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GCPS-Wave Azimuthal
Anisotropy
: Benefits for Fractured Reservoir*
By
James E. Gaiser1
Search and Discovery Article #40120 (2004)
*Adapted from the Geophysical Corner columns in AAPG Explorer, April and May, 2003, entitled, respectively, "Reservoir Cracks Tell Many Tales” and “Stress Direction Hints at Flow,” and prepared by the author. Appreciation is expressed to the author, 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.
1Geophysical Advisor, WesternGeco, Denver, Colorado ([email protected])
There are many known fractured reservoirs worldwide that have been profitably produced, but it is safe to say that none of them have been depleted efficiently. As production costs rise and our industry focuses more on production and development, it is becoming crucial to recognize the influence of fractures early in the life of a field for optimal reservoir management. An important part of this management begins with the classification of fractured reservoirs based on production issues, such as rates and reserves.
Fractures have a significant effect on permeability, resulting in preferred directions of flow, and they are probably more common than we think. A key strategy for fractured reservoir management is an accurate description of the geological, geophysical, and petrophysical attributes of fractures within the reservoir. Traditionally this information comes from well data and, to some extent, large-scale seismic features (observable faults).
This article describes how sub-seismic
attributes of azimuthal anisotropy
can potentially add to this characterization
of a fractured reservoir.
uIntroductionuFigure captionsuPS-wavesuFractured reservoir typesuFractures & PS-wave datauValhall fielduAdvanced applicationsuWyoming gas sandsuAdriatic carbonatesuFracture characterizationuConclusionuAcknowledgementsuReferences
uIntroductionuFigure captionsuPS-wavesuFractured reservoir typesuFractures & PS-wave datauValhall fielduAdvanced applicationsuWyoming gas sandsuAdriatic carbonatesuFracture characterizationuConclusionuAcknowledgementsuReferences
uIntroductionuFigure captionsuPS-wavesuFractured reservoir typesuFractures & PS-wave datauValhall fielduAdvanced applicationsuWyoming gas sandsuAdriatic carbonatesuFracture characterizationuConclusionuAcknowledgementsuReferences
uIntroductionuFigure captionsuPS-wavesuFractured reservoir typesuFractures & PS-wave datauValhall fielduAdvanced applicationsuWyoming gas sandsuAdriatic carbonatesuFracture characterizationuConclusionuAcknowledgementsuReferences
uIntroductionuFigure captionsuPS-wavesuFractured reservoir typesuFractures & PS-wave datauValhall fielduAdvanced applicationsuWyoming gas sandsuAdriatic carbonatesuFracture characterizationuConclusionuAcknowledgementsuReferences
uIntroductionuFigure captionsuPS-wavesuFractured reservoir typesuFractures & PS-wave datauValhall fielduAdvanced applicationsuWyoming gas sandsuAdriatic carbonatesuFracture characterizationuConclusionuAcknowledgementsuReferences
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PS-Waves
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.
Solutions that PS-wave
The goal, of course, is to reduce the total production costs for reservoir depletion by using fracture information as early as possible. Fractured Reservoir Types
It is well known that porosity and
permeability are key factors used to describe fractured reservoirs. As a
motivation for the need of azimuthal A Type I fractured reservoir is where fractures dominate both porosity and permeability. Most of the reserves are stored in the fractures, and flow is confined within them. These are very heterogeneous and anisotropic reservoirs. At the other end of this distribution are Type IV reservoirs, where fractures provide no additional permeability or porosity. Ideally this would be a homogeneous "tank" reservoir when no fractures are present -- but when they are present, fractures can sometimes be a problem and act as barriers to flow. Type II and Type III fractured reservoirs are of an intermediate nature where fractures control permeability and assist permeability, respectively. In these two cases, more reserves are stored within the matrix, but fractures still have an impact and can result in anisotropic permeability and unusual response to secondary recovery (elliptical drainage). Bottom line: In going from Type IV to Type I, there is an increasing effect of fractures. Fractures and PS-Wave Seismic DataFracture properties are fractal by nature, as illustrated in Figure 2. Cores and image logs typically provide the small-scale features of the reservoir and surface-seismic data can provide the largest scale features like faults with large displacements. Each tool yields a portion of the total fracture network; however, it is clear that these end members alone do not control production. If they did, reservoir models and fluid simulations would be perfect. Fracture properties over the intermediate range of scales in Figure 2 are missing. Traditionally this has been filled with paleo-strain fields that relate to possible fracture directions and intensities, inferred from geomechanical modeling by palinspastic reconstruction. This method, however, can be highly non-unique and uncertain in the presence of unconformities.
