BP神经网络在电潜泵井故障诊断中的应用

Application of BP neural network in failure diagnosis of ESP production wells

甘露 王仪 王波

GAN Lu WANG Yi WANG Bo

北京奥伯特华能科技发展有限公司，北京　100084 中国地质大学，北京　100083

Optimization Petroleum Technologies, Inc., Beijing 100084, China China University of Geosciences, Beijing 100083, China

电流卡片是诊断电潜泵井故障诊断的主要依据，目前主要由技术人员凭经验手动完成，难以实现快速大批量诊断，诊断结果准确性受工程师技术水平与工作状态影响较大。提出了应用BP神经网络进行电潜泵故障诊断的方法，首先集合专家经验，对不同工况电流卡片的特征进行分析，得到特征样本库。然后对样本库进行学习训练，得到神经网络计算所需的权值和阈值，并进行保存。将要诊断的电流卡片的特征数据输入，运用BP神经网络识别算法计算得到相似度。最后根据相似度的大小找出与样本库中最接近的故障类型作为该潜油电泵的故障类型。通过编程应用，证明提出的方法可以准确、快速、批量地诊断电潜泵井工况，可推广应用。

Traditionally, technical engineers often rely on their experience to manually diagnose ESP wells failure according to electrical current charts which is the main evidence for ESP wells diagnose. This makes it hard to achieve batch diagnosis, and the diagnosing accuracy is significantly affected by the technical level and working state. BP neural network technology applied to diagnosing ESP wells failure is addressed in this paper. First, characteristics of electrical current charts under different conditions has been analyzed to obtain the characteristic sample by integrated experts experiences; second, the necessary weights and threshold values for neural network calculation are given by learning and training the sample set; and then been saved; third, the character data of electrical current cards which need to be analysis is putting to calculate the similarity by using the BP neural network identification method; finally, the failure type of ESP is determined by comparing the calculated similarity to the most nearly sample.The computer program of this technology is used on site, and application results has proved that the method is reliable and robust, and can be utilized to batch diagnose ESP well failures correctly and quickly.