Intelligent monitoring technology for the water flow of oil drilling can realize the automatic monitoring of gaseous pollutant from oil drilling and minimize the cost of manual monitoring to the greatest extent. The existing feature extraction methods cannot describe the change process of water flow, it is difficult to obtain abnormal samples and fully enumerate them, and the fusion layer information is not fully utilized. In order to solve the problems, a new water flow abnormal data detection algorithm was proposed. Firstly, a new feature representation method named shape flow was proposed. Then, the classic anomaly detection unsupervised neural network GANomaly was optimized into a residual structure. Finally, a feature fusion layer was added to the GANomaly to improve the learning ability of neural network. Experimental results show that, the detection accuracy of the improved algorithm reaches 95%, which is 5 percentage points higher than that of the GANomaly algorithm. The proposed algorithm can be applied to the detection of abnormal water flow data in different scenarios, and can overcome the influence of fog on the experimental results.