Last month
we discussed the importance of accurately describing the geological,
geophysical and petrophysical attributes of fractures to optimize
fractured reservoir management. Ron Nelson’s 2001, classification
of “fracture-dominated” vs. “matrix-dominated” reservoir types helps
to recognize the range of porosity and permeability in those producing
reservoirs.
We also found that converted waves (PS-waves), created
by traditional downgoing compressional waves
(P-waves) that reflect as shear-waves (S-waves), provide us with
a unique ability to measure anisotropic seismic attributes that
are sensitive to fractures.
We saw an example at Valhall Field in the North Sea
where the fast shear wave orientation showed a concentric subsidence
stress field in shallow horizons.
Unfortunately the situation is a bit more complicated.
Figure 1 shows that the
complexity of S-wave splitting can increase with the distance of
travel. The separate fast and slow waves produced by the initial
PS-wave in the first (lower) anisotropic layer encountered can split
again within the next (upper) anisotropic layer above.
In addition, each rock layer can have a different
orientation of fractures (coordinate frame) and different fracture
density. The various split S-wave modes are combined when detected
by the two horizontal geophones. In order to estimate the azimuthal
anisotropy (fracture properties) at the target, we need to unravel
the data by layer stripping in a top-down fashion.
As a result, the overburden anisotropy must be determined
and removed first. The results we saw last month represent an estimate
of this overburden anisotropy above the Valhall Field.
PS-wave Data Example: Wyoming Fractured Gas Sands
Several land examples from Wyoming were acquired to
investigate naturally fractured gas sands. Two of these, from the
Green River Basin, show similarities in the orientation of the fast
S-wave and amount of anisotropy in the overburden, as well as fracture-related
anisotropy associated with faults and lineaments.
Another example is the Madden Field from the Wind
River Basin (figure 2). Naturally fractured
tight gas sands in the Tertiary age Lower Fort Union formation produce
from depths of 4,500 to 9,000 feet. A 3-D seismic survey covering
15 square miles over the crest of the field shows the fault trends
(bold east-west lines). The seismic data was acquired using dynamite
with 20 pound charges set at a depth of 60 feet.
The important attributes shown in figure
2 are the percent anisotropy in color, from zero to 9 percent
over the Lower Fort Union (at 2.2 to 3.3 seconds reflection time)
after correcting for overburden anisotropy by layer stripping, and
the fast S-wave orientation by small vectors whose length is proportional
to percent anisotropy.
The interesting point here is that variations in
percent anisotropy appears to be controlled by the faults; the orientation
of the fast S-wave is usually oblique to them. Areas of high percentage
of anisotropy may represent sweet spots of concentrated fracturing
or fracture swarms.
Although fracture properties have not been directly
calibrated with anisotropy measurements from borehole data in the
survey area, a VSP outside the area showed changes in anisotropy
(S-wave orientation) between the overburden and Lower Fort Union
that are similar to the PS-wave anisotropy.
PS-wave Data Example: Adriatic Fractured Carbonate
The next example (figure 3)
is from the Adriatic Sea offshore Italy, where the target is the
naturally fractured Scaglia carbonate in the Upper Paleocene. Significant
east-west tectonic compression creates north-south anticlinal structures
where commercial quantities of gas have accumulated in fractured
zones.
The operators (Agip) acquired an ocean bottom cable
(OBC) seismic survey to help them position two horizontal wells
for optimal recovery.
The fast S-wave direction shown in color illustrates
the bi-modal distribution associated with the target layer. Yellows
and oranges are oriented roughly east-west, and blues and greens
north-south.
Note the compartmentalization and apparent control
by faulting (thin black lines). Where faults and anticlinal structure
(thick red arrows) change direction in the south there is also a
change in the fast S-wave direction (browns and dark blues).
The most important result is the good agreement with
the borehole data in wells at the top of the structure (white points).
From breakout analysis and induced fracture studies,
the maximum horizontal stress is consistently about N70E. This agrees
with P-wave fast directions determined from AVO analyses as a function
of azimuth.
Based on production, borehole fracture studies and
anisotropy from seismic data, the Emilio Field has characteristics
of a Type II fractured reservoir. Out of the small number of wells
drilled, only a few are highly productive.
Although there may be some secondary matrix or vuggy
porosity, it appears that fractures control the permeability and
have a significant impact on the production.
Fracture Characterization Technology
Historically the classification of Type I (fracture-dominated)
to Type IV (matrix-dominated) reservoirs has proved to be quite
useful.
Figure 4 is a graph,
also from Nelson (2001), that shows examples from several reservoirs
where the percentage of wells are ordered from the least to the
most productive and the vertical axis is cumulative production.
The different fractured reservoirs correlate nicely with these production
characteristics.
For the Type I, fracture-dominated heterogeneous
reservoirs, a small percentage of wells contribute to most of the
production, and there are many dry and marginal wells. As we transition
through the other types, the curves become straighter, and more
wells contribute equally to the total production.
The 45-degree line corresponds to a homogeneous-isotropic,
matrix-dominated reservoir where all wells contribute equally.
Nelson has quantified these fractured reservoir types
by a “Fracture Impact Coefficient.” He points out that this is not
necessarily a physical property of the reservoir, but is instead
a result of drilling fields on regular grids without exploiting
the presence of fractures — something he calls “fracture denial.”
Consequently, it might be more appropriate to call
this quantity the “Fracture Denial Coefficient,” because it appears
to be directly proportional with fractured reservoir type and ranges
between 0.28 — 0.73.
Ultimately our goal is to avoid the scenario of unproductive
wells in the lower left corner of the graph in figure
4 by using every tool at our disposal to characterize fractures
as early as possible for efficient reservoir depletion. One of these
tools can be PS-wave data for measuring azimuthal anisotropy and
the heterogeneity related to fractures.
The examples presented in these articles suggest that
azimuthal anisotropy can be measured with wide-azimuth PS-wave surveys
and that S-wave splitting is highly sensitive to the maximum horizontal
stress direction. Knowing these maximum stress directions, which
are aligned with open fractures when the differential stress is
large enough, provides valuable information about preferred reservoir
flow directions.
Potentially, PS-wave data could become an integral
part of fracture sweet-spot detection, reservoir model building/simulation
and dynamic reservoir management through the use of time-lapse surveys.
However, to utilize this technology optimally, it
is important to calibrate results with ground truth for incorporating
into reservoir models. One approach is VSP data to acquire azimuthal
S-wave information at the same scale as surface-seismic data.
Dipole sonic and FMI logs are also valuable for characterizing
small-scale fracture properties that can be related to larger scale
features.
It also is important to improve our resolution with
smaller seismic time windows and more accurate anisotropy models
that include dipping fracture properties. However, these will have
to be the subject of future research.
The author thanks Rich Van Dok, Richard Walters
and Bjorn Olofsson from WesternGeco, for their expertise in data
processing of the Madden, Emilio and Valhall studies, respectively;
and also Lynn Inc., Eni/Agip division, BP and WesternGeco for
their support and permission to publish this material.