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Figure Captions
 Figure 1. A maximum and minimum direction of
seismic anisotropy are used to estimate  fracture orientation and
intensity. The geophysical model assumes a set of vertical fractures
with constant strike orientation. In this figure the velocity is slowed
by crossing the fractures so that the maximum velocity is parallel to
the fracture strike.
 Figure 2. Index map of Wind River Basin, Wyoming, with location of Circle
Ridge Field (after Keefer, 1969). Structure contours on top of Permian.
 Figure 3. Outline of structural geometry for
Circle Ridge Field looking from north to south.
Major fault surfaces are
color-coded and labeled. Overthrust block appears on the top surface and
is bounded by the Red Gully Fault. Compressionial shortening is higher
in the north and decreases to the south. As a result the northern part
of the field is fragmented into several imbricates.
 Figure 4. Magnitude and orientation of
fracturing in the overthrust block based on extensional strain
calculated through a three dimensional Palinspastic reconstruction.
Warmer colors indicate increased extensional strain and increased
fracture intensity. Fault block is bounded on southwest edge by Red
Gully Fault. Dot indicates location of well data.
 Figure 5. Outcrop of Jurassic Nugget
Sandstone, Circle Ridge Field, Wind River Reservation, Wyoming, looking
to the north. Lines above photograph are fracture traces from outcrop,
color-coded by set. Black fracture set is completely absent on east side
(right) of outcrop.
 Figure 6. Example of multiple fracture sets
and the resulting velocity anisotropy and predicted fracture
orientation.
 Figure 7. Examples of fracture networks that
would have similar seismic attributes over the volume delineated by the
square, but very different network permeability values.
The pattern in
Figure 7a is unconnected; fracture permeability would be zero. On the
other hand, the network shown in Figure 7b is well-connected, leading to
a permeable fracture network.
 Figure 8. Conversion of a DFN model of
fracturing into a finite element mesh for use in simulating flow and
transport through the fracture network. DFN models make it possible to
calculate the network permeability at any scale, and thus provide the
link between seismic attribute data and permeability values.
 Figure 9. Snapshot of pressure in the
fractures after injection. The colors indicate the pressure variations
in the network (Blue colors indicate high pressure, orange indicates low
pressure). Orange arrows show direction of flow out from the injector into
the fracture network.
 Figure 10. A DFN model with fractures curling
around the structure of a plunging anticline.
The cyan and blue colors
indicate higher permeability, and the magenta cells with lower fracture
permeability. Note the high permeability corridor set up along the crest
of the anticline.
The underlying theory behind the ANMO and AVAZ
processing is quite simple: Most geophysical processing algorithms
assume that all fractures are approximately vertical, and are locally
oriented in a single dominant direction (Figure 1). The maximum
detectable seismic effect is when the seismic raypath travels
perpendicular to the open fractures, crossing the slow velocity,
possibly fluid-filled, open fracture. A maximum and minimum direction of
fracture influence on P-wave and S-wave velocity can be determined and
used to indicate the dominant fracture orientation.
The difference between the maximum and minimum
effect gives some measure of the fracture intensity. This same process
can be applied in a number of data volumes where the change in Vp or Vs
as a function of azimuth is measured by the change in stacking
velocities (azimuthal NMO) or the change in reflection coefficients (azimuthal
AVO).
Fracture
Orientation in Rocky Mountains
A critical feature of recently processed AVAZ
and ANMO data volumes has been that the dominant fracture orientation
can change dramatically over short distances. Recent work on a project
sponsored by the U.S. Department of Energy (www.fracturedreservoirs.com)
shows that these changes are not only possible, but also highly likely
in a Rocky Mountain compressional setting where the stress field is
complex.
The Circle Ridge Field, in Wyoming’s Wind
River Reservation, Wind River Basin (Figure 2), was characterized
through a combination of 2-D cross-sections and 3-D structural
reconstructions based on well and surface data, and fracture data from
surface outcrops and subsurface image logs. The fracture and structural
data were supplemented with data from several transient well tests, a
bromide tracer test and a nitrogen injection test.
The structure is primarily determined by NE-SW
compression, which caused the formation of a series of imbricate fault
blocks along the Red Gully Fault, including several imbricates to the
north (Figure 3). The entire structure has been characterized as a
fault-breached, fault-propagation fold. Development of the structure is
likely to have produced the fracturing within the reservoir units.
Fracture development was predicted using strain calculated through a 3-D palinspastic reconstruction of the field.
Figure 4 shows differences in extensional
strain magnitude and orientation throughout a block of the Tensleep
Formation in the hanging wall of the field’s Red Gully Fault. The
contours and line lengths represent the magnitude of the maximum
extensional strain due to the initial folding of the reservoir
formations. The figure’s red lines represent the strike orientation of
extensional fractures that would develop perpendicular to the local
direction of maximum extensional strain. The red lines also show the
dominant set; it is likely that a secondary joint set perpendicular to
the set shown might also develop.
