中国石油大学学报（自然科学版）2025，Vol.49Issue(4)：1-10,10.DOI:10.3969/j.issn.1673-5005.2025.04.001
地震属性驱动的条件生成对抗网络沉积微相模型构建
Construction of sedimentary microfacies model based on conditional generative adversarial network
摘要
Abstract
Due to the complexity and strong heterogeneity of stratigraphic structure,as well as the limited availability of log-ging,core,and oil testing data,existing sedimentary microfacies modeling methods struggle to achieve accurate results.To address this challenge,a new modeling approach based on conditional generative adversarial networks(cGANs)was pro-posed.This method utilizes grey correlation analysis to calculate the degree of correlation between various seismic attributes and the sand-to-ground ratio,thereby identifying attributes with strong predictive relevance.These selected seismic attribute images are then used as inputs to a convolutional neural network,which is employed to construct a prediction model for the sand-to-ground ratio.The resulting predictions are visualized as a thermal map,which,combined with well log phase dia-grams,serves as a joint constraint for training the generative adversarial network.Based on this,a sedimentary microfacies generation model is developed to enable accurate modeling of sedimentary microfacies.This method was applied to a case study of an oilfield in eastern China.The results demonstrate that the cGAN-based model can effectively capture complex ge-ological patterns,achieving a well-point coincidence rate of 94.1%.关键词
条件生成对抗网络/深度学习/沉积微相/砂地比/灰色关联/卷积神经网络Key words
conditional generative adversarial network/deep learning/sedimentary microfacies/sand-to-ground ratio/grey correlation/convolutional neural network分类
信息技术与安全科学引用本文复制引用
刘昕,孙胜,张立强,蔡明俊,鲁玉,卢文娟..地震属性驱动的条件生成对抗网络沉积微相模型构建[J].中国石油大学学报（自然科学版）,2025,49(4):1-10,10.基金项目
山东省自然科学基金项目(ZR2024MF037) （ZR2024MF037）
