Fluid identification using frequency-dependent AVO inversion in dissolution caved carbonate reservoir

Sun, Sam Zandong; Jiang, Shan; Sun, Xuan; Yang, Haijun; Han, Jianfa; Li, Yandong

doi:10.1190/segam2012-1017.1

Cited by 5 publications

(6 citation statements)

References 11 publications

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“…(2010) replaced the reflection coefficient in the AVO approximation formula with an amplitude spectrum of various frequencies (Wilson, 2010). Sun et al (2012) used frequencyvarying AVO inversion technology to identify the gas-bearing properties of carbonate reservoirs. The frequency-varying AVO inversion technology reduced the ambiguity of the prediction results and improved the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Reservoir characteristics analysis and favorable area prediction of Zhuhai Formation, Wenchang A oilfield, South China Sea

Liu

2024

Front. Earth Sci.

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Wenchang A oilfield is a recently discovered low-permeability oilfield in the western South China Sea. The exploration target is the Zhuhai Formation, which reservoir lithology changes significantly, and the distribution of favorable reservoirs is unclear. In this study, the reservoir characteristics of the Zhuhai Formation in the Wenchang A oilfield, South China Sea, were analyzed through core identification, thin section identification, physical property testing, pre-stack geostatistical inversion, and frequency-based AVO inversion. Pre-stack geostatistical inversion is based on geostatistics and combines the seismic inversion algorithm with the stochastic sequential simulation algorithm. While the frequency-varying AVO inversion method is an extension of the conventional AVO inversion method, with its core technology being spectrum decomposition. After frequency division, multiple data sets directly participate in the calculation, which enhances the stability and accuracy of inversion.We also predict the favorable reservoir areas of six small layers in the Zhuhai Formation. The results show that 1) the reservoir lithology of the Zhuhai Formation mainly consists of feldspar quartz sandstone and feldspar quartz sandstone, indicating high compositional maturity. The porosity of the reservoir ranges from 14.0% to 19.0%, with an average value of 16.3%. The permeability of the reservoir ranges from 3.1 mD to 126.1 mD, with an average value of 22.4 mD, indicating a medium porosity and low permeability reservoir. The supporting structure of a sandstone reservoir is particle-supported, with the main contact being the “point-line” contact between particles. The main types of pores in sandstone are primary intergranular pores and secondary intergranular dissolved pores. Some pores are feldspar-dissolved pores, and occasionally there are hetero-based micropore. 2) The diagenesis of the Zhuhai Formation reservoir mainly includes compaction, cementation, and dissolution. Cementation and dissolution have minimal impact on the physical properties of reservoirs. The porosity loss of the reservoir after compaction ranges between 16.0% and 27.9%, and the compaction rate ranges between 40.0% and 69.8%, indicating a medium compaction diagenetic facies. 3) The favorable areas of each layer of the Zhuhai Formation are mainly concentrated in the southwest of the study area. The upper layer of ZH1I, the upper layer of ZH1II and the lower layer of ZH1II exhibit the best physical properties, the thickest favorable sand body, the strongest oil and gas display, the widest range of favorable areas, and the greatest exploration potential. The favorable exploration potential of layer 1 in the lower part of ZH1I and layer 2 in the upper part of ZH1II is moderate. The second layer in the lower part of ZH1II has the poorest properties and lower exploration potential. The main factors affecting the favorable area include physical properties, oil and gas display, sand body thickness, etc.

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Section: Introductionmentioning

confidence: 99%

Reservoir characteristics analysis and favorable area prediction of Zhuhai Formation, Wenchang A oilfield, South China Sea

Liu

2024

Front. Earth Sci.

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“…Cheng et al [17] proposed identifying tight gas sandstone reservoirs using frequency-dependent attributes regarding intercept, gradient, fluid factor, and pseudo-Poisson's ratio. Sun et al [18] investigated the sensitivity of the P-wave velocity dispersion attribute to fluid types in hydrocarbon prediction for a carbonate reservoir. Han et al [19] combined the azimuth AVO inversion with spectral decomposition and used anisotropic P-wave dispersion attribute to discriminate fractures and fluids.…”

Section: Introductionmentioning

confidence: 99%

“…Figure9(b) illustrates the fluid mobility attribute FM computed using the method in Section 2.2. In equations(11) and(18), the involved frequency ranges from 5 to 50 Hz, covering dominant frequencies of seismic reflections.Sections in Figures9(a) and 9(b) correspond to the poststack seismic profile shown in Figure5. According to analyses using synthetic data in Section 3.2, high-value D K and FM indicate volcanic gas reservoirs with high porosity and gas saturation.…”

