Distinguishing fizz gas from commercial gas reservoirs using multicomponent seismic data.pdf

Partial gas discrimination is a challenging problem becauselow and high gas saturation can result in very similar seis-mic AVO, bright spot, and velocity sag anomalies. This istypically explained using Gassmann’s theory: i.e., (1) smallamounts of gas in the pore space cause large decreases inrock incompressibility while further increasing gas contentdoes not reduce rock incompressibility significantly, and (2)the shear modulus is not affected by nonviscous fluids inthe rock pore space. In addition, rock bulk density variesgradually with water saturation, as predicted using the vol-ume-average equation. Consequently, low-gas saturationreservoirs and high-gas saturation reservoirs can have sim-ilar VP and VP/VS values (or, equivalently, Poisson’s ratios).Therefore, in many cases, high and low gas saturations can-not be distinguished using existing hydrocarbon indicatorsand techniques. These indicators include those based on VPvariations, such as velocity sags, and those based on Poisson’sratio variations. The latter category has many variations—bright spots, amplitude variation with the offset of P-waveseismic data, VP/VS from P-wave or multicomponent seis-mic data, the fluid factor (Smith and Gidlow, 1987), Lamé’spetrophysical parameters (Goodway et al., 1997), and othersimilar approaches. Density information may sometimesdistinguish high and low gas saturations. However, densityvariation due to lithology changes can be much larger thandensity variation associated with fluid changes so that thelatter is masked (Zhu, 2000).

Fortunately, when high-quality multicomponent seismicdata are available, ratios of amplitude measures from P-Pand P-S seismic reflections (RPP and RPS, respectively) maybe good partial gas indicators (PGIs). These ratios, ∆RPP/∆RPSand ∆RPS/∆RPP, are determined by the differences betweencoefficients measured in a water saturated area and anothertarget area with unknown saturation. The basic idea is to com-pare reflectivities from a target portion of the reservoir thathas unknown water saturation with an area that is 100%water-saturated. Reflection coefficients are evaluated for thesame incident angle �.

This paper discusses how ∆RPS, ∆RPP/∆RPS, and∆RPS/∆RPP might be able to distinguish water-saturationvariations based on theoretical reflection coefficients andsynthetic seismograms in homogeneous and isotropic media.Theoretical reflection coefficients are computed using theZoeppritz equations. Sand VP, VS, and density variations asfunctions of water saturation are computed usingGassmann’s equation. The results of our analysis suggest that∆RPP/∆RPS and ∆RPS/∆RPP can discriminate low and highgas saturation in all four classes of gas sands.

Methodology. We calculate reflection coefficients assuminga model of two welded, elastic, isotropic half spaces. Eachset of calculations assumes constant shale properties.Likewise, the properties of the background sand, the 100%water-saturated region, are also fixed and give the back-ground RPP and RPS. Sand properties in the target area changedue to water-saturation variations and yield that region’s val-ues of RPP and RPS.

We first consider a Class 3 reservoir where sand prop-

erties change with water saturation. ∆RPS/∆RPP and∆RPP/∆RPS were tested for partial gas discrimination whengas gradually but continuously replaced brine in the porespace. Then, shale and/or sand properties were varied totest a series of reservoirs and demonstrate whether∆RPS/∆RPP and ∆RPP/∆RPS are theoretically good PGIs forall four classes of reservoirs.

We compute RPP and RPS using the exact Zoeppritz equa-tions. ∆RPS/∆RPP and ∆RPP/∆RPS are then computed fromthe theoretical reflection coefficients. Paraxial ray tracing isused to generate synthetic seismograms, and the proposedPGIs are then tested on them. The PGIs are also tested againstother seismic attributes with potential for partial gas dis-crimination.

The effect of gas saturation on velocities is computedusing Gassmann’s equations when gas gradually replacesbrine in the rock pore. The cases we considered include (1)different Poisson’s ratios for the shale and the sand, (2) dif-ferent porosities of the sand, and (3) different densities.

