基于量子LM神经网络和粗糙集的石油储层识别方法研究

提出一种量子LM（Levenberg Marquardt，LM）神经网络与粗糙集相结合的智能识别方法，以替代传统的统计识别方法和工程应用中以单一智能控制为基础的识别方法．基于LM神经网络的技术方案可以整理测井定位数据，提高预测的准确性；量子计算具有并行和类映射的优势；通过削减冗余信息和简化信息量，粗糙集可以降低量子LM神经网络的复杂性，缩短数据处理时间，削减神经网络的负担．通过在石油储层识别实践中的应用证明：该方法可以有效提高计算速度和识别精度，降低成本．

Research on oil reservoir identification method based on quantum LM neural network and rough set

An intelligent identification method of oil reservoir based on quantum Levenberg-Marquardt(LM) neural network and rough set is presented,substituting traditional statistical identification methods and the method based on a single intelligent control in engineering applications.The technical solutions based on LM neural network can organize the logging location data to improve the accuracy of prediction.Quantum calculation has the advantages of parallel and class mapping.By reducing the redundant information and simplifying the information content,rough set can reduce the complexity of quantum LM neural network,shorten the data processing time and lighten the burden of neural network.The method is applied to the practice of oil reservoir identification,and it is proved that it can improve the calculating speed and identification precision,and reduce the cost.