1. 2nd International Workshop in “Geoenvironment and
Geotechnics”, September 2008, Milos island, Greece
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Developing computational groundwater monitoring and management system for Estonian oil shale deposit
H. Lind, K. Robam and I. Valgma
Tallinn University of Technology
K. Sokman
Estonian Oil Shale Company
ABSTRACT
Mining in Estonian oil shale deposit mainly
takes place in the Ordovician, Keila-Kukruse
aquifer. The aquifer is affected by mining and is
fully dried out around the working mine areas
(Perens and Savitski, 2008). The groundwater
level is decreased down to 30 m, to the mine
floor elevation using about 30 pumping stations
(Lind, 2005). The pumping rate is very high depending on the season, ranging from 10 up to
40 m3 per ton of produced oil shale (Reinsalu
et al., 2006). The Estonian oil Shale Company
mined 15.5 million tons of oil shale in 2007
(Source: Estonian Oil Shale Company). In order
to predict the effects, and avoid the social and
environmental impacts, there is a need to continuously monitor the situation and run software
simulations aiming at decreasing the pumping
rate. This becomes even more important since
the environmental taxes on usage of groundwater resources increase every year. Therefore, it is
necessary to monitor groundwater level and
quality, and develop a groundwater model for
the overview of the situation during mine operations and also for a number of years after mine
closure.
1. INTRODUCTION
The aim of the research is to develop and reorganize monitored groundwater data by Estonian
Oil Shale Company and build a dynamic
groundwater model in order to create a sustainable groundwater monitoring and management
system. The monitored data is used as input data
for the hydrogeological modeling using the Visual ModFlow Professional software for the ac-
tive and prospective mining areas. The goal for
the model is to generate descriptive three and
two dimensional dynamic water table maps depicting hydrogeological conditions, in order to
provide information regarding the changes of
the water dynamics (i.e. from the graphical
maps of water flow directions), as well as diagrams and reports of water in- and outflow.
Also information about the water exchange between mines is considered useful (Reinsalu and
Valgma, 2003; Reinsalu, 2005), as well as the
water income rate into working mines. Today
the monitoring system used by Oil Shale Company is not very flexible to analyze the situation
and to create a dynamic model; a MS Excel
worksheet with diagrams and a static model of
groundwater level was created previously. The
research project presented here will create a systematic database for developing a computational
groundwater model of the oil shale deposit for
sustainable management.
2. INPUT DATA FOR MODELING
In order to build up the groundwater model it is
necessary to have a lot of detailed input data in
a structured form. For a simplified model at
least information about the geological layers,
the hydraulic conductivity, observation wells,
pumping wells and boundary conditions is
needed. For importing the data into the Visual
ModFlow software some data processing is required. For the monitored water level data until
today simple MS Excel worksheets were used.
Currently collected data did not allow analysis
of the information and easy extraction of the
needed output for groundwater modeling.
A MS Access database was created in order

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2nd International Workshop in “Geoenvironment and
Geotechnics”, September 2008, Milos island, Greece
to record continuously monitored observation
well data in a structured form. Also MapInfo
and its addition Vertical Mapper are used to
generate background information. For inserting
already existing data, a comfortable layout was
developed to copy the information from MS Excel worksheet into MS Access database. New
observed records for a certain observation well
will be added using a form, as there is no need
to insert well number, information of aquifer
and geography. Each observation well in the database has a unique identification number to use
as a link in different query tables. Query tables
are used to extract only the needed information
from the main table; in addition, the monitored
information at certain time is added using queries as filling the main database table with observed information at different time periods
would be too complicated. Using query tables
the information needed for groundwater modeling with Visual ModFlow software can be easily
exported. Output can be given selectively by
monitored time, by aquifer and by geographical
well location. The database will be further developed to give seasonal average water level per
observation well as the groundwater level varies
seasonally.
