数据挖掘技术在钻头优选中的应用

Application of data mining technology in bit selection

杨明合1袁2 翟应虎2 夏宏南

长江大学石油工程学院，湖北荆州434023 中国石油大学石油天然气工程学院，北京102200

Schol of PetroleumEngineering,Yangtze
University,Jingzhou 434102 College of Petroleum and
Natural Gas Engineering, China University of Petroleum,
Beijing 102200,China

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
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 incrase greatly at the same time. The results of
caculating show that the seleting bits with the data after processing
are more reasonably than prvious bits. The data mining method
should be counted because the data are huge in the field of drilling.