MRCSP Phase I Geologic Characterization Report - Midwest ...

22 CHARACTERIZATION OF GEOLOGIC SEQUESTRATION OPPORTUNITIES IN THE MRCSP REGION
industrial-waste injection well, 2) Class II—brine injection well,
and 3) Class III—solution mining well. Locating all of these wells
had never been accomplished before by all of the MRCSP project
members; this information is usually kept by state or federal regulatory
agencies. However, information about these wells, especially
the Class I and II wells (Figure 13) will be crucial in understanding
the injection characteristics of many of the target formations under
consideration. Therefore, under Phase II of the MRCSP Partnership,
the geologic team will obtain as much information as possible from
these injection operations.
Salinity Grid
A salinity grid can be generated from mapping, either by direct
interpolation (Kriging etc.) or by exploiting the general relationship
of salinity increasing with depth. Mapping salinity accurately
in this region is difficult because the data needed are not routinely
gathered and submitted to state agencies; therefore the coverage is
sparse. For example, the Mount Simon Sandstone has only 18 measurements
of salinity scattered across the MRCSP area. In addition,
formation waters are continuously modified by filtration through
clay membranes, ion exchange reactions, precipitation of minerals,
and by the solutioning of the surrounding rocks (Blatt and others,
1980), causing further uncertainty. For these reasons, a statistical
salinity verses depth model was used to create the salinity grids
used in capacity calculations for this investigation. The model was
constructed from existing sample data using least-squares regression.
Individual models were created for each formation and used
with the overburden (depth) maps to make a continuous salinity
grid for each formation.
Geothermal Gradient and Temperature
Models of the surface temperature and geothermal gradient were
created to calculate the temperature at depth for use in the capacity
calculations. For the surface temperature, the thirty-year average
for over 275 cities was obtained for the conterminous United States
(NOAA, 2000). The temperatures were interpolated into a grid using
a minimum curvature algorithm.
For the geothermal gradient, a number of datasets were investigated.
These datasets included the American Association of
Petroleum Geologists (AAPG) bottom-hole temperature dataset
(AAPG, 1994), the Southern Methodist University (SMU) dataset
(Blackwell and Richards, 2004a), and the 2004 AAPG heat flow
dataset (Blackwell and Richards, 2004b). Each dataset was evaluated
for data quality and spatial distribution. The AAPG heat flow
dataset (Blackwell and Richards, 2004b) was not used because the
data distribution was considered too sparse in the project area—only
three heat flow measurements were for Ohio. The 1994 AAPG geothermal
dataset was unsatisfactory because it was uncorrected for
thermal equilibrium and, when analyzed using spatial statistics, the
spatial variance was quite large. Of those evaluated, the SMU dataset
(Blackwell and Richards, 2004a) was the best for this project because
it combined a good combination of data coverage and quality.
A regional correction was applied, which significantly reduced the
spatial variance. In areas where the SMU dataset was missing data,
such as Pennsylvania, data from the AAPG bottom hole temperature
dataset (AAPG, 1994) was used to augment the SMU dataset. The
augmented SMU dataset was used to create the geothermal gradient
grid for the region using kriging in Geostatistical Analyst.
Screening Maps
The large number of maps, data grids, and calculations generated in
this regional assessment make it difficult for the public, or any other
user, to interpret the various attributes related to CO 2 sequestration
in geologic units in the MRCSP study area.. Therefore, the geologic
team has devising several methods to condense the various types of
information contained herein into a smaller number of summary maps
for quick reference, by both technical and non-technical audiences.
Several techniques for creating summary maps were investigated.
Approaches ranging from complex expert systems models, which
codify qualitative geological knowledge numerical algorithms, to
simple screening maps. Because the expert systems models rely
on so much soft information (knowledge rather than data), it was
decided, at this stage in the project, that simple Boolean screening
maps were the best approach to presenting meaningful summaries.
Quantifying geologic knowledge through expert systems approaches
must be done with care and can be time consuming if realistic
algorithms are to be developed. Research into more advanced techniques
will continue in Phase II.
A screening/planning map was produced using grids for all deep
saline formations. Structure and isopach grids were reclassified into
binary grids showing where the geology was appropriate and inappropriate
for CO 2 injection, then reclassified to show areas where
overburden thickness was greater than 3,000 feet (using the 2,500-
foot rule of thumb for miscible injection, with 500 feet added to
account for potential map error). Isopach grids were reclassified to
show thicknesses greater than 50 feet. The reclassified grids were
recombined into a single grid showing the number of appropriate
targets and the name of the targets (Figure 14). This map can
also be viewed as a 3-dimensional scene (Figure 15). The map is
presented herein and will be discussed further with various stakeholder
groups, including the partnership sponsors, to elicit input on
its usefulness, clarity, and how it can be improved and added-to for
development in Phase II.
DATA STORAGE AND DISTRIBUTION
Geologic data for this project is provided in both digital and as
hard copy (paper) map formats. This was done to ensure that the
needs of a wide range of stakeholders were met. The approach
allows information to be distributed to individuals ranging from
sophisticated GIS modelers to non-technical users who just need a
map for a planning meeting.
Data Storage
All GIS data is being stored in a centralized ArcSDE database
maintained by the Ohio Division of Geological Survey. For geologic
target and confining layers, there are contour and grid data,
geologic unit crop lines, and fault locations stored. Point data used
in mapping are stored as a database containing all formation tops
with a listing of basic well-header data (i.e., well operator, location,
producing formation, well status, etc.). The database also contains
all GIS layers created in this project, including layers from the terrestrial
studies, CO 2 sources, surface digital-elevation model, oil
and gas fields, and the various data and grids needed for capacity
calculations. The database may be queried to obtain data for an
individual geologic layer, by formation, depth, location, or any
combination the user requires.