The MS Access database together with
linked geographic data by MapInfo Professional
software (Fig. 1) allows visualizing the well location on a two dimensional map. All information added to the main table of the described database can be presented. MapInfo is used to
generate simple static groundwater level models, where the average water table elevations are
generated from linked database as the Visual
Modlow is rather inflexible to create simple
static models. In addition, the user can not easily change interpolated data that Visual Modflow generates based on measured data.
Figure 1: Map of oil shale deposit with observation well
database linked with Access database.
Figure 2: Groundwater model of Oil Shale deposit created
with Visual ModFlow.
3. BUILDING A GENERAL GROUNDWATER MODEL
Mathematical models have a key role in assessing the future behavior of a system to find effective operating conditions for sustainable development and management groundwater resources. Besides the importance of organizing
mining operations, advanced groundwater monitoring and modeling techniques are useful for
environmental impact assessments, while different infrastructure objects are planned to be build
nearby closed and waterfilled underground
mines.
The groundwater model of an oil shale deposit is under development using the Visual
ModFlow software. The developed model will
be dynamic; the data obtained through monitoring can be used to rerun the model and obtain
results in real-time. As the software takes into
account different parameters, including geological, hydrological and hydrogeological data, it is
necessary to restructure current databases and
collect the additional input data necessary for
the model. One of the difficulties when creating
the model is to collect and process the information needed for building and running the model.
The area of the model is 127 x 55 ≈7000 km2
which includes mined out and prospective areas
of oil shale (Fig. 2). As the model area is large,
the accuracy of the results will be low and,
hence, the output will be used for a general
overview of groundwater dynamics.
The created model has a grid size 100x100 m
and has five main layers - on top is the ground,
below it a 1 m thick soil layer, then limestone
and an oil shale layer. The bottom of the model
is a water impermeable layer (mainly the Uhaku
geological bed) (Fig. 3). For the grid layer information, previous research information cre-

3. 2nd International Workshop in “Geoenvironment and
Geotechnics”, September 2008, Milos island, Greece
Figure 3: Visualisation of the model layers – ground layer
and layer of oil shale.
ated with MapInfo Professional was utilized.
Today line information available in the
model includes rivers and streams, investigation
areas and mined out areas (properties and
boundary conditions for these areas can be
added later) (Fig. 4).
Also, observation wells have been added as
calibration data for the model (Fig. 5). While
adding the wells the following problem was encountered: the interpolated model domain was
smaller than the actual observation well depth.
Therefore not all wells are entered since the
software is rather inflexible to enlarge the model
boundaries.
As the area of oil shale deposit is more than
2700 km2 the model can include only the basics
for the general overview; when a detailed investigation is needed for a certain location a
smaller model can be developed (extracted)
from the larger model. As there is lack of data
for the area surrounding the oil shale deposit it
may be necessary to create inactive areas around
the deposit.
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Figure 5: Part of mined out and prospective area (Aidu
opencast), observation wells can be seen.
4. WATER QUALITY MANAGEMENT
When the mine will be closed down, the
groundwater level will increase within two to
four years up to the level as it was before the
economic activities started (Reinsalu et al.,
2006). Changing the groundwater regime during
and after mining will result in an increase in certain chemical components of the groundwater.
The 2004 data showed a higher rate of sulphate
content, total Fe, total oil products and total
phenol in the water sampled from the closed waterfilled - underground mine, which exceeded
the drinking water norm (Reinsalu et al., 2006;
Erg, 2005).
Therefore it is also necessary to monitor the
water quality in the closed mine and of the
pumped water from the mine which is settled in
settling basin before directing into nearby rivers
or dikes. It is also necessary to monitor seasonal
changes in the concentrations of chemicals in
the mine water. Hence, the groundwater quality
management program has been started in different parts of the oil shale deposit. The model of
water dynamics will be developed further for
managing groundwater quality.
5. FURTHER DEVELOPMENT
Figure 4: Inserted line information - rivers, lakes, prospective and mined out areas. Coloured background describes ground elevation.
