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Seismic Rock-Property Transforms for Estimating Lithology and Pore-Fluid Content*
By
Haitao Ren1, Fred J. Hilterman1, Zhengyun Zhou1, and Mritunjay Kumar2,
Search and Discovery Article #40224
Posted November 30, 2006
*Adapted from extended abstract prepared for presentation at AAPG Annual Convention, Houston, Texas, April 9-12, 2006
1Center for Applied Geosciences and Energy, University of Houston ([email protected])
2Dept. of Geosciences, University of Houston
Velocity and
density values from approximately 2200 sand reservoirs and their encasing shale
intervals were cataloged using well-log curves from the offshore Louisiana shelf
in the Gulf of Mexico. The reservoir depths range from 200m to 5500m and the
reservoirs are predominantly Pliocene to mid-Miocene in age. Fluid substitution
was conducted so that all 2200 reservoirs have velocity and density values for
gas, oil and brine saturation. While conventional depth plots for velocity and
density trends were unstable and generally exhibited random correlation, we did
discover two robust reflection-coefficient transforms. The first transform
relates the normal-incident reflection coefficient (NI) for either gas or oil
saturation to the NI of the equivalent brine-saturated reservoir. We call these
pore-fluid transforms. The second transform relates the near-angle
reflection amplitude to the far-angle reflection amplitude for various
pore-fluid saturations. Surprisingly, the change in amplitude from near to far
angles is predominantly dependent on lithology (shale content, porosity, etc.)
and not the pore-fluid saturant. Thus, these relationships are named
lithology transforms. Using a lithology transform along with the horizon
amplitude maps from near- and far-angle stacks, a reflection-coefficient map for
a specific pore fluid is generated. Normally, the first reflection coefficient
map generated is for the down-dip
brine-saturated portion of the prospect, and
then it is changed to represent the reflection coefficient values for various
hydrocarbon saturations using the pore-fluid transforms. When the converted
reflection coefficient values of the down-
dip
portion of the prospect match the
prospect reflection coefficient values, then an estimate of the pore fluid and
water saturation, SW, is established.
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Distinguishing between fizz and commercial gas saturations from seismic
data is a difficult problem because low and high gas saturation result
in very similar Amplitude-Versus-Offset (AVO) responses. A small amount
of gas in the pore fluid lowers the rock P-wave velocity (Vp)
dramatically, while the S-wave velocity (Vs) does not change much for
different pore fluids and saturations. Rock bulk density changes
linearly with water-saturation. Consequently, fizz water and commercial
gas saturations have very similar Vp and Vp/Vs values and subsequently
very similar AVO responses. Hilterman and Liang (2004) showed that a
reservoir with 31% porosity and SW=0.3 has an identical AVO response to
a reservoir with 34% porosity and SW=0.9. In short, an AVO analysis of
the prospect reservoir, by itself, will not discriminate economic versus
fizz saturation. However, by comparing the reservoir’s AVO properties to
the down-
Calibration of Reflection Coefficient (RC) Transforms Rock-properties for approximately 2200 60m-thick intervals were available from the proprietary TILETM 2 database in the Gulf of Mexico (Geophysical Development Corporation (GDC), Figure 1). For each interval, the velocity and density for shale and for wet-, gas- and fizz-saturated sands were calculated using fluid-substitution techniques (Hilterman, 2001). With the velocity and density values known or estimated, NI values were calculated. Finally, using the linear approximation of Zoeppritz’s equation, the reflection coefficients for wet, gas, and fizz saturations at an incident angle of 30° were calculated for the top of each reservoir. From the catalog of reflection-coefficients, pore-fluid and lithology transforms were developed for the different pore-fluid states and these are shown in Figure 2. In Figure 2a, the NI for both gas and fizz saturation are plotted against the NI of the equivalent brine-saturated reservoir. These are a pore-fluid transforms. In Figure 2b, NI is plotted against the far-angle reflection coefficient. There is a separate plot for each of the pore-fluids. Surprisingly, the change in amplitude from near to far offsets is predominantly dependent on lithology (shale content, porosity, etc) and not the pore-fluid saturant. Thus, this type is named the lithology transform. The quantitative relationships for pore-fluid and lithology transforms are expressed by NIHYC = B1 + B2 NIWET (1a) RC(θ) = L1 + L2 NI (1b) where θ is the reflection incident angle; RC(θ) is the reflection coefficient at the far-offset angle θ (set to 30° for this study); NIHYC and NIWET relate to the hydrocarbon and brine saturations respectively. B1, B2, L1and L2 are the transform coefficients and are listed in Table 1.
Reflection Coefficient Mapping Zhou et al. (2005) proposed a technique for converting seismic amplitude maps into NI maps. The technique is based on a thin-bed reflection model that is expressed by Lin and Phair (1993) as A(θ ) = K *RC(θ )*cos(θ ) , (2) Where θ is the incident angle; A(θ) is the seismic amplitude; RC(θ) is reflection coefficient for the upper boundary. K is expressed by, K = k * 4πb/λ , Where k is a constant for the seismic survey; b is the thin-bed thickness; and, λ is the seismic wavelength. The lithology transform that yields NI is NI = (L1*A0°) / [A(θ)/cos(θ) – L2*A(0°)] . (3) Before converting the seismic near- and far-angle stacks, A(0°) and A(θ=30°), into a reflection coefficient map, a preprocessing multiplication is necessary. Normally, the amplitudes on the far-angle stack are not properly calibrated to the near-angle stack. This initial calibration of the angle stacks to borehole data is very important in the NI mapping technique.
