A Method for Identifying Thin Interbedded Lithological Interfaces Based on the Combination of Wavelet and Deconvolution
The second member of the Sangonghe formation in the Moxizhuang area of the Junggar basin is characterized by complex and diverse lithology with rapid variations.The thin layers of lithology within a single sandstone body frequently alternate,exhibiting pronounced lithological,physical,and oil-bearing heterogeneity characteristics.Therefore,meticulously distinguishing the thin interbedded lithology and identifying its layering interfaces are crucial prerequisites for reservoir identification,fluid interpretation,and the determination of effective reservoir thickness.To accurately identify the layering interfaces of thin interbedded lithology,we propose a method that integrates wavelet analysis with deconvolution,approaching it from the perspectives of logging information acquisition resolution and signal analysis.Firstly,utilizing the principle of multi-scale decomposition and reconstruction of wavelet transform,the natural gamma ray(GR)logging curve is decomposed into high and low frequency components,thereby extracting effective signals reflecting lithological variations of the strata as well as high-frequency noise.Then,the effective signals are reconstructed to form a GR curve that has been denoised and better represents the true stratigraphic information.Finally,the deconvolution method is applied to the reconstructed GR curve for high-resolution processing,significantly enhancing the vertical resolution of the GR curve in response to thin-bedded lithology.As validated by core lithology data from exemplary wells,the morphological and numerical characteristics of the high-resolution GR curve obtained through this method can effectively enhance the precision in identifying multiple thin interbedding interfaces within sandstone bodies,thereby offering a solid foundation for conducting detailed evaluations of the reservoir effectiveness in the second member of the Sangonghe formation within the Moxizhuang area.
万方数据
维普
