A method for identifying the thin layer using the wavelet transform of density logging data

作者：Zhang, Quanying; Zhang, Feng^*; Liu, Juntao; Wang, Xinguang^*; Chen, Qian; Zhao, Liang; Tian, Lili; Wang, Yang

来源：Journal of Petroleum Science and Engineering, 2018, 160: 433-441.

DOI：10.1016/j.petrol.2017.10.048

• Wavelet transform
• Density logging
• Mother wavelet
• Decomposition level
• Thin-layer identification

In the late stage of oilfield development, thin reservoirs become particularly important for oil and gas exploration. However, current density logging, as a primary method of reservoir identification, has a lower resolution in identifying thin-layers. In this study, a discrete wavelet transform (DWT) is utilized in density logs to identify thin-layers. By adopting different Daubechies (dbN) wavelets and decomposition levels, we analyze the approximation coefficients (cA) and detailed coefficients (cD) and identify the thin-layer signal from detailed coefficients. And then, we reconstruct a new density curve with enhanced thin-layer signal for identifying the thin layer. Results show that db4 wavelet and 3 level are the optimum mother wavelet and decomposition level for the density logging. Detailed coefficients (cD3) from 3rd level decomposition are highly consistent with the thin-layer information, which is suitable for thin-layer identification. Besides, the reconstructed density curve shows a higher thin-layer resolution. This method is successfully applied in the oilfield, and the thin-layer resolution of density curve is improved from 30 cm to 15 cm in accordance with microspherically focused logging (RXO).

• 出版日期2018-1
• 单位中国石油大学（北京）