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confidence: 99%

Characterization of a Volcanic Gas Reservoir Using Seismic Dispersion and Fluid Mobility Attributes

Guo

Liu

et al. 2021

Lithosphere

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Seismic dispersion and fluid mobility attributes are used to characterize a volcanic gas reservoir in the Songliao Basin of China. A rock physics model is constructed to describe poroelastic behaviors associated with heterogeneous fluids saturation within the volcanic gas reservoirs, where velocity dispersion and attenuation of propagating waves are attributed to the wave-induced fluid flow described by the patchy saturation theory. Modeling results indicate that the frequency-dependent bulk modulus at the seismic frequency is more sensitive to gas saturation than the P-wave velocity dispersion. Accordingly, a new inversion method is developed to compute bulk-modulus-related dispersion attribute DK for improved characterization of volcanic gas reservoirs. Synthetic tests indicate that DK is more sensitive than traditional P-wave dispersion attribute DP to the variations of reservoir properties. The high value of dispersion attribute DK indicates the volcanic gas reservoirs with high porosity and gas saturation. At the same time, fluid mobility attribute FM can discriminate the volcanic gas reservoir as DK. Field data applications illustrate that DK and FM exhibit anomalies to the gas zones in the volcanic gas reservoir on the cross-well section. However, DK is more robust than FM to identify favorable zones on horizontal slices for specific target layers. Overall, rock physical modeling provides insights into the poroelastic behaviors of volcanic gas reservoirs, and inversion for seismic dispersion attribute DK improves hydrocarbon detection in the volcanic gas reservoir.

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“…; Zhang ; Innanen , ; Sun et al . ; Wu et al . ) aims to estimate seismic attenuation indirectly through the frequency dependence of the reflection coefficient from attenuative and dispersive media.…”

Section: Introductionmentioning

confidence: 99%

“…The applicability of the method is limited by the thickness of the attenuating zones, and some targets are too thin for successful application of the method. An alternative approach Ren, Goloshubin, and Hilterman 2009;Wilson, Chapman, and Li 2009;Xu et al 2011;Zhang 2011;Innanen 2011Innanen , 2012Sun et al 2012;Wu et al 2013) aims to estimate seismic attenuation indirectly through the frequency dependence of the reflection coefficient from attenuative and dispersive media.…”

mentioning

confidence: 99%

Quantitative gas saturation estimation by frequency‐dependent amplitude‐versus‐offset analysis

Chapman

et al. 2014

Geophysical Prospecting

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Seismic amplitudes contain important information which can be related to fluid saturation.The amplitude-versus-offset (AVO) analysis of seismic data based on Gassmann's theory and approximation of the Zoeppritz equations, has played an central role in reservoir characterization. However, this standard technique faces a long standing problem, its inability to distinguish between partial gas and "fizz-water" with little gas saturation. In this paper, we studied seismic dispersion and attenuation in partially saturated poroelastic media by using frequency-dependent rock physics model, through which the frequency-dependent AVO (FAVO) response is calculated as a function of porosity and water saturation. We propose a crossplotting of two attributes derived from FAVO response to differentiate partial gas saturation and "fizz-water" saturation. One of the attributes is a measure of "low frequency"or Gassmann, reflectivity, while the other is a measure of the "frequency-dependence" of reflectivity. This is in contrast to standard AVO attributes, where there is typically no such separation. A pragmatic FAVO inversion for rock and fluid properties is also established based on Bayesian theorem. Synthetic study is performed to explore the potential of the method to estimate gas saturation and porosity variations. An advantage of our work is that the method is in principle predictive, opening the way to further testing and calibration with field data. We believe that such work should guide and augment more theoretical studies of FAVO analysis.

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Fluid identification using frequency-dependent AVO inversion in dissolution caved carbonate reservoir

Cited by 5 publications

References 11 publications

Reservoir characteristics analysis and favorable area prediction of Zhuhai Formation, Wenchang A oilfield, South China Sea

Reservoir characteristics analysis and favorable area prediction of Zhuhai Formation, Wenchang A oilfield, South China Sea

Characterization of a Volcanic Gas Reservoir Using Seismic Dispersion and Fluid Mobility Attributes

Quantitative gas saturation estimation by frequency‐dependent amplitude‐versus‐offset analysis

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