低阻油藏是一种非常规油藏 ,电性与物性之间存在着一种模湖的非线性关系。如何评价它的产状是研究和生产的难点。本文从分析油井的产能出发 ,导出了影响油藏产状的因素。并利用人工神经网络具有自适应、自学习的特点 ,将神经网络与常规测井、试油、试井等动态资料相结合进行油藏产状评价 ,取得了较好的效果。 Low-resistivity reservoir is an unconventional reservoir, and there is a nonlinear relationship between the electrical and physical properties of a model lake. How to evaluate its production is the difficulty of research and production. Based on the analysis of oil well productivity, this paper deduces the factors that affect the reservoir shape. The artificial neural network (ANN) has the characteristics of self-adaptation and self-learning. The neural network is combined with the conventional logging, oil testing and well testing to make the evaluation of reservoir shape. The good results are obtained.