Ninety-degree changes in dominant fracture
orientation across fracture fairways seen in Figure 4 are consistent
with orientation patterns predicted by AVAZ data in nearby reservoirs.
These orientation variations arise due to inhomogeneities in the stress
field and the resulting fracture networks are consistent with well image
log and tracer data.
Similar changes in fracture orientation occur
in nearby outcrop at a much smaller scale (Figure 5). The black
fractures occur only on the left portion of the outcrop, nowhere else.
Red fractures dominate over blue fractures in the left portion, while
blue fracture intensity increases markedly on the right hand side.
Since seismic anisotropy can be influenced by
the presence of natural fractures – and that a high degree of
variability in fracture orientation and intensity is to be expected in a
Rocky Mountain compressional setting – interpretation of seismic data
requires a sound link with knowledge of the fracture geology in a
region.
The determination of fracture azimuth and
intensity is usually based on the assumption that there is a single
dominant fracture orientation, typically vertical. Frequently, fractures
occur in several sets with cross-cutting orientations (Figure 6), and
generally multiple sets are necessary in order to get well-connected
plumbing for long-term productivity in the absence of high matrix
permeability.
A number of attributes can be extracted from
the seismic data. They can be grouped into two major categories:
Orientation attributes such as the fast P or S
wave velocity azimuth were initially interpreted as the dominant
fracture orientation. In the case of multiple fracture sets, the
seismically sampled orientation is a function of the relative intensity
of each fracture set. The net effect of multiple sets appears to be an
average azimuth weighted toward the dominant set, although some data
appear to show the seismic azimuth switching from one set orientation to
another with no intermediate orientations apparent. For example, in an
area characterized by a single dominant regional fracture trend
orientation, any additional second fracture set may cause the attribute
to appear to rotate away from regional trend, although there is no
actual rotation of either of the fracture set orientations.
In the early development of anisotropic
seismic analysis it was thought that high levels of anisotropy, as
measured by the difference between the fast and slow P and S wave
velocities, indicated a high level of fracturing. It is becoming clear
that the influence of multiple fracture sets complicates the seismic
intensity measurements. For example, where fracturing is intense, the
seismic properties used to characterize orientation tend to become more
isotropic. Small variations in any one set can produce apparent
rotations of the interpreted fracture orientation. Isotropy in these
seismic properties also exists when fracture intensity is very low.
Thus, the magnitude of the anisotropy does not
in itself differentiate between regions of high fracture intensity and
low fracture intensity. Other attributes such as interval velocity must
be used to differentiate between an absence of fractures and an excess
of fractures.
Once the attributes of the natural fracture
system have been mapped, the next step is to take these attributes and
use them as a predictive tool. This process, however, is not as simple
as identifying fracture properties at a potential drilling location, as
it is the connectivity between the well and the fracture network that is
critical. Seismic attributes do not yet quantify any aspects of fracture
network connectivity. For example, in Figure 7a the same five fractures
occur in each of the two sample volumes, and would exhibit similar
seismic attributes. However, only the network on the right (Figure 7b)
would be conductive.
In order to assess the connectivity of a
reservoir, the next step after obtaining the fracture attributes from
the seismic data is to use DFN models to understand the consequences of
fracture orientation and intensity on permeability. The DFN approach
models fractures as two-dimensional polygonal planar objects, like
playing cards, located in three-dimensional space (Figure 8a). Each
fracture is characterized by its surface area and shape and has flow
properties such as permeability, compressibility and aperture.
Network models can be formed based on an
interpretation of seismic attribute data, engineering data, and image
log data as available. Once fractures are generated, a finite element
mesh can be constructed according to the fracture geometry (Figure 8b),
and a flow solution can be obtained that takes into account the
connectedness of the fracture system. Figure 9 shows an example of a
pressure pulse spreading through a fractured reservoir in response to
injection.
Discrete Fracture Network (DFN) models have
provided an important tool to make the connection between seismic
properties and reservoir. The DFN approach can be combined with seismic
attribute mapping by first developing an interpretation of the link
between attributes and fracturing. For example, the difference between
the fast and slow P or S velocities can be used to control the fracture
intensity of one fracture set within the DFN reservoir model, and the
rotation of the P or S fast velocity azimuth can control the generation
of the second fracture set.
Once the DFN model has been generated, a grid
can be placed over the model and a finite element mesh used to calculate
the potential volume of flow within each of the grid cells. In
Figure
10, a DFN model is displayed with a grid populated by fractures, with
the colors in each grid cell indicating the calculated permeability
values. In this case, a high permeability pathway has evolved along the
crest of the anticline due to the structural control of fracturing.
Recent advances in the processing of 3-D
seismic data are providing valuable new tools for quantifying fracture
properties between wells. In order to make use of this new information,
it is necessary to:
Although uncertainties abound, these
attributes provide new insight into notoriously difficult reservoirs,
and promise to enhance recovery through focused engineering efforts.
Keefer, W.R.,
1969, Geology of petroleum in Wind River Basin, Central Wyoming: AAPG
Bulletin, v. 53, p. 1839-1865.
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