Azimuthal
We can measure In addition to the P-waves that reflect at a common midpoint (CMP), we detect PS-waves that convert at common-conversion points (CCP), using three-component (3C) geophones. The source-to-detector azimuth controls the direction of polarization of the created S-wave, but this upgoing S-wave immediately splits and travels to the surface as two orthogonally polarized S-waves.
Figure 4
shows a more detailed view of S-wave splitting for a single set of
vertical fractures, simulated by a grid that is oriented north-south.
The upgoing converted S-wave travels as a fast and slow component that
is polarized parallel and perpendicular to the fractures, respectively.
The time difference between them depends on the percent S-wave
PS-Wave Data Example: North Sea Subsidence StressThe algorithm used for fracture characterization is a layer-stripping method that consists of first finding an optimal rotation of the horizontal components to separate fast and slow S-waves by Alford rotation. This provides the fast S-wave direction (fracture orientation). Then correlation of the fast and slow S-wave provides time delays for estimates of the amount of splitting and fracture density information. Figure 5 shows the results from the shallow overburden at the Valhall Field in the North Sea. A 3-D ocean bottom cable (OBC) survey was acquired there in 1998 using wide-azimuth source-receiver geometry to provide a full range of azimuth data.
The small vectors show the orientation
of the fast shear-wave direction, oriented NNW by SSE, as measured along
the receiver lines, and the length of these vectors is proportional to
the time lag or percent Note the interesting concentric pattern centered on the production platform (red triangle). This is a dramatic example where man-made alterations of the subsurface have induced horizontal-stress perturbations near the surface.
The pattern of S-wave splitting
correlates precisely with subsidence at the platform due to collapse of
the reservoir. In the center where there has been four meters of
subsidence the
Unfortunately the situation is a bit
more complicated than that described in the Valhall example, and
advanced applications of seismic azimuthal
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 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 Another example is the Madden Field from the Wind River Basin (Figure 7). Naturally fractured tight gas sands in the Tertiary Lower Fort Union formation produce from depths of 4500 to 9000 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 were acquired using dynamite with 20 pound charges set at a depth of 60 feet.
The important attributes are shown in
Figure 7, the percent
The interesting point here is that
variations in percent
Although fracture properties have not
been directly calibrated with PS-wave Data Example: Adriatic Fractured CarbonateThe next example (Figure 8) is from the Adriatic Sea, offshore Italy, where the target is the naturally fractured upper Paleocene Scaglia carbonate. 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 bimodal 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
Fracture Characterization TechnologyHistorically the classification of Type I (fracture-dominated) to Type IV (matrix-dominated) reservoirs has proved to be quite useful. Figure 9 is a graph, also from Nelson (2001), showing examples from several reservoirs where the percentage of wells are ordered from the least to the most productive, nd 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 9 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
The examples presented in these
articles suggest that azimuthal 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
AcknowledgmentsThe 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.
Nelson, R.A.,
2001, Geologic analysis of naturally occurring fractured reservoirs (2nd
edition): Gulf Professional Publishing, Boston, Mattner, Joerg, 2002, Fractured reservoir characterization from collecting data to dynamic modeling: Course, GeoTech Consulting, slide (as part of presentation).
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