When ∆RPS/∆RPP and ∆RPP/∆RPS are tested to comparehigh and low gas saturation, the background sand proper-ties are computed using Wang and Nur’s (1992) best-fitcurves of brine-saturated sands from data in several places.The drained bulk and shear moduli in the target area arecomputed using Gassmann’s equations. Density is obtainedusing the volume-average equation. When the elastic mod-uli deviate from the best-fit curves, VP, VS, and density willdiffer from those sands that fit the best-fit curves, and water-saturation variations will result in variations of Poisson’sratio. This is also tested.

Model studies. First, we assume that the sand is dominatedby quartz and that its porosity is 20%. The bulk and shearmoduli of the brine-saturated sand are 19.48 and 11.25 GPa,

1238 THE LEADING EDGE NOVEMBER 2000 NOVEMBER 2000 THE LEADING EDGE 0000

Distinguishing fizz gas from commercial gasreservoirs using multicomponent seismic data

FUPING ZHU, RICHARD L. GIBSON JR., JOEL S. WATKINS, and SUNG HWAN YUH, Texas A&M University,College Station, Texas, U.S.

Figure 1. Examples of sand properties computed usingGassmann’s equations.

respectively. The drained bulk modulus, computed fromGassmann’s equations, is then 15.78 GPa, based on the para-meter values in Table 1 (from Wang and Nur, 1992).

The VP, VS, and density of the sand are computed as func-tions of water saturation from the Gassmann and volume-average equations. Typical results are shown in Figure 1.Small amounts of gas reduce VP dramatically because gasreduces the bulk modulus significantly. Additional gas doesnot reduce VP because the further change in bulk modulusis slight, but VP does increase gradually when the effect ofthe decrease in the rock bulk density exceeds the effect ofthe decrease in the bulk modulus. On the other hand, VS sys-tematically increases with increasing gas. As a result,

Poisson’s ratio for the sand at 80% water saturation is aboutthe same as it is at 20% water saturation. The density of thesand is about 2.32 at pure brine-saturation and 2.16 at puregas saturation. The density drops only about 7% from purebrine to pure gas saturation.


Figure 2. RPP (a) and RPS (b) as a function of water saturation at the top of a Class 3 gas reservoir. Labels in thecharts are water saturations for the closest curves.

Figure 3. ∆RPS at the top (a) and bottom (b) of the reservoir in Figure 2.

a) b)

a) b)

Table 1. Density and bulk modulus (k) of rock components used in the model

Density (g/cm3)k (GPa)

Quartz2.6540.00

Brine1.002.25

Gas0.200.06

We now take VP, VS, and the density of the shale equalto 3.9 km/s, 2.086 km/s, and 2.3 g/cm3, respectively. Thezero-offset P-wave reflectivity is -0.001 when the sand ispurely brine-saturated, and -0.047 when purely gas-satu-rated. These values are computed from the exact Zoeppritzequations at the shale/sand interface. The AVO gradient isnegative, so this is a Class 2 brine sand changing to a Class3 gas sand laterally as hydrocarbon content varies.

Figure 2 shows RPP and RPS as a function of the incidentangle for water-saturation values ranging from 1.0 to 0.0.Warm colors indicate high gas saturation and cool colors indi-cate low gas saturation.

Figure 2a shows that the RPP of the gas sands (low orhigh gas saturation) are very close, although the RPP of thepure brine-saturated sand is separated from the rest. All thegas sands have similar P-wave AVO patterns, with approx-imately equal intercepts and amplitudes that increase withincident angle. Therefore, P-wave bright spot and AVO tech-niques would not be very effective for partial gas detectionin this case.

Changes in RPS should be easier to detect (Figure 2b).However, in this case, interpreters must look for dim spotson converted-wave data. Because it is simpler to see brightspots, and this may be a more natural anomaly to seek in


Figure 4. ∆RPP/∆RPS at the top (a) and bottom (b) of the reservoir in Figure 2.

