Author(s)

Yu Zhang^*, Junlin Zhang, Shengli Xu, Lina Yang

CNOOC EnerTech-Drilling & Production Co., Tianjin, China.

Machine learning is a good method for predicting fracture by integrating multi-source information. Post-stack seismic attributes are commonly used to predict medium to large fractures, while pre-stack seismic attributes are proven to be more sensitive to small and micro sized fractures through forward modeling. Using machine learning algorithm to fuse information from different scales to predict fracture can greatly improve the accuracy of fracture prediction. On the basis of In-Situ stress prediction, the paper conducted post-stack seismic attribute analysis and pre-stack seismic attribute analysis, further studied on the sensitivity of seismic attributes to fracture and selected sensitive attributes, used the sensitivity log of well-bore fractures as the target log for learning, ultimately obtained a comprehensive body of fracture. Through blind well verification, the prediction results match well with the we1l data and the prediction results is highly consistent with the production data. The results of fracture prediction are reliable, and the research method has certain reference significance for fracture prediction.

In-Situ Stress, Fracture Prediction, Seismic Attribute, Machine Learning

Zhang, Y., Zhang, J. L., Xu, S. L. and Yang, L. N. (2024) Research on Fracture Prediction Method Based on Multi-Source Information Fusion. Journal of Geoscience and Environment Protection, 12, 291-304. doi: 10.4236/gep.2024.126018.