The
use of GIS 3D tools to tackle ground water contamination at
Younis Altobi and Danny Bailey
Introduction
As
societies expand, groundwater resources are becoming more and more of an
important asset. As a downside of expansion, groundwater resources are
experiencing many negative anthropogenic affects. One of the most prominent
anthropogenic affects is hydrocarbon contamination. Pipelines circulating
hydrocarbons, such as oil, stretch across the world either above or below the
surface. These pipelines leak at least once in their life span (http://www.epa.gov).
More than 14,000 oil-spills are reported to the US Environmental Protection
Agency each year (http://www.epa.gov).
Oil-spills
severely impact the environment and different habitat and resources. For
example, vegetation, marine and human species are primary causalities of
oil-spills along with natural and man-made resources, i.e. ground water.
However, the magnitude of their impact depends on the oil characteristics
(Hydrocarbon type; light vs. heavy) and the spreading rate at the spill site.
The clean-up process following an oil-spill can be challenging in terms of time
needed, cost in terms of training personals and equipment needed, and accurate
understanding of hydrocarbon distribution. The severity of the oil-spills
impact on resources increases with increasing clean-up time.
If the hydrocarbon front percolate through the overlying sediment and reach the water table, then large scale contamination treatment and distribution modeling is required. However, successful modeling of hydrocarbons plume within the ground water is a complex and challenging process. This study attempts to develop new monitoring and modeling techniques of oil-spill distribution with time by utilizing ArcGIS tools.
Study site
The
focus of this project will be a site investigation of a nationally famous
oil-contamination site northwest of
Figure 1a A map
showing location of the study site with respect to
Figure 1b A detailed aerial photograph of the study site showing the well locations and the different oil pools. The outlined box and the map to the right highlight the extent of the north oil pool and the surrounding wells. X is the location where the pipeline burst in 1979 (http://mn.water.usgs.gov/bemidji).
Objectives
As
a novice to the use and application of ArcGIS, I was amazed by its capabilities
and the usefulness of its applications. After choosing the study site, we
wanted to investigate surface and groundwater interaction and their effect of
the hydrocarbon plume migration. Using GIS techniques and utilizing Arc Hydro
Groundwater Data Models devised by Gil Strassberg and Suzanne Pierce (CRWR), we
proposed:
2-Dimentional surface-ground water interaction data
model using aquifer, stream networks, aquifer recharge and contaminant
transport.
3D geological framework of the aquifer using wells,
coring and test data.
Model groundwater movement through out the aquifer.
Model hydrocarbon migration since the 1979 oil spill
through out the aquifer.
Model the aquifer hydrologic unit to determine the
effects of lithology and porosity on water and hydrocarbon movements.
Produce a 3D distribution profile of the hydrocarbon
plume across the site.
However, as with any study, within the course of the investigation we had to change and modify our goals to fir with the data we had and timeframe. We also started to learn about the current limitations of ArcGIS and where it can be improved in order to apply it to ground water contamination monitoring and modeling (discussed later). Therefore, this study accomplished the following goals:
Produce a 3D geological framework of the aquifer using
water wells and coring.
Model hydrocarbon migration since the 1979 oil spill
through out the aquifer.
Produce a 3D distribution profile of the hydrocarbon
plume across the site.
Produce 3D surface topography of the site.
Model ground water level changes through time.
Model oil level, thickness, and concentration through time.
The goals were achieved using several ArcGIS programs. For example, all the 3D modeling and surface mapping was done using ArcScene. Data layout and projection were done using ArcMap, ArcToolbox and ArcCatalog. Microsoft Excel and Access were used to build the attribute tables needed for the applications above.
Data
All the data were acquired from the USGS Bemidji website (http://mn.water.usgs.gov/bemidji/) (Fig.2). The site contained information regarding maps, current and past research conducted in the oil-spill site, list of contact information of researchers working in the site, data, and results from various projects.
The data contained well information (more than 200 wells) regarding well number, (X, Y) locations, elevation, water level, oil level, oil thickness and oil concentrations. All the wells have well number and (X, Y) locations. However, the rest of the wells contained some of the remaining data with few exception that had all the above. Coring wells were also listed in the USGS site and those wells were primarily used to construct the geological framework. Regarding oil concentration, this study focused on modeling the Benzene and Toluene concentrations only.
Data Analysis
After selecting wells from and around the north oil pool, surface elevation data from a set of wells scattered across the area were collected in order to interpolate a land surface for the study area.
From the coring wells, 6 different lithologies were identified based on the core description provided with each well. Porosity and permeability values were then estimated from text-book values of similar lithologies. For each well, tables with top and bottom depth of each lithology along with lithoID were created. This is going to be used to build a 3D projection of wells into the subsurface.
As the study is focused on modeling hydrocarbon distribution through time, we selected time frames for the study. The time frames have a 5 year time span as changes in oil level take place at very low rate. Wells were then organized into the 3 time frames and the 3 different categories, water level, oil level and oil thickness, and oil concentration (Benzene and Toluene).
For each table, time frame, wellID , Latitude, Longitude, category elevation form surface elevation at that well, or oil concentration subdivisions were created.
Attribute table of the elevation wells and a sketch showing the location of the wells relative to the north oil pool (see fig.1b).
Part
of the attribute table that was used to construct the 3D projection of cores
within the study site. Top and bottom depth of each Lithology is calculated
with respect to surface elevation. Second table is core description and its
interpreted LithoID, porosity and permeability.
Oil level and thickness attribute table for period 1. Notice the number of wells and their wellID.
Part of the wells used to construct the attribute table for water level during period 2. Notice the number of wells and wellID.
Benzene and Toluene concentration attribute table for period 3. Notice the number of wells and wellID.