Assume the estimated normal incidence, NIest, from the field data near- and far-angle stacks is P times higher than the true normal incidence, NItrue. That is, NIest=P*NItrue. Then, a new far-angle stack, Atrue(θ) can be generated from the original angle stacks Araw(θ) and Araw(0°). It is reasonable to assume that the amplitudes for Araw(0°) are valid and only the amplitudes for Araw(θ) need to be corrected. With a little bit of algebra, the following results. Atrue(θ) = P*Araw(θ) – cos(θ) *L1* Araw(0°)*(P-1), and (4a) Atrue(θ) = Araw(0°) (4b) The calibrated amplitude maps from Equations 4a and 4b are now inserted into Equation
Figure 3 contains horizon amplitude plots
from an offshore block (3 miles by 3 miles) in High Island, Gulf of
Mexico. Figure 3a is the near-angle map and
Figure 3b, the far-angle. The area circled
by a red line, which we will call a prospect, is actually a known
commercial gas field. Using a suite of well-log curves from a field
well, the NItrue values for wet, fizz and gas saturation were
generated for the location of the well. Equation 3 was applied to the
two angle stack maps with the wet coefficients in
Table 1 to obtain a reflection-coefficient
map that pertains to the areas where the sand formations were wet. The
amplitudes down- The first step is to convert all the brine-saturated formations into their respective NI map. This is done by applying Equation 3 with the wet coefficients from Table 1 to yield the NI wet map in Figure 4a. The prospect is masked because we believe it is not brine saturated and thus the computed NI values would be incorrect. Even though the lithology or bed thickness may change across the survey, this map should be a good estimate of the brine-saturated NI; that is, NI for the brine-saturated formations may vary. Using Equation 1 and the fizz coefficients in Table 1, the brine-saturated map in Figure 4a is converted to an equivalent fizz reflection coefficient map as shown by Figure 4d. In essence, a fluid substitution was performed on the wet NI map in Figure 4a. A similar process is done to obtain the gas NI map in Figure 4e. We will set aside the maps in Figures 4d and 4e for a moment and return to the near- and far-angle maps in Figure 3. This time, we will assume that the prospect is fizz charged (SW=0.9) and apply Equation 3 to the two angle stack maps using the appropriate fizz coefficients in Table 1. The result is displayed in Figure 4b, where only the prospect area is open because we are applying a lithology transform that assumes fizz saturation. Figure 4c represents a similar lithology transform under the assumption that at the prospect SW= 0.3 (gas). The
final step is to compare the two prediction processes. If the prospect
is truly fizz charged, then the amplitude in the prospect (Figure
4b) should match the NI down- In the combined fizz case map (Figure 4d), the color of prospect area was calculated by equation (3), and this color is significantly different from that of the outside area computed by Equation (1). This means that, in the prospect area, the assumption of a fizz reservoir is wrong. However, in the combined gas case map (Figure 4e), the color of prospect area matches the outside area quite well, which shows that the assumption that the prospect is a commercial gas reservoir is correct.
There
are several advantages of this interpretation technique to estimate SW.
First, there is no requirement that the down- There
are also some limitations. This method assumes that the down-
Normal incident reflection coefficients vary with changing rock properties. But NIGAS and NIWET locally have a robust linear relationship that we called the pore-fluid transform. For specific pore-fluids and SW, far-angle reflection coefficients linearly relate to NI. This we named the lithology transform. An
estimation of SW was possible once the two rock-property transforms were
developed. The technique requires an amplitude comparison of the area
down-
We thank the sponsors of the Reservoir Quantification Laboratory. We thank Fairfield for the seismic data and GDC for the rock-properties in TILE2. Portions of this work were prepared with the support of the U.S. Department of Energy under the Award No. DE-FC26-04NT15503. However, any opinions, findings, conclusions, or recommendations expressed herein are those of the authors and do not necessary reflect the views of DOE.
Hilterman, F.J., 2001, Seismic amplitude interpretation: Distinguished Instructor Series, No. 4, SEG/EAGE. Hilterman, F., and Liang, L,, 2003, Linking rock-property trends and AVO equations to GOM deep-water reservoirs: 73rd Ann. Internat. Mtg, Soc. Expl. Geophys., p. 211-214. Lin. T.L., and Phair. R., 1993, AVO tuning: 63rd Ann. Internat. Mtg., Soc. Expl. Geophys., Expanded Abstracts, p. 727-730. Zhou, Z., Hilterman, F., Ren, H., and Kumar, M., 2005, Water-saturation estimation from seismic and rock-property trends: 75th Ann. Internat. Mtg: Soc. Expl. Geophys. |