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

Abstract

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

Applications of Differential Effective Medium (DEM)-Driven Correlations to Estimate Elastic Properties of Jafurah Tuwaiq Mountain Formation (TMF)

“…From this map, the high-BI locations shown in red are targeted to drill wells and obtain log and core data. Similar maps can be produced for the stiffness properties of the formation [35].…”

“…From this map, the high-BI locations shown in red are targeted to drill wells and obtain log and core data. Similar maps can be produced for the stiffness properties of the formation [35]. In this study, as we had only access to XRD data, the mineral BI was estimated based on the mineral composition.…”

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

“…It includes 6 tectonic units, i.e. Chuandong highsteep fold belt, Chuanbei low-flat fold belt, Chuanxi low-steep fold belt, Chuanzhong flat fold belt, Chuannan low-steep fold belt and Chuandong high-steep fold belt (Chen et al, 2017b). WY block lies in Chuannan low-steep fold belt, as shown in Figure 1.…”