Figure 5. ∆RPS/∆RPP at the top (a) and bottom (b) of the reservoir in Figure 2.

a) b)

a) b)

field data, another PGI may be more effective. For example,the RPS difference between the background (Sw = 1.0) andthe target area, ∆RPS, indicates high gas saturation with highamplitudes (Figure 3a). However, ∆RPS is primarily negativeat the top of the reservoir (Figure 3a) but positive at the bot-tom of the reservoir (Figure 3b). This behavior would forcethe interpreter to seek different anomalies.

In contrast, when changes in RPP and RPS are combinedin the ∆RPP/∆RPS ratio, high gas saturation results in rela-tively weak amplitude values at both the top (Figure 4a) andthe bottom (Figure 4b) of the reservoir. On the other hand,low gas saturation shows very negative ∆RPP/∆RPS valuesand its gradient varies significantly over the offset range. Apotential problem with this approach is that ∆RPP/∆RPS isnot stable in the very near offset range where ∆RPSis verysmall.

The reciprocal ratio, ∆RPS/∆RPP, will avoid this instabil-ity and have large magnitudes for high gas saturations; ittherefore is a more intuitive PGI.

Like ∆RPP/∆RPS, the ∆RPS/∆RPP values are negative andtheir curves for different water saturations are well separatedat both the shale/sand interface (Figure 5a) and thesand/shale interface (Figure 5b). Unlike ∆RPP/∆RPS, however,high gas saturation shows large ∆RPS/∆RPP magnitude andsteep negative gradients over the offset range. If traces con-verted to ∆RPS/∆RPP values are stacked, a bright spot willindicate high gas saturation and a dim spot low gas satura-tion, in keeping with the tradition of associating bright spotswith gas detection. In addition, ∆RPS/∆RPP is more sensitivein the intermediate offset range where both RPP and RPS arelikely measured in seismic data. Therefore, ∆RPS/∆RPP is aneven better partial gas indicator (PGI) than ∆RPP/∆RPS.

When the sand and/or shale properties are changed torepresent different classes of reservoirs and when thePoisson’s ratios of the sand and the shale vary, ∆RPP/∆RPSand ∆RPS/∆RPP are still effective PGIs for most cases. Anexample of a Class 4 gas sand is shown in Figure 6. The topreflections of the reservoirs have positive P-wave AVO gra-dients. Although Class 4 gas sands have this unique AVObehavior, ∆RPS/∆RPP still works for partial gas discrimina-

tion. In this example, the sands are the same as in Figure 2.The shale has VP of 4.094 km/s, VS of 2.4 km/s, and densityof 2.4 g/cm3. The shale’s velocities and density are all largerthan those of the sand.

Figure 7 shows several ∆RPS/∆RPP curves for differentwater saturations as a function of the incident angle at thetop of reservoirs. The model in Figure 7a shows a Class 2sand. Figure 7b shows a Class 1 gas reservoir at differentwater saturations. The sand reservoirs are the same as inFigure 2. The shale in Figure 7a has VP of 3.7 km/s, VS of1.98 km/s, and density of 2.3 g/cm3. The shale in Figure 7bhas VP of 2.8 km/s, VS of 1.496 km/s, and density of 2.3g/cm3. Like the model in Figure 2, the shales have higherPoisson’s ratios than the brine and gas sands. The densityof the shale is smaller than that of the brine sand but largerthan the densities of the gas sands. The density in Figure 7bdecreases from the shale to the sand, while VP increases. This


a) b)

c)

Figure 6. RPP (a), RPS (b), and ∆RPS/∆RPP (c) at the top ofa Class 4 gas sand.

