基于水流分割的石油钻井水流异常检测

通过对钻井管道水流的智能监控技术实现,可以解决石油钻井污染气体的自动监测问题,最大程度的减少人工监测成本;但是依然有以下几个难点需要攻克:(1)传统的特征提取方式不能描述水流形态的变化过程;(2)因为异常情况发生的概率很低,所以异常样本稀少,全监督的方法不再适用;为解决特征提取问题,提出了一种基于图像分割的新特征特提取方式——形态流,形态流可以从时序上描述水流形态的变化;另一方面,为克服异常样本稀少的问题,通过无监督的方式——多元高斯建模,来判别水流数据是否正常;实验表明在水流异常数据检测任务中算法检测精度达到了93.6%,在使用GPU并行加速处理时可达到28帧每秒的处理速度,能够准确地检测出水流数据中的异常数据帧。

基于形态流的石油钻井水流异常检测

针对石油钻井水流的智能监控技术,可以实现石油钻井污染气体的自动监测,并最大限度地减少人工监测成本。针对现有特征提取方法不能描述水流形态变化过程,异常样本获取困难且不能完全列举,以及没有充分利用融合层信息的问题,提出了一种水流异常数据检测算法。首先,提出了一种新特征表示方法——形态流;然后,将经典的异常检测无监督神经网络GANomaly优化为残差结构;最后,在GANomaly的基础上增加了特征融合层,从而提升神经网络的学习能力。实验结果表明,改进后的算法检测精度达到了95%,相较GANomaly算法提升了5个百分点。所提算法能适用于不同场景下的水流异常数据检测,并能克服雾气对实验结果的影响。

Numerical simulation of deposit in confluence zone of debris flow and mainstream

Abundant solid materials were formed as a result of landslide and collapse due to Wenchuan earthquake.The solid source around mountains would form a debris flow when appropriate rain condition occurs.Such a debris flow is structurally very large and strong,and the river flow can hardly wash away the deposit when the debris flow enters into the mainstream.As a result,the deposit on the river bed due to debris flow will cause a series of hazards.Based on the previous researches and relevant data,this paper simplified the interaction between debris flow and current of the main river,and adopted the finite element characteristic-based-split algorithm which is favorable to the stabilization of dealing with the convection.Finally,the numerical model of the confluence of debris flow deposit and main river was developed,and the deposit progress of the mega-debris flow from Wenjiagou in Mianyuan river was reproduced.Furthermore,the influence of the deposit on the flow route of the main river,and distribution of velocity and water depth were analyzed.The results showed that the simulation deposit terrain qualitatively agreed with the field data through comparison,including the deposit area and depth distribution.Furthermore,the improvement of the model in future was discussed.