The number of wells varies for each period depending on the data available. Thats why different wells had to be used in order to have a large enough dataset for each time frame.
Data interpretation
- All the attribute tables were projected using geographic coordinate (NAD 1983) in ArcMap.
- The personal geodatabases for each of the time frame
(
- Each personal geodatabases contained several feature data sets (oil thickness and thickness, water level, oil concentration, elevation and lithology).
- Each feature dataset was then projected using ArcToolbox to projected coordinates, MN Central State Plane.
- Within each dataset, the relevant feature class was added from ArcMap and projected using MN Central State Plane.
- Each dataset was then added into ArcScene and the data were extruded from the elevation surface (layer properties/baseheight/elevation surface).
- All surfaces were created as raster using Inverse Distance interpolation.
A screen capture
of ArcCatalog showing the different personal geodatabases for each of the time
frame (
Results
Surface elevation
Figure
shows elevation Surface interpolated
from selected surface elevation points distributed across the study site (green
points indicate the location of the elevation points within the site.
3D projection of core lithologies
A 3D Surface elevation of the site with different wells and projected core lithologies. The different colors of the projected cores represent different lithologies.
Water level
1
2
3
Figure shows 3D water level surface for the 3 different time frames. Overall trend shows that the water level rises in period 2 and then drops again during period 3. The extent of the changes and its relation to precipitation data can be further investigated. Combing these surfaces with the oil level and thickness (see below) will highlight these changes.
Oil level and Thickness
1
2
3
Figure shows oil level (top surface) and thickness in the 3 time frames. Oil level drops with time and oil thickness decreases away from the spill site. The dark color indicates the high elevation of the oil and it lies directly below the spill site. The changes in oil level with time need to be further investigated to address the effects if any of water fluctuation.
1
Oil concentration
(Benzene and Toluene)
2
3
Figure
shows oil concentrations (Benzene; pink, and Toluene, purple) through the
different time frames. The high peak in benzene concentration lies right below
the spill site. With time, Toluene concentration is deceasing at spill site and
increase away (towards NE). The relation of this changes and water transport
has not been investigated in this study. However, assuming NE ground movement
direction from these diagrams can be that far off. The
to the surface.
3D cube of the site
Figure shows 3D cube of the site with land elevation surface, different wells across the site, projected cores, and oil level and thickness. The diagram is for period 2 which had better data in terms of the number of wells. Other cubes were also created for period 1 and 3.
Looking at the cube, everyone will agree that plotting the subsurface geology across the site and relating lithological characteristics (porosity and permeability) will help in understanding the oil migration and distribution with time.
Conclusions
ArcGIS tools such as ArcMap and ArcScene can play a very important role in monitoring and modeling oil-spill distribution through time. The study results show that even for a small site, if you have the data required, then ArcGIS will be a vital tool in understanding hydrocarbon plume migration and can help in site treatment and remediation. Linking surface waters to ground water can be done if you have the high resolution stream data required for small spill sites. Building geological models and combing them with porosity and permeability distributions across the site are not yet available in ArcGIS tools, but can be developed in the future. They will immensely help in providing the 3D view of the site with different surfaces and lithologies. Tracking analysis and forward modeling of plume migration will be the next step. The combined results will provide the EPA with distribution profiles for spill containment and treatment which will result in better resource management and huge time and money saving.
Limitations
During the course of conducting this study we had to overcome obstacles caused either by data or ArcGIS program limitations. The limitations that influenced this study are:
Time
Sorting
numerous attribute tables; different wells had different data and different
monitoring periods. We had to sort these attribute tables with wells that had
all the required data for that field and for the different time periods. Most
times these data had to be collected from different tables from the USGS
website.
Projection;
changing the projection for each feature dataset from Geographic to projected
using ArcToolbox and repeating the whole process for each feature set for the
specified period of time.
Area too
small for surface/subsurface water interaction.
Difficulty
to link stream network to ground water as most of the surface stream network
data is too large for the our study site (200x100 m). The size of our site did
limit the extent of the study, but the resolution of the stream data is also
too small. Developing high resolution stream and surface data will be extremely
useful for future studies concerning small sites.
Geological
framework.
Linking
different lithologies across the area using ArcScene is still not possible.
This is a program limitation as specific layers can be plotted and extruded to
a common depth in order to have any type of subsurface configuration. ArcScene
is not yet capable to plot real geological cross section with different
lithological changes between wells.
Interpreting
the spatial lithological distribution between wells is also an ArcScene
limitation. This is normally done by plotting fence diagrams and liking varying
lithologies together.
Tracking
analysis in ArcScene is not yet possible. Because of this we had to do three
separate timeframes.
Future Work
The study addressed the large scale problem and produced valid interpretations. However, a few questions need to be addressed in order to quantify the success of applying ArcGIS techniques in monitoring and modeling oil-spill contamination sites. These include;
Investigating
the porosity and permeability effects on hydrocarbon distribution and
migration. This will address the lithological variation impact on plume
transport which is needed to model movement direction and extent with time.
Creating
polygons for geological framework might help in viewing the subsurface geology
of the site and combined with the porosity and permeability data will help in
understanding 3D transport geometries.
Determining
the cause of oil level variations (i.e. induced by water table fluctuations, or
actual downward migration of oil level).
Acknowledgment
We
would like to thank Gil Strassberg (CRWR) for his time and help with ArcScene
and projection techniques. Gil also gave us some valuable feedback on our ideas
and data analysis methods. Dr David Maidment for his enthusiasm towards this
project and his ideas. Suzanne Pierce (UTDoGS) for useful discussion concerning
ArcScene applications. Geoff Delin and
Todd Anderson from the