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2001
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Photogeology and GIS in oil exploration in ecuador
Marcelo Echeverría
National Polytechnic University
Department of Oil studies and Natural Gas & CEPEPOL
P.O. Box 17-01-27-59, Quito, Ecuador
PHONE: 011-593-2- 507127 FAX: 011-593-2-449476
Email: mechever@mail.epn.edu.ec,
jmecheve@ecnet.ec
Wibold Jongsma
Department of Informatics and Computer Sciences
Unit for Artificial
Intelligence and Geographical Information Systems (UNISIG)
National Polytechnic University, Quito, Ecuador.
Veronica Echeverría CEPEPOL National Polytechnic University
Quito, Ecuador
Abstract:
Oil exploration in the Amazon basin of Ecuador helped us to generate geological, structural maps using GIS, air photos,
satellite and radar images. This tool and materials not only allowed a faster way of working but also permitted the
development of new experimental Geological lineaments Intersection Density Methodology (GIDM). The GIDM
permits to establish a correlation between areas of high geological lineaments intersection density and the possible
presence of petroleum fractured fields associated with these fractured zones.
Introduction
The petroleum exploration is considered an art in which
many geological variables are gathered, using expensive
and sophisticated methods. Afterwards, data is carefully
interpreted, to finally make predictions which are very
important, since the investments that have to be made for
an exploitation are really high, and often large parts of the
population depend on them.
The present study is motivated by the idea to develop a
method in which remote sensors are used in combination
with field data and geophysical measurements in order to
optimize future investments. In the petroleum exploration,
especially in remote areas, aerial photos, satellite and
radar images have played a mayor role to establish the
geological mapping foundation. However, the proposed
methodology has to be reviewed for its applicability
before the petroleum exploration can start and demands
are translated in petroleum barrels. In order to fulfill this
requirement, the method has been experienced on a pilot
area which will be described later on in this article.
Within this framework, the help of different petroleum
companies has been very useful, because in addition to
the contracted aerial photo interpretations, they
contributed with the experimentation and refinement of
the proposed method.
The main philosophy behind the methodology is that
when there are concentrations of geological lineament
intersections, then the geological structure is highly
fractured, and can possible serve as a oil reservoir.
Therefore the idea of the methodology is to make better
use of geological fractures, interpreted on remote sensing
imagines and translated into (photo) lineament maps.
Based on the geological lineament intersections (GDIM)
isolines are extracted with a certain density value,
converting it into a density map. According to this density
map, recommendations can be formulated to indicate
which geological structures are favorable for petroleum
exploration. The use of remote sensors within GDIM is
also very useful in areas with buried structures under
sediments because they let us visualize these structures by
its reflection due to the relative transparency of the
sediments.
Methodology
The methodology starts with the interpretation of the
linear geological structures on the remote sensing images.
Logically, the type of images used depends on its
availability and the available budget. In case radar or
optical satellite images are used, they have to be imported
into a GIS, georeferenced and, an optimal combination of
bands has to be established. In case of Landsat TM
images at least a combination of band 4 and 7 are
recommended (Lillesand & Kiefer, 2000). Due to its
scale, aerial photos are often preferred for photo
lineament studies, but for example in case of the
equatorian Amazon, they are not always available. On the
other hand, with optical satellite images it is very hard to
find images with very low cloud cover which allows a
successful interpretation. Finally, with radar images there
can be a problem with shadows formed by relief which
often interfere with geological structure reflection
patterns.
This problem occurs in the higher parts of the Amazon
basin. However, more towards the east, further down in
the Amazon basin, this problem is almost solved because
there is very little relief. Therefore, often a combination
of different types of images is preferred to complement
one another.
For the implementation of the GDIM the following steps
are proposed:
1. Preprocessing the remote sensing images in GIS
software package if necessary,
a) In case of satellite images, importing the
different bands separately, georeferencing the
images, select the study area, eventually apply
some enhancement procedure to improve the
quality of the image.
b) In case of aerial photos, scanning of the photos,
import the different photos, digitizing or
importing contour lines, interpolation of contour
lines into a Digital Elevation Model (DEM),
generation of a digital ortho photo using Ground
Control Points (GCP´s) and photo camera
parameters like focal length, generating a digital
ortho photo mosaic by joining the individual
digital ortho photos together.
This way a digital non-classified raster base map is
created.
2. Identify and outline geological lineament structures
through optical interpretation on the computer screen
or in case of aerial photos, direct on the photos and
afterwards, through onscreen digitizing, on the digital
ortho photo mosaic. This way a vectorial segment
map is created within the GIS.
3. Location of geological lineament structures
intersection points on the screen. With this step a
third vectorial point map layer is aggregated within
the GIS.
4. Overlaying a vectorial matrix with a certain grid size
(each grid cell forms a polygon) for which the density
of intersections points is calculated. (for example
number of intersections points per ha or km2). To
each grid cell the density value is assigned. For small
areas this procedure can be easily done manually,
however, for larger areas, with some simple
programming this procedure can be automated.
5. Assignment of the density value of each grid cell in
its center, this procedure generates a new point map
in which the points are equally distributed but several
points may have the value zero. Afterwards the
vector point layer is rasterized, using the same grid
cell as was chosen for the above-mentioned vector
matrix.
6. Interpolation of the raster image created in step 5,
which generates a Digital Terrain Model (DTM) with
a density value in each pixel for the whole study area.
7. If required a vectorial segment map can generated
through a raster-to-vector operation based on the
DTM in order to create an isoline density map. The
advantage of creating an isoline density segment map
is that it facilitates an easier visual interpretation.
Besides it can be overlayed with other map layer like
for example the raster base map.
Result of these steps are visualized in maps 2-4 at the end
of this article.
Logically, the GIS permit us to incorporate other thematic
layers or additional information within the non-spatial
database. Therefore this tool not only helps us to
automate the GDIMethodology but also assists in
establishing a (spatial) information base.
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