摘要
针对油田异常井诊断的问题,提出基于反馈动态神经网络的模型,该模型具有适应性强、学习效率高等特点。结合粒子群算法弥补其训练速度慢和容易陷入局部最小的缺点,给出模型及算法的优化原则和实现技术。最后根据实际问题,进行油田异常井诊断模型的具体应用,实验结果证明模型对于异常井诊断具有较高准确性及可行性。
According to oilfield abnormal well,this paper proposed a dynamic feedback neural network model,which has the characteristics of strong adaptability and higher learning efficiency.Combined with the particle swarm algorithm to compensate for its slow training speed and falling easily into local minimum points,it gave the principle of optimization model and algorithm and implementation technology.Finally,according to the actual problem,this papers carried on the concrete application of diagnosis model for oilfield abnormal well,and the experimental results show that the model for abnormal well has higher diagnostic accuracy and feasibility.
出处
《计算技术与自动化》
2015年第2期114-116,共3页
Computing Technology and Automation
关键词
反馈动态神经网络
粒子群算法
异常井
feedback dynamic neural network particle swarm algorithm abnormal well