Developing the management system the observed data should be gathered in a structured
form to insert the specified input data into the
groundwater model. To create a simplified
model the following data should be collected:
geological layers, hydraulic conductivity, observation wells, pumping wells and boundary
conditions. Further development of the model

4. 4
2nd International Workshop in “Geoenvironment and
Geotechnics”, September 2008, Milos island, Greece
foresees the creation of a database of pumping
wells to be inserted into the model. Pumping
wells will show the influence and changes on
the water level nearby the mine workings. Also
the model will give information to optimize
used pumping capacities and also reorganize the
location(s) of the pumping stations where it is
technically possible.
More detail information will be gathered regarding the hydraulic conductivity at certain
layers and areas and the parameters for some of
the boundary conditions of the model like river,
lake, amount of precipitation and aquifer
groups. After specifying the input data the situation of model should be calibrated which requires the equal values of observed and calculated water table values. This requires running
the software and analyzing the results several
times before receiving any results. While the
created model is very large and while it goes
more and more detailed there can be limits on
capacity of regular computer.
6. CONCLUSIONS
A groundwater management system in Estonian
oil shale deposit is necessary to be developed
further in order to understand the groundwater
dynamics and the changes in the concentrations
of potential pollutants (either primary or as a result of chemical reactions between water and
minerals, i.e. pyrite) in order to decrease and/or
avoid any negative impacts. Groundwater usage
is sustainable today, but it can become even
more efficient by a) decreasing the influence of
mining to people living nearby active mine areas whose drinking water wells could be dry or
polluted and b) avoiding any negative impacts
on environmentally protected areas. As the depression cone due to mining is very wide, influencing different environmentally protected areas, draining wells for drinking water etc, technological solutions like impearmable walls, infiltration dams, pumping water from mine back
to the area should be applied to keep the water
level. ModFlow helps to model this situation
and thus make the right decisions regarding the
application of these technological solutions.
Furthermore, it should be noted that if there
is a need to reach drinking water quality the
technical solutions are available, while the cost
and economical question should be considered.
Computational mathematical models can be
used to allocate the technical and environmental
constraints.
To conclude it should be mentioned that the
software package will be used to have a computational groundwater model to simulate conditions at technogenic mining areas. Today is important to predict the influence to the environment before the mine starts working. The computational groundwater management system can
be used while new mines are opened, current
mining is progressed or to overview the situation at closed down mines especially if nearby
building activity is needed. The output files
from the software can be used to visualize the
current and also the future conditions after geological changes by mining development. The
output data can be used for economical calculations as well.
The current project is done as part of the research “Conditions of sustainable mining”
ETF7499, whose scope is to use computer modeling to form the basics for sustainable, environmentally friendly mining.
REFERENCES
Reinsalu, E. and I. Valgma, 2003. Geotechnical Processes
in Closed Oil Shale Mines, Oil Shale, Tallinn: Estonian Academy Publishers, 398 - 403.
Reinsalu, E., 2005. Changes in Mine Dewatering After
the Closure of Exhausted Oil Shale Mines, Oil Shale,
Tallinn: Estonian Academy Publishers, 261 - 273.
Erg, K., 2005. Changes in groundwater sulphate content
in Estonian oil shale mining area. Oil Shale, 22 (3),
275-289.
Lind, H., 2005. The modelling of hydrogeological conditions. The case study of dewatering Tammiku Kose
surface mine, Thesis, Estonian National Library.
Reinsalu, E., I. Valgma, H. Lind and K. Sokman, 2006.
Technogenic water in closed oil shale mines, Tallinn:
Oil Shale, Estonian Academy Publishers Vol. 23.
Perens, R. and L. Savitski, 2008. Põlevkivi kaevandamise
mõju põhjaveele (in English: Oil Shale mining influence on groundwater). Keskkonnatehnika, 3/08, 4447.