Figure Captions
Figure 1. Index map of Southern United
States, showing the location of Appleton Field.
Figure 2. a. Geologic model (strike section)
of the Smackover Formation at the Appleton Field. Porous bindstones and
boundstones in the lower part of the Smackover developed preferentially
over exposed basement ridges, whereas the upper part of the formation is
essentially nonporous. The Smackover is typically non-porous throughout
its entire thickness away from the ridges. The siliciclastic Norphlet
Formation laps onto the flanks of the ridge beneath the Smackover. The
Buckner Anhydrite overlies the Smackover, and thins over the ridge
crest. A thick siliciclastic section of the Haynesville Formation caps
the zone of interest. b. Seismic model of the Appleton Field made by
convolving the geologic model with a zero phase 25 Hz Ricker wavelet.
2c. Arbitrary line through the 3-D seismic data so as to correspond
approximately to the geologic cross-section in Figure 2a.
Click here for sequence of Figure 2a, 2b, 2c.
Figure 3. Attribute -based map of the
thickness of the porosity zone in the area of the Appleton Field.
Contours in meters. Bottomhole location of well drilled following this
study shown by green dot.
Figure 4. Comparison of seismic attributes
derived from data shown in Figure 2c with
seismic attributes derived from model results shown in
Figure 2b. a. Average frequency between
the top and base of the Smackover from the data. b. Average reflection
strength between those same two markers from the data. c. Average
frequency between the top and base of the Smackover from the model
results. d. Average reflection strength between those same two markers
from the model results. In both cases, average frequency for the
Smackover is higher and average reflection strength is lower where the
porosity zone is present beneath the reef crest.
General Statement
There is an increasing interest in the use of
attributes derived from 3-D seismic data to define reservoir physical
properties such as presence and amount of porosity and fluid content.
This seismic -guided property prediction approach was popularized only
about five years ago, when it was demonstrated that log-derived
reservoir properties sometimes could be relatively easily correlated to
seismic attributes – and that the regression equations so derived could
be used to make predictions about those properties away from existing
wellbores.
Explosive growth in interest in this approach
has led to a proliferation of methods for refining it. Multiple
regression, geostatistics, neural networks and other approaches are
being explored to help correlate log and seismic data, and then to
distribute reservoir properties throughout the area of 3-D seismic
surveys. The predictions made by these methods are often appealing,
especially when presented in color. But how does one truly assess the
likelihood that any given prediction truly represents the subsurface
reality? Various statistically based methods have been developed to help
answer this question.
Using a procedure known as exclusion testing,
the interpretation team will use only a subset of the well database
during the project’s correlation phase, then test the physical
properties predictions against measurements from wells that were
excluded from the calibration phase. This procedure works well when
abundant well information is available, but it is not practical when
only a limited number of wells penetrate the target formation – such as
when the field is either small or at an early stage of development.
A two-pronged methodology for assessing the
results of a seismic -guided physical-properties prediction can be used
to reduce risk when only limited well control is available. The
methodology involves:
This approach is illustrated herein with an
example from the Jurassic Smackover Formation.
Appleton Field is a small field (840 aces, 13
wells, of which four were producing in 1997) in southwestern Alabama (Figure
1). Unlike other Smackover fields, where high energy shoal
carbonates are the primary productive intervals in the formation, here
the best production is from a dolomitized algal buildup that developed
over a paleobasement high located landward of the Jurassic shelf margin.
True vertical depth to the top of the formation in the Appleton
generally exceeds 3,800 meters (12,500 feet), and most wells are
deviated, to variable extents.
The data set for this project consisted of
wireline log information from 10 wells (deemed to be too few for
exclusion testing), production data and a 3-D seismic survey. The links
between geology and seismic response were evaluated by creating simple
2-D seismic models. The modeling began with the construction of geologic
models (e.g., Fig. 2a) that were convolved with a wavelet (chosen to
match the frequency and phase characteristics of the data) to generate
2-D synthetic seismic transects (Fig. 2b).
The model results showed that:
-
The reflection from the top
of the Smackover would combine with that from the overlying Buckner
Anhydrite to form a peak.
-
The porous part of the
Smackover in this field would be represented by a trough.
-
The base of the Smackover
would be a peak where the porous carbonates overlie basement rocks but
would change to a trough where non-porous carbonates overlie the
siliciclastic Norphlet Formation on the ridge’s flank.
Comparison of the model results to
corresponding transects through the seismic data (Fig.
2c; note that the
model result is noise free, whereas the data are somewhat noisy) allowed
the horizons of importance to be identified and mapped in the 3-D
volume. Structure maps derived from the 3-D data showed undrilled
structural culminations that were not apparent in previously published
maps. If these structures are porous, they could be infill targets since
existing wells would leave attic oil.
An empirical relationship was then sought
between seismic attributes and log properties that could be used to
predict the thickness of the porosity zone (defined by a 12 percent
porosity cut-off) away from existing wells. Over 30 seismic attributes –
a relatively small number compared to some studies – were derived and
analyzed. Multiple regression techniques established a polynomial
expression between the thickness of the porosity zone and three
attributes:
-
Average frequency of the
Smackover interval.
-
Average reflection strength
for that interval.
-
Isochron value between the
porosity zone seismic pick and the base of the formation (e.g., Figure
2).
Values for these three attributes – for the
entire 3-D survey area – were then input into the empirically derived
regression expression to generate a map of the thickness of the porosity
zone at the Appleton Field (Figure 3). The results suggested that
porosity is well developed beneath the structural culminations.
To help assess the validity of this
prediction, one of the initial 2-D seismic models was refined, and the
results were exported to a seismic interpretation package. From the
model results, it was possible to derive the same seismic attributes
that had been used in the physical properties calibration. The same
general trends seen in the 3-D data were visible in the model results (Figure
4), suggesting that the attributes selected for regression
analyses were responding to stratigraphic geometries.
Next, the porosity zone thickness map was
compared to what is known about the field’s geology. The best – and
thickest – porosity zones in the Appleton Field are considered to be
developed in algal boundstones on the crest of the paleostructure, where
our seismic predictions indicated. Other porosity predicted to be
present on the seaward flank of the buildups might be explained as
reef-front talus accumulations. Despite the porosity in the reef-front
areas, they are located below the oil water contact (as derived from
well control), and apparently lack updip closure.
A well drilled following this study (Figure
3) encountered 21 meters (69 feet) of porosity greater than 12 percent
at a location where the predicted thickness of this zone was 19 meters
(62 feet). The well tested 136 BOPD. This result is considered to be a
successful test of the physical properties prediction, although the
structural culmination was not as well developed as predicted. Ideally,
the new log information would be used to help update the existing
structure map and attribute correlation parameters.
Neither the seismic modeling component nor the geologic “reality check”
of this program is intrinsically new. The potential exists, however, for
these methods to be underutilized during field development, since so
much effort is focused on the process’ mathematical (statistical)
aspects. Although ideally all components of the process (statistics,
geology and geophysics) will be satisfied during an attribute -based
characterization program, risk can be reduced (not eliminated) where
limited well control exists through forward modeling and integration of
the geology. No matter how mathematically rigorous a physical properties
prediction might be – and no matter how many wells are available for the
study – it should be rejected if it is not both geologically and
geophysically plausible.
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