of fracture orientation and density can be an important aspect of
structural analysis and reservoir characterization. Typically a detailed survey of
fractures in outcrop is done by visual observation. This technique, although
precise, is labor and time intensive and hampered by the limited amount of data
obtained especially from large exposures to be used for statistical analysis. Our
surface classification algorithm provides an easy and automated way to quantify
fracture information at outcrop scale.
The exposure at Big Rock has a blocky aspect due to preferential
weathering on the fracture planes. Orientation of fractures in outcrop is
determined based on their geometry. High-density terrestrial laser scanners
proved very useful to capture the 3-D surface morphology of the outcrop and the
exposed fracture surfaces. These automatic, fast, high-accuracy laser scanner
systems produced enormous volumes of point cloud data at centimeter
resolution. An unsupervised pattern classification technique was developed that
can extract surface orientation information directly from the point cloud. Analysis
of the high quality, high resolution three-dimensional terrain data indicated how
fractures are distributed in the rock volume.
Combining 3-D digital lithologic mapping with multispectral mapping and
three-dimensional analysis of terrain surfaces of the outcrop at centimeter detail
the amount of information is increased tremendously and the exposed variation
in sand and shale can be considered in modeling the flow.