Incident angle (degree)

is different from Figure 2, where both VP and density decreasefrom the shale to the sand. In Figure 7c, sand porosity changesto 25% and, consequently, velocities and density decrease.The bulk and shear moduli of the brine-saturated sands inthis example were changed to be arbitrarily smaller than pre-dicted using the best-fit curves of Wang and Nur. This resultsin large Poisson’s ratio variations associated with water-sat-uration changes. The Poisson’s ratio of the brine-saturatedsand increases to 0.32. The values for the gas sand decreasesto 0.15 at 80% water saturation and 0.1 at pure gas satura-tion. The shale velocities are the same as in Figure 7b, butthe density increases to 2.5 g/cm3. Therefore, the shale hashigher velocities and higher density than all sands. And thePoisson’s ratio of the shale in Figure 7c is smaller than thatof the brine-saturated sand but larger than that of the gassands. This is another Class 1 reservoir at the shale/sandinterface.

These different classes of reservoirs have clearly differ-ent RPP and RPS characteristics. Bright spots and AVO inmany models are not sensitive to water saturations. However,as shown in Figure 6, ∆RPS/∆RPP curves of different watersaturations are all well separated. The bottom reflections ofthese reservoirs are tested too. They all have ∆RPP/∆RPS and∆RPS/∆RPP patterns similar to those at the top of the reser-voirs.

The ∆RPS/∆RPP ratio has some common characteristicsin all models. For high gas saturation, it is negative whenthe incident angle is below 30° and it normally has negativesteep gradients in these incident angles. For low gas satu-ration, however, ∆RPS/∆RPP is either negative with gentlegradients or at least partly positive. If traces converted to∆RPS/∆RPP are stacked, high gas saturation demonstrates neg-ative bright spots; low gas saturation often shows negativeor positive dim spots, or sometimes, positive bright spots.

Therefore, high gas saturation should be distinguishablefrom low gas saturation using ∆RPS/∆RPP and ∆RPP/∆RPS.The method is applicable for all four classes of reservoirs, atthe shale/sand or sand/shale interface. It is applicable whenthe shale density and velocity both larger than those of the

sands or when one is larger and the other is smaller. It isapplicable when the Poisson’s ratio is larger than, equal to,or smaller than that of the brine-saturated sand or sand withlow gas mixture. No prior knowledge of the reservoir classis required. And no velocity and density information of thereservoirs is required.

Exceptions occur when the Poisson’s ratio of the shale issmaller than the Poisson’s ratio of a sand with high gas sat-uration. Gas reservoirs like these are probably not encoun-tered very often. Exceptions may also occur when thevelocities and density of the sand do not fit the predictionsof Gassmann’s equations.

Several other seismic attributes—including ∆RPP, ∆RPS,∆RPP/RPP, and ∆RPS/RPS — were tested against ∆RPS/∆RPPand ∆RPP/∆RPS to determine the best PGIs. Of these otherattributes, only ∆RPS (P-S wave AVO and bright spot) candistinguish partial gas saturation in many cases (Figure 3).


a) b)

c)

Figure 7. ∆RPS/∆RPP at the shale/sand interfaces of aClass 2 gas sand (a), a Class 1 gas sand (b), and anotherClass 1 gas sand (c).

Yafei Wu (1999, personal communication) also found thatRPS could be a good PGI. High gas saturation always makesRPS more positive at the shale/sand interface and more neg-ative at the sand/shale interface. Therefore, RPS is positiveat the shale/sand interface (Figure 3a) and positive at thesand/shale interface (Figure 3b). On the other hand, for allmodeled cases, ∆RPP varies in the opposite way to ∆RPS whenlarge amounts of gas are introduced to the pore space at inci-dent angles smaller than 30°. ∆RPS /∆RPP is always negativefor high gas saturation with small and intermediate offsets,as is ∆RPP/∆RPS. Therefore, ∆RPS/∆RPP and ∆RPP/∆RPS areconsistent PGIs for all cases. ∆RPS/∆RPP is also more sensi-tive to water saturation than ∆RPS. ∆RPS/∆RPP is stable at thevery near offsets and indicates high gas saturation withbright spots and therefore is preferred.

