Abstract: Besides the porosity, reservoir permeability is also affected by various factors such as pore shape and size. Therefore, the accuracy of reservoir permeability prediction can be improved only when considering the pore structure. As a factor for describing pore structure, shear flexibility factor plays a role in permeability prediction. But a single pore structure factor is not enough to describe the influence of complex pore structure on permeability. Based on the research idea of the shear flexibility factor, a new pore structure factor (shear Lee factor)is deduced from the rock skeleton model, which has a good linear relationship with the dominant pore size. Thus, it can approximately represent the dominant pore size. First, we combine the pore aspect ratio with the shear Lee factor into dual pore structure factors; next, the reservoir lithofacies are classified using the dual pore structure factors and GA-BP neural network; finally, based on lithofacies classification, the dual pore structure factors and GA-BP neural network are used to predict the reservoir permeability. The actual prediction results from seismic data show that the lithofacies classification and permeability prediction effect controlled by dual pore structure factors are superior to that by the single pore structure factor. The description of pore structure by the pore aspect ratio and shear Lee factor in terms of pore shape and size has considered various factors that affect the permeability, and thus can improve the accuracy of reservoir permeability prediction.

Key words: reservoir permeability, pore structure, rock skeleton model, dual pore structure factors, lithofacies classification, neural network

摘要： 除孔隙度外,储层渗透率还受到孔隙形态和孔隙尺度等多种因素的影响,因此只有在考虑孔隙结构的基础上才能提高储层渗透性预测的精度。剪切柔度因子作为描述孔隙结构的因子,在渗透率预测中发挥了一定的作用,但单个孔隙结构因子不足以描述复杂孔隙结构对渗透率的影响。根据剪切柔度因子的研究思路,从岩石骨架模型中推导出新的孔隙结构因子(剪切Lee因子),其与孔隙主尺度之间具有良好的线性关系,因此能够近似表示孔隙主尺度。将孔隙纵横比与剪切Lee因子组成双孔隙结构因子,然后利用双孔隙结构因子及遗传算法优化的神经网络(GA-BP)对储层岩相进行分类,最后在岩相分类的基础上采用双孔隙结构因子及GA-BP神经网络对储层渗透率进行预测。实际地震资料预测结果表明,基于双孔隙结构因子的岩相分类和渗透率预测效果都优于单孔隙结构因子。孔隙纵横比和剪切Lee因子从孔隙形态和孔隙尺度两方面描述孔隙结构,其考虑了影响渗透率的多种因素,因此能够提高储层渗透性预测的精度。

关键词: 储层渗透性, 孔隙结构, 岩石骨架模型, 双孔隙结构因子, 岩相分类, 神经网络