Application of high-precision 3D seismic technology to shale gas exploration: A case study of the large Jiaoshiba shale gas field in the Sichuan Basin

Chen, Zuqing; Yang, Haijun; Wang, Jingbo; Zheng, Tianfa; Jing, Penggui; Li, Suguang; Chen, Chao

doi:10.1016/j.ngib.2016.03.006

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…The Lower Silurian Longmaxi black shale and overlying Liangshan limestone constitute a good reservoir-seal assemblage (Zhang et al, 2008a;Liu and Wang, 2012). The entire Longmaxi Formation has abundant graptolite of about 500 m thick throughout the block, which is a product of deep-water shelf deposition indicating a strong reducing environment (Chen et al, 2016). The Longmaxi Formation comprises two distinctive sections as shown in Figure 2.…”

Section: Tectonic and Sedimentary Backgroundmentioning

confidence: 99%

Using machine learning technology based on fuzzy neural network and geophysical data to predict the shale gas sweet spots: A case study of lower silurian longmaxi formation in Wy Block, Sichuan Basin

Sheng

Shu

Wang

et al. 2022

Energy Exploration & Exploitation

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Compared with shale gas in north American, it is older, more mature, and significantly difference in China. Taking the Longmaxi Formation of WY block in Sichuan Basin as an example, the quantitative prediction method of sweet spots for high mature shale gas is formed. Firstly, the geophysical response characteristics of shale gas reservoir is studied, and the physical characteristics of the sweet spots are determined. Then, the distributions of reservoir evaluation parameters, such as total organic carbon, brittleness, and others, are obtained by pre-stack simultaneous inversion. Finally, fuzzy neural network method is carried out to predict the distribution of sweet spots and to deploy wells. Shale gas sweet spots in this block vertically concentrate within 30 m above the bottom of the Longmaxi Formation. And two grades of sweet spots are evaluated. The quantitative relationship between the production results of horizontal wells and the evaluation is established with Fuzzy neural network method.

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Section: Tectonic and Sedimentary Backgroundmentioning

confidence: 99%

Using machine learning technology based on fuzzy neural network and geophysical data to predict the shale gas sweet spots: A case study of lower silurian longmaxi formation in Wy Block, Sichuan Basin

Sheng

Shu

Wang

et al. 2022

Energy Exploration & Exploitation

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“…As the contributor besides P-and S-velocities for the angle-dependent reflectivity series, the density parameter can be computed in theory under the current pre-stack seismic system. Although the specific seismic acquisition system, i.e., wide azimuth angles, small intervals, high folds and long spread, as well as serious data processing, was applied to improve the seismic image precision for this shale reservoir (Chen et al 2016), the seismic data quality is varying across the whole research area with an average S/N ratio about 5. The largest incidence angle at the target shale reservoir is about 30°.…”

Section: Geology Backgroundmentioning

confidence: 99%

“…The accuracy of inversion for any reservoir principally relies upon the quality of input data. Successful data processing in this area focuses on preserving the true amplitudes in the whole processing workflow, for example, amplitude compensation, noise attenuation, resolution enhancement and pre-stack migration (Chen et al 2016). The key criterion for preserving amplitude is that the AVO characteristics between synthetic and data gathers should be similar.…”

Section: Field Data Applicationmentioning

confidence: 99%

Pre-stack seismic density inversion in marine shale reservoirs in the southern Jiaoshiba area, Sichuan Basin, China

et al. 2018

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For a typical marine shale reservoir in the Jiaoshiba area, Sichuan Basin of China, P-impedance is sensitive for identifying lithology but not suitable for indicating good shale reservoirs. In comparison, density is an important quantity, which is sensitive for identifying the organic-rich mud shale from non-organic-rich mud shale. Due to the poor data quality and incidence angle range, density cannot be easily inverted by directly solving the ill-posed pre-stack seismic inversion in this area. Meanwhile, the traditional density regularizations implemented by directly using the more robust P-impedance inversion tend to be inaccurate for recovering density for this shale reservoir. In this paper, we combine the P-impedance and the minus uranium to construct the pseudo-P-impedance (PIp) at well locations. The PIp is observed to be sensitive for identifying organic-rich mud shale and has a good correlation with density in this area. We employ the PIp-density relation into the pre-stack inversion framework to estimate density. Three types of regularization are tested on both numerical and field data: These are no regularization, traditional regularization and the proposed approach. It is observed that the proposed method is better for recovering the density of organic-rich mud shale in the Jiaoshiba area.

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“…The Lower Silurian Longmaxi black shale and overlying Shiniulan limestone constitute a good reservoir-seal assemblage (Zhang et al., 2008) as shown in Figure 2 (left). The entire Longmaxi Formation has abundant graptolite of about 500 m thick throughout the block, which is a product of deepwater shelf deposition (Chen et al., 2016) indicating a strong reducing environment. The Longmaxi Formation comprises two distinctive sections.…”

Section: Geologic Setting Of the Research Areamentioning

confidence: 99%

Prediction of sweet spots in shale reservoir based on geophysical well logging and 3D seismic data: A case study of Lower Silurian Longmaxi Formation in W4 block, Sichuan Basin, China

Sheng

Zhao

Ouyang

et al. 2016

Energy Exploration & Exploitation

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W4 block of Sichuan Basin is a pioneer in shale gas exploration and development in China. But geophysical prospecting is just at its beginning and thus has not provided enough information about how sweet spots distribute for the deployment of horizontal well. This paper predicted sweet spots based on logging and 3D seismic data. Well logging interpretation method was used to get the key evaluation parameters of shale reservoir and determine the distribution of sweet spots in vertical direction. Rock physics analysis technology was used to define the elastic parameters that were sensitive to the key evaluation parameters, such as TOC and gas content of shale gas reservoir. At the same time the quantitative relationships between them were established. Based on the result of seismic rock physics analysis, prestack inversion was carried out to predict the transverse plane distribution of the key evaluation parameters of shale reservoir. These research results are integrated to determine the distribution of sweet spots. The results show that sweet spots in this area were characterized by high TOC content, high gas content, high GR, high Young's modulus, low Poisson's ratio, low density, and low P-wave velocity. Density was the most sensitive elastic parameters to TOC of the reservoir. The optimal combination for predicting the gas content is composed of six parameters include density, Poisson's ratio, and so on. Sweet spots in this block vertically concentrate within 30 m above the bottom of Longmaxi Formation. Two classes of sweet

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Application of high-precision 3D seismic technology to shale gas exploration: A case study of the large Jiaoshiba shale gas field in the Sichuan Basin

Cited by 7 publications

References 6 publications

Using machine learning technology based on fuzzy neural network and geophysical data to predict the shale gas sweet spots: A case study of lower silurian longmaxi formation in Wy Block, Sichuan Basin

Using machine learning technology based on fuzzy neural network and geophysical data to predict the shale gas sweet spots: A case study of lower silurian longmaxi formation in Wy Block, Sichuan Basin

Pre-stack seismic density inversion in marine shale reservoirs in the southern Jiaoshiba area, Sichuan Basin, China

Prediction of sweet spots in shale reservoir based on geophysical well logging and 3D seismic data: A case study of Lower Silurian Longmaxi Formation in W4 block, Sichuan Basin, China

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