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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

Detection and identification of subsurface anomalous structures are key objectives in seismic exploration. The coherence technique has been successfully used to identify geologic abnormalities and discontinuities, such as faults and unconformities. Based on the classic third eigenvalue-based coherence ([Formula: see text]) algorithm, we make several improvements and develop a new method to construct covariance matrix using the original and Hilbert transformed seismic traces. This new covariance matrix more readily converges to the main effective signal energy on the largest eigenvalue by decreasing all other eigenvalues. Compared with the conventional coherence algorithms, our algorithm has higher resolution and better noise immunity ability. Next, we incorporate this new eigenvalue-based algorithm with time-lag dip scanning to relieve the dip effect and highlight the discontinuities. Application on 2D synthetic data demonstrates that our coherence algorithm favorably alleviates the low-valued artifacts caused by linear and curved dipping strata and clearly reveals the discontinuities. The coherence results of 3D real field data also commendably suppress noise, eliminate the influence of large dipping strata, and highlight small hidden faults. With the advantages of higher resolution and robustness to random noise, our strategy successfully achieves the goal of detecting the distribution of discontinuities.

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