Abstract: At present,reservoir engineering and production engineering analyze anomalies separately, using specialized data and information systems of their disciplines. In such cases, the complex correlation between the reservoir and well systems is somewhat simplified and thus the resultant diagnosis of production anomalies is inadequate and the