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| Reservoir prediction method of fusing frequency-decomposed seismic attributes using Stacking ensemble learning |
| LIU Lei1,2, LI Wei2,3, DU Yushan4, YUE Dali1,2,3, ZHANG Xueting2,3, HOU Jiagen1,2,3 |
1. College of Artificial Intelligence, China University of Petroleum (Beijing), Beijing 102249, China;
2. National Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China;
3. College of Geosciences, China University of Petroleum(Beijing), Beijing 102249, China;
4. Shengli Oil Field Company, SINOPEC, Dongying, Shandong 257015, China |
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Abstract Seismic attributes contain a large amount of reservoir information， and the fusion of multiple seismic attributes can improve the precision of reservoir prediction. However， due to the complex underground geological structure and strong heterogeneity， a single fusion method of seismic attribute is difficult to describe the reservoir characteristics. Therefore， a reservoir prediction method of fusing frequency-decomposed seismic attributes using Stacking ensemble learning is proposed. The method consists of three main parts： ①Based on the amplitude and frequency relationship of the reservoir with different sand thicknesses， seismic data with varying frequencies and bandwidths are considered to reduce the uncertainties of seismic attribute interpretation. ②Jointly preferring seismic attributes based on correlation analysis and unsupervised clustering is performed to eliminate redundant information of seismic attributes. ③A Stacking ensemble learning model， which can combine the advantages of various models， is designed to fuse seismic attributes with different frequencies and bandwidths for improving seismic attribute interpretation resolution. The proposed method is applied by taking the Chengdao Oilfield in the Bohai Bay Basin as an example. The quantitative linear formula analysis method is proposed to further evaluate the generalization effect of the Stacking model. The results prove that comprehensive prediction accuracy and the reliability of the Stacking model are significantly improved compared with those of the single-class models. The corresponding high-value areas of the fusion attribute are more evident， and the correlation coefficient between the fusion attribute and sand thickness can reach 0.92， indicating that the method has great potential in reservoir prediction.
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Received: 30 May 2023
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2024, Vol. 59 
