摘要
裂缝系统是个复杂的地质体,其储层物性的改善作用是非线形的,各种评价参数与裂缝发育程度之间的关系也是非线形的。基于人工神经网络理论,开展了常规测井资料识别评价裂缝的研究。结果表明,基于BP神经网络的裂缝性储集层常规测井识别,与成像测井对比具有较好的应用效果。
Fracture system is a complicated geologic body.The improvement effect of reservoir physical property is nonlinear and the relationship between the various evaluated parameters and the extent of fractures growing is nonlinear too.There are the subjective uncertainty and ambiguity factors for using the routine logging data to recognize fractures.Although the imaging logging are intuitive and accurate,the cost are very high.The result shows that fractures identifying based on BP neural network has preferable effect compared with imaging logging.
出处
《断块油气田》
CAS
2007年第2期60-62,共3页
Fault-Block Oil & Gas Field
关键词
裂缝识别
常规测井
成像测井
人工神经网络
fracture identification,routine logging,imaging logging,artificial neural network.
