【摘要】 大量数据统计结果表明，孔隙度与渗透率之间的关系密切，但传统基于孔隙度-渗透率经验公式预测的渗透率误差较大，难以满足复杂储层的渗透性预测需求。为此，利用人工智能可以挖掘数据隐含关系的优势，借助多任务学习共享机制有效缓解单任务学习在小样本条件下的过拟合问题，提出了一种基于多任务学习的储层渗透性地震预测方法。该方法以叠后地震数据和纵波阻抗数据为网络的输入数据，将测井孔隙度和渗透率作为网络的标签数据，通过网络训练构建井旁道地震数据与测井数据的网络模型，实现井间储层孔隙度与渗透率的同步预测。应用该方法对四川盆地金秋气田沙溪庙组致密气藏8号砂体的渗透性进行了预测，预测结果与实钻井数据吻合程度高，且具有较高的纵、横向分辨率，验证了方法的有效性。更多还原

【Abstract】 A large amount of statistical data has demonstrated a close correlation between porosity and permeability. However, traditional permeability prediction methods based on porosity and permeability empirical formulas exhibit significant errors and fail to meet the requirements of predicting the permeability of complex reservoirs. The advantage of artificial intelligence can be leveraged to explore the hidden relationship of data,and the multi-task learning and sharing mechanism can effectively alleviate the overfitting problem of single-task learning under the few-shot condition. In this paper,a seismic prediction method of reservoir permeability based on multi-task learning was proposed. The method employed post-stack seismic data and P-wave impedance as network inputs,with well-log porosity and permeability serving as labeled data of the network.Through network training,an optimal network model was established by integrating near-well seismic and well-log data,realizing the simultaneous prediction of reservoir porosity and permeability parameters between wells. Application results from the tight gas reservoir in the Shaximiao Formation of Jinqiu Gas Field,Sichuan Basin demonstrated high consistency between predicted permeability parameters of Sand Body No. 8 and actual drilling data, along with superior vertical and horizontal resolution. This validated the method’s effectiveness for spatial permeability prediction using seismic data.更多还原