In addition, because ∆RPS/∆RPP and ∆RPS are sensitiveto gas saturation at intermediate to far offsets and ∆RPP/∆RPSis sensitive at near offsets, they compensate for one anotherand reduce drilling risks due to inaccurate amplitude infor-mation at some offset ranges.

Synthetic seismograms. Synthetic seismograms were gen-erated using paraxial ray tracing for reservoirs in Figure 2.Sands with water saturation of 100% (background sand), 80%(target sand 1), and 20% (target sand 2) are used. These area Class 2 brine formation and two Class 3 gas reservoirs,respectively. Table 2 lists velocities and density. The thick-ness of the top layer is 2 km.

Figure 8 shows the synthetic seismograms for these exam-ples. The top event is the P-P reflection and bottom is the P-S reflection (as functions of offset). All have the sameamplitude scale. Target sands 1 and 2 both have positive P-wave AVO responses, i.e., their amplitudes are negative atthe zero offset and magnitude increases with offset. They also

all have similar P-S wave AVO patterns, although the mag-nitudes and angles of incidence for the RPS maxima or min-ima differ. These can cause misinterpretation and may leadto drilling in target sand 1, which contains fizz water. If P-Swave seismic data are not available, extensive forward mod-eling and prior knowledge of the rock properties are a mustin order to detect gas in this case. Partial gas discriminationwould not be possible because the RPP response for Sw = 0.8,which is almost the same as for Sw = 0.2.

When multicomponent seismic data are available, how-ever, detecting gas and even fizz water would be possible.RPS differences in the brine-saturated background area andthe target area could be a good gas indicator. The results ofsubtracting the synthetic seismograms and the target sandareas (Figures 8b and 8c) from corresponding synthetic seis-mograms from the background area (Figure 8a) are shownin Figures 9a and 9b, respectively, as a function of incidentangle. The top event is equivalent to ∆RPP and the bottomevent to ∆RPS. These seismogram differences show moresensitivity for gas detection than the original seismograms.However, both ∆RPP and ∆RPS vary greatly from case to case.Without prior knowledge of reservoir class, it is hard tomake drilling decisions without careful integration of ∆RPP


b) c)a)

Figure 8. Seismograms at the top of three reservoir with porosity of 20%: (a) Background Sand, Sw = 100%, (b)Target Sand 1, Sw = 80%, and (c) Target Sand 2, Sw = 20%.

Figure 9. Difference of the seismograms in Figure 8 of Sw = 100% and (a) Target Sand 1, Sw = 80%, and (b) TargetSand 2, Sw = 20%, as a function of incident angle.

a) b)

Table 2. Rock properties used for the synthetic seismograms

ShaleB. SandT. Sand 1T. Sand 2

Vp (m/s)3900385536973755

Vs (m/s)2086220222172265

ρ (g/cc)2.3002.3202.2882.192

Sw

1.00.80.2

and ∆RPS. Therefore, seismic attributes that have consistentcharacteristics for different classes of reservoir are desired.Meanwhile, these attributes should enhance the sensitivityof seismic for detecting fluid and lithologic variations.

Figure 10 shows ∆RPS/∆RPP from the seismograms inFigure 9. The steep solid ∆RPS/∆RPP curve in Figure 10, reach-ing -400 at 30°, indicates high and normally commercial gasaccumulation. The dotted ∆RPS/∆RPP curve indicates low andnormally noncommercial gas accumulation. Because∆RPS/∆RPP has consistent characteristics for high gas satu-ration in almost all classes of reservoirs, the amount of nec-essary prior knowledge of the reservoirs and model studyis theoretically reduced. ∆RPS/∆RPP also has higher sensitivityto water saturation than ∆RPS.

