裂缝型生物灰岩油藏储层具有结构复杂、非均质性强和双孔隙网络特征等问题，导致缺乏针对生物灰岩的裂缝识别技术方法，因此认识和评价该类储层的裂缝发育情况是一大难题。以王徐庄油田为例，在岩心资料、计算机断层扫描（computed tomography, CT）、铸体薄片和常规测井曲线、成像测井（FMI）成像等多种资料的基础上，构建裂缝型储层预测模型：通过岩心照片、薄片和CT对比识别出“微”裂缝，总结不同产状裂缝的双侧向电阻率测井响应特征，根据电阻率幅差较大等明显特征发现生物灰岩相发育微裂缝；构建基于非线性运算的反向传播（back propagation, BP）人工神经网络，划分单井裂缝型储层；采用综合概率指数法实现全区裂缝储层分布预测，对比总结裂缝发育处与不发育处油气产量特点。研究成果为裂缝发育与油气产量的关系研究提供理论依据。

Fractured biological limestone reservoirs are characterized by structural complexity, strong inhomogeneity, and dual pore networks, resulting in a lack of technical methods for fracture identification for biological limestone, so it is a big problem to recognize and evaluate the fracture development of this type of reservoir. Wang Xuzhuang oilfield was taken as an example, based on various data such as core data, computed tomography(CT), injecting thin sections conventional logging curves and imaging logging(FMI) imaging, fracture reservoir prediction models were constructed. The "micro" fracture was identified through the comparison of the core photo, thin section and CT, then the response characteristics of dual laterolog for different production fractures were summarized and micro-fractures could be found in the biological limestone phase of the research section according to the large difference in the amplitude of the electrical resistivity. A back propagation(BP) artificial neural network was constructed based on nonlinear operations, the single-well fractured reservoirs were delineated. The comprehensive probabilistic index approach was adopted to realize the prediction of the distribution of fractured reservoirs in the whole region. The characteristics of oil and gas production at the fracture development and non-development places were compared and summarized. The research results provide theoretical basis for the research on the relationship between fracture development and oil and gas production.