Print this page
Click to view article in PDF format.
GC3-D
Seismic 
Data
in Imaging Fracture Properties for Reservoir Development*
By
Bob Parney1 and Paul LaPointe2
Search and Discovery Article #40098 (2003)
*Adapted for online presentation from the Geophysical Corner column in AAPG Explorer October and November, 2002, entitled “Fractures Can Come Into Focus”and “Simple Seismic, Complex Fractures,” respectively, and prepared by the authors. Appreciation is expressed to the authors, to R. Randy Ray, Chairman of the AAPG Geophysical Integration Committee, and to Larry Nation, AAPG Communications Director, for their support of this online version.
1Axis Geophysics, Denver, Colorado ([email protected])
2Golder Associates, Redmond, Washington ([email protected])
It has long been recognized that the presence of
naturally occurring fracture networks can lead to unpredictable heterogeneity
within many reservoirs. Conversely, fractures provide high permeability pathways
that can be exploited to extract reserves stored in otherwise low permeability
matrix rock. One of the primary difficulties in managing fracture heterogeneity
and the consequent uncertainty is that production rates and volumes are
controlled by fracture network connectivity between the producing wells, while
the primary sources of data on fracture properties are measured only in the
vicinity of wells. In some ways this is like trying to predict the size of a
schoolyard by close examination of a single link in the surrounding fence.
Recent advances in the processing of 3-D seismic data,
however, are providing valuable new tools for the imaging of fracture properties
between wells. Those tools are the analysis of seismic velocities as affected by
raypath direction and offset distance. Specifically, adjusting velocities as a
function of azimuth (velocity anisotropy) to improve reflection imaging has
produced by-product data volumes of seismic velocity anisotropy (ANMO) and
improved data volumes of azimuthal changes in amplitude as a function of offset
(AVAZ).
These seismic advances raise the following questions:
-
Geologically, what should this newly imageable level of fracture heterogeneity look like?
-
How do we then make the link between fracture properties and reservoir performance?
|
uTheory of seismic response to fractures uFracture orientation in Rocky Mountains uInterpreting multiple fracture set properties
uTheory of seismic response to fractures uFracture orientation in Rocky Mountains uInterpreting multiple fracture set properties
uTheory of seismic response to fractures uFracture orientation in Rocky Mountains uInterpreting multiple fracture set properties
uTheory of seismic response to fractures uFracture orientation in Rocky Mountains uInterpreting multiple fracture set properties
uTheory of seismic response to fractures uFracture orientation in Rocky Mountains uInterpreting multiple fracture set properties
uTheory of seismic response to fractures uFracture orientation in Rocky Mountains uInterpreting multiple fracture set properties
|
Theory of Seismic Response To FracturesThe 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
Fracture Orientation in Rocky Mountains
A critical feature of recently processed AVAZ
and ANMO
The Circle Ridge
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
Figure 4 shows differences in extensional
strain magnitude and orientation throughout a block of the Tensleep
Formation in the hanging wall of the
Ninety-degree changes in dominant fracture
orientation across fracture fairways seen in Figure 4 are consistent
with orientation patterns predicted by AVAZ 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
Interpreting Multiple Fracture Set PropertiesThe 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
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
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 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
In order to assess the connectivity of a
reservoir, the next step after obtaining the fracture attributes from
the seismic
Network models can be formed based on an
interpretation of seismic attribute
Seismic + Fractures = Permeability Prediction
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 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.
Summary
Recent advances in the processing of 3-D
seismic
Although uncertainties abound, these attributes provide new insight into notoriously difficult reservoirs, and promise to enhance recovery through focused engineering efforts.
ReferenceKeefer, W.R., 1969, Geology of petroleum in Wind River Basin, Central Wyoming: AAPG Bulletin, v. 53, p. 1839-1865. |
Figure 1. A maximum and minimum direction of
seismic anisotropy are used to estimate fracture orientation and
intensity.
Figure 3. Outline of structural geometry for
Circle Ridge 
Figure 4. Magnitude and orientation of
fracturing in the overthrust block based on extensional strain
calculated through a three dimensional Palinspastic reconstruction.
Figure 5. Outcrop of Jurassic Nugget
Sandstone, Circle Ridge 
Figure 6. 
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.
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.
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.