A novel seismic inversion method based on multiple attributes and machine learning for hydrocarbon reservoir prediction in Bohai Bay Basin, Eastern China - Liu, Zongbin; Zhu, Jianmin; Tian, Bo; Zhang, Rui; Fu, Yongheng; Liu, Yuan; Wang, Lixin (Frontiers in Earth Science, 2024)
As the demands for hydrocarbon exploration continue to rise, the identification of thin sand bodies becomes significantly important for subsequent petroleum exploration and development efforts. However, traditional inversion techniques struggle with complex subsurface structures because of the low frequency seismic data. To characterize the architecture of hydrocarbon reservoir precisely, a novel seismic inversion method is applied to improve the resolution of seismic data for a high interpretation accuracy. In this study, we take the X Oilfield in Eastern China as an example, adopted a novel approach combining spectral decomposition with convolutional neural networks (CNNs) within a genetic algorithm (GA) framework for inversion. The CNNs are adept at recognizing and interpreting the spatial configurations in the data, thereby establishing a high correlation between seismic attributes and sand body distributions. GA helps CNNs to get an optimal solution in a fast speed. The results reveal that the model's sand thickness predictions closely match the actual measurements at wells, with a new horizontal well's alignment with the predicted output reaching an accuracy of 85.1%. Compared to traditional seismic inversion methods, our method requires less data. This approach may find a wider application, especially at offshore oilfields with few wells data and low quality seismic data.