数据挖掘技术在钻头优选中的应用  

Application of data mining technology in bit selection

作　　者：杨明合[1] 翟应虎[2] 夏宏南[1] 

机构地区：[1]长江大学石油工程学院,湖北荆州434023 [2]中国石油大学石油天然气工程学院,北京102200

出　　处：《断块油气田》2007年第6期60-62,共3页Fault-Block Oil & Gas Field

摘　　要：应用基于粗糙集理论的数据挖掘技术对数量巨大的钻头数据资料进行处理,能在保留关键信息的前提下,对钻头数据进行约简并求得知识的最小表达,去除冗余信息。这样在使用人工神经网络优选钻头时,网络的训练样本数和训练步数都有较大减少,而迭代精度却明显提高;实例计算表明,使用约简后的样本数据进行钻头选型,优选的钻头序列更加合理;由于钻井行业涉及的数据量巨大,这种数据挖掘技术应该得到足够重视。The huge bit data are processed with the data mining based on the rough sets theories. This method can obtain minimal expression of the bit datum with maintaining key information. The nerve network trining data will decrease largerly and the iterating accuracy degree of nerve network will increase greatly at the same time. The results of caculating show that the seleting bits with the data after the processing are more reasonably than prvious bits. The data mining method should be counted because the data are huge in the field of drilling.

关 键 词：数据挖掘 粗糙集合 钻头优选 神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]