摘要
针对钻井过程的复杂性、不确定性等特点,提出了基于人工神经网络钻井事故预测与诊断模型.以钻井过程工况参数构成神经网络的输入特征向量,以钻井过程正常运行模式及常见事故模式的监测数据作为训练及检验样本,根据钻井事故诊断特点,确定了神经网络的结构与参数,采用改进算法和学习规则,实现对神经网络系统的训练和模拟,建立能够准确预测事故的神经网络模型.该方案的提出可使现场工作人员及时监测钻井过程,降低事故发生率,节约钻井成本,提高效率.
Because of the complexity and uncertainty of drilling process,it is proposed that the prediction and diagnosis of drilling accidents are carried out by artificial neutral network.The operating parameters of drilling process are taken as the inputs of the network.The monitoring data of the normal drilling condition and the common fault conditions in drilling process are taken as the learning and test samples.The structure and parameters of the network are determined based on the characteristics of the fault diagnosis of the drilling process and the abundant data from drilling spot.The improved algorithm and learning rule are used to train and stimulate the network system.The established neural network model can precisely forecast and diagnose the common drilling accidents.The presented method can make the field workers monitor drilling process in time,which will decrease accident rate and drilling cost,and increase drilling efficiency.
出处
《西安石油大学学报（自然科学版）》
CAS
2008年第2期99-102,共4页
Journal of Xi’an Shiyou University（Natural Science Edition）
基金
陕西省自然科学基金项目"石油钻井过程安全预警与多源信息融合智能监控技术研究(编号:2006E12)"
中石油科技中青年创新基金"钻井安全诊断及主动防范系统网络控制技术平台构建(编号:05E7040)"
陕西省教育厅专项科研计划项目"基于信息融合的钻井过程事故智能监控与预警技术(编号:07JK365)"
关键词
神经网络
钻井过程
工况识别
事故诊断
neural network
drilling process
recognition of operating condition
diagnosis of fault