Notice also that Figure 10 uses the signed maximum∆RPS and ∆RPP. If the rms ∆RPS and ∆RPP are used, ∆RPS/∆RPPwill be positive for all cases. Thus, the sensitivity of ∆RPS/∆RPPtoward high or low gas saturation is reduced for some casessuch as Figure 7b, although it is still very sensitive in manyother cases.

Conclusions. ∆RPS, ∆RPS/∆RPP, and ∆RPP/∆RPS can be effec-tive hydrocarbon indicators and PGIs when high-qualitymulticomponent seismic data are available. These hydro-carbon indicators are more sensitive to water saturation vari-ations than existing hydrocarbon indicators. ∆RPS/∆RPP is abetter PGI because it shows bright spots for high gas satu-ration, is most sensitive to water saturation in the interme-diate offsets, and is stable for small angles of incidence.

∆RPS/∆RPP distinguishes commercial gas reservoirs fromnoncommercial reservoirs with differences in the change of∆RPS/∆RPP at near and intermediate offsets. High gas satu-ration has steep negative ∆RPS/∆RPP gradients and negative∆RPS/∆RPP values with large magnitudes. Low gas satura-tion has gentle negative ∆RPS/∆RPP with small magnitudesand gentle gradients or sometimes has at least partly posi-tive ∆RPS/∆RPP. Gas saturation is also indicated by the∆RPS/∆RPP values in the intermediate offset range, where

∆RPS/∆RPP is most sensitive to water saturation. For exam-ple, ∆RPS/∆RPP of -500 at an incident angle of 30°, scaled by1000, may suggest commercial gas accumulation. Avalue of-50 suggests fizz gas. If traces converted to ∆RPS/∆RPP dataare stacked, high gas saturation shows negative bright spotsand low gas saturation negative dim spots or positive spots.

∆RPS/∆RPP and ∆RPP/∆RPS are applicable to all fourclasses of gas reservoirs in the same way at the shale/sandand the sand/shale interfaces. They are applicable when thePoisson’s ratio of the shale is equal to or larger than that ofsand with pure brine or small amount of gas. They are alsoapplicable when density and velocity both increase ordecrease, or one decreases and the other increases, from theshale to the sand.

Suggested reading. “Framework for AVO gradient and inter-cept interpretation” by Castagna et al. (GEOPHYSICS, 1998).“Improved AVO fluid detection and lithology discriminationusing Lamé petrophysical parameters; “��”, “��”, & “�/� fluidstack” by Goodway et al. (SEG 1997 Expanded Abstracts).“Weighted stacking for rock property estimation and detectionof gas” by Smith and Gidlow (Geophysical Prospecting, 1987).“Elastic wave velocities in porous media: A theoretical recipe”by Wang and Nur (in Seismic and Acoustic Velocities in ReservoirRocks, Geophysics Reprint Series No. 10, SEG 1992). “Shear-wavevelocity estimation using multiple logs and multicomponentseismic AVO interpretation: Gulf of Thailand” by Zhu (doctoraldissertation, Texas A&M University, 2000). “Distinguishingwater saturation changes from porosity or clay content changesusing multicomponent seismic data” by Zhu et al. (GCAGSTransactions, 2000). LE

Acknowledgments: Special thanks to Dag Nummedal, Robert Estill, GregBall, and James DiSiena from Unocal Corporation for helpful discussions.F. Zhu is also grateful for a helpful discussion with several Vastar geo-physicists, especially Keith W. Katahara and Ken Hargrove. Thanks alsoto Unocal for financial support of F. Zhu and to the SEG and GCAGSscholarship committees.

Corresponding author: F. Zhu, [email protected]


Figure 10. ∆RPS/∆RPP of the two cases in Figure 9.Values measured from signed maximum amplitudes ofthe seismograms.

Distinguishing fizz gas from commercial gas reservoirs using multicomponent seismic data.pdf

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