MRCSP Phase I Geologic Characterization Report - Midwest ...

GEOLOGIC MAPPING PROCEDURES, DATA SOURCES AND METHODOLOGY
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ence on the accuracy of the final grids. The correlation coefficient
between cross-validation error and data density was 0.51 and the
coefficient between cross-validation error and the range was 0.39
(for these data, increased range was associated with increased
error). Data density and range were combined in a multi-variate
linear regression model that explained 81 percent of the variance
in cross-validation error and predicted the amount of error within
100 feet (RMSE). The maps could be improved by more well control,
especially in deep and faulted areas of the Rome trough and
Appalachian basin.
Comparisons between cross-validation error and gridding error
provided additional discernment on uncertainty issues. In general, if
the two error measurements showed good agreement, it confirmed
the gridding method was creating surfaces with error levels compatible
with those expected from direct gridding from kriging (block
kriging). However, the gridding error was much smaller for the
Cambrian basal sandstones structure map and showed the improved
fit of the hand-contoured map in the Rome trough area. Yet, such improvement
was not observed on other Lower Paleozoic maps. The
gridding error was much larger than the cross-validation error for
both the Oriskany and Medina structure maps, which were interpolated
using Petra. One possible interpretation was that the gridding
method (ANUDEM) found it difficult to fit the small closed-contour
features present on these maps.
Both the computer interpolation and final gridding routines were
expected to have difficulty in the faulted regions of the study area.
Faults violate the basic assumptions of kriging and are difficult
to represent in a grid. RMSE grid errors were compared between
the faulted areas and the rest of the basin. The faulted areas had
much larger errors in the Cambrian basal sandstones structure and
the Copper Ridge Dolomite isopach maps (Table 3). These layers
contained many wells that occurred directly on faults (the Cambrian
basal sandstones isopach was very thin in the faulted area and had a
small RMSE value). Otherwise, the magnitude of error was similar
for the two regions and the faulted areas did not consistently contain
increased error over the rest of the region (Table 3). However,
the user should be particularly cautious when using the maps in the
faulted regions of the Lower Paleozoic.
METHODOLOGIES FOR OTHER MAPS
Oil and Gas Fields Map
The mapping and compilation of state oil and gas fields maps
into one regional GIS layer for this project has greatly advanced
our ability to assess energy and sequestration resources at regional
and state scales. The map represents the first digital petroleum field
data for the states of Maryland, Michigan, Pennsylvania, and West
Virginia. Moreover, Michigan and Maryland were able to significantly
update their petroleum fields maps, and in Pennsylvania and
West Virginia, their current oil and gas field digitization projects
were completed as a result of the MRCSP project. Digital layers
from these states were combined with updated digital maps from
Indiana, Kentucky, and Ohio to make the first seamless regional
map and database of oil and gas fields. The resulting map/GIS layers
will have many uses for CO 2 sequestration, oil and gas exploration
and development, regional planning, general public education,
and uses by other sectors.
Methodologies used in creating and storing oil-and-gas-field
tabular data and field boundary maps differed widely from state to
state. The biggest challenge to making an integrated, regional map
was to conform the tabular field data from each state into a common
Table 3.—Comparison between uncertainty
in faulted and non-faulted areas
Mapping Unit
Faulted Area
(RMSE ft)
format. Ohio Division of Geological Survey personnel designed a
data structure that allowed tabular attributes to be populated with
data from each state (data tables can be found on the accompanying
GIS CD). The oil and gas fields database contains the basic
attributes necessary for the calculation of CO 2 sequestration potential
(average depth, porosity, thickness). The main challenge in
creating the system was assembling data from geologically similar
units into common regional plays. Common plays were developed
by combining geologic units of similar age and lithology using
the stratigraphic correlation chart created by the MRCSP team
as guidance (Figure 5). For instance, the “Clinton”/Medina play
map locally contains fields that produce from the Silurian “Clinton”
sandstone of Ohio (Cataract Group on Figure 5), the Medina
Group sands of Pennsylvania and the Tuscarora Sandstone of West
Virginia (see Figure A7-2).
The methods used to draw the oil-and-gas-field boundaries
(polygons) varied from state to state. The most common method
was to sort the well data by play or individual producing formation,
and draw the field boundaries by hand. Usually a buffer of less than
one-quarter to no more than one-half mile was used to define the
boundary near the outmost wells of a pool or field. Within larger
fields, holes will be found within the interior of the field polygon;
this is where dry holes are encountered, or where producing wells
have been drilled farther apart than the established minimum buffer.
Such hand-drawn maps existed as legacy data for most of the
states and were used as a starting point in Pennsylvania, Indiana,
West Virginia, Kentucky and Ohio—in these instances the field
boundaries were simply digitized and attributed. These new digital
maps can, and are, digitally updated as needed by automatic or
semi-automatic buffering methods (using a GIS package) when
new wells are drilled in Indiana, West Virginia, and Ohio. Field
maps for Michigan were made solely using GIS buffering of the
well locations for Phase I, but will be augmented by hand-digitizing
in the future. Field boundaries were merged into a common
GIS layer, but blending of oil-and-gas-field boundaries between the
states was not done. The individual state maps were compiled from
a variety of base maps that were at different scales (see metadata
in the oil-and-gas-fields layer on the accompanying GIS CD); users
should be cognizant of the accuracy differences from state to state
because of this.
Injection Wells
Rest of Basin
(RMSE ft)
Basal Cambrian Injection 754 297
Targets Structure
Basal Cambrian Injection 33 402
Targets Isopach
Copper Ridge Structure 359 401
Copper Ridge Isopach 1,141 332
Rose Run Structure 211 263
Rose Run Isopach 26 27
Knox Structure 130 185
The different injection-well types gathered for the MRCSP
region are categorized as follows: 1) Class I—hazardous and