针对小壕兔-大16井区储层结构复杂、有利区预测困难的问题,运用贝叶斯挖掘算法,利用计算机辅助模拟手段,构建研究区精细水力单元模型,探明储层砂体发育规律。研究结果表明,基于贝叶斯推论的表征技术能高效识别储层水力单元类型,为气藏描述提供准确的地质信息;指数函数递增法在测井曲线划段过程中表现更加出色,Rt测井段间样点的聚簇规律归因于储层的高含水特性;3类水力单元具有较大的原始油气储量和潜力,亦是提高气藏采收率过程中亟待突破的对象。该研究成果对预测储层有利区具有重要参考价值。
Aiming at the problem of complex reservoir structure and difficult prediction in Xiaohaotu-Da16 well area, Bayesian mining algorithm and computer-aided simulation method were used to build the fine hydraulic unit model and correlate the development law of reservoir sand bodies in the target area. The results show that the characterization technology based on Bayesian inference can effectively identify the types of reservoir hydraulic units and provide accurate geological information for gas reservoir description. The exponential function increment method performs better in the process of well logging curve segmentation. The clustering rule of the samples in Rt logging interval is attributed to the high water-cut characteristics of the reservoir. The three types of hydraulic units have large original oil and gas reserves and potential, and they are also urgent to be broken through in EGR process. The research results provide reference for predicting favorable reservoir areas.
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