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Through the realization of intelligent monitoring technology for the water flow of the drilling pipeline can solve the problem of automatic monitoring of polluted gas from oil drilling and minimize the cost of manual monitoring. However, there are still several difficulties that need to be overcome: (1) The traditional feature extraction method cannot describe the change process of the water flow pattern; (2) Because the probability of abnormal situations is very low, the method of full supervision with rare abnormal samples is not applicable. In order to solve the problem of feature extraction, proposes a new feature extraction method based on image segmentation-morphological flow, which can describe the change of water flow morphology in time series; on the other hand, in order to overcome the problem of rare abnormal samples, an unsupervised method-multivariate Gaussian modeling is used to determine whether the water flow data is normal. Experiments show that the detection accuracy of the algorithm in the water flow abnormal data detection task reaches 93.6%, and the processing speed of 28 frames per second can be reached when using GPU parallel acceleration, and it can accurately detect abnormal data frames in the water flow data.