Last
month we recognized how recent advances in the processing
of 3-D seismic data are providing valuable new tools for the imaging
of fracture properties between wells. In order to interpret this
data it is necessary to understand how fracture patterns influence
the seismic data, and to understand the types of fracture heterogeneity
that are likely to occur at this newly imageable scale.
This month
we discuss the interpretative steps needed to extract the complex
properties of natural fracture systems from the simple seismic fracture
models used during processing.
Discrete
Fracture Network (DFN) models have provided an important tool to
make the connection between seismic properties and reservoir.
Interpreting
Multiple Fracture Set Properties
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 1), 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:
- Attributes that sample
fracture orientation.
- Attributes that sample
fracture intensity.
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.
Anisotropy:
Fractures or No-Fractures
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.
The Next
Step: Calculating Permeability
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 2a 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
2b) 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
3a). 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 3b),
and a flow solution can be obtained that takes into account the
connectedness of the fracture system.
Figure
4 shows an example of a pressure pulse spreading through a fractured
reservoir in response to injection.
Seismic
+ Fractures = Permeability Prediction
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
5, 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.
Summary
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:
- First interpret the
connection between the seismic measurement and the naturally occurring
fracture sets.
- Connect the fracture
pattern to reservoir parameters through techniques such as DFN
modeling.
Although
uncertainties abound, these attributes provide new insight into
notoriously difficult reservoirs, and promise to enhance recovery
through focused engineering efforts.
Editor's
note: Bob Parney ([email protected], 303-831-0544) is with
Axis Geophysics, Denver, and Paul LaPointe is with Golder Associates,
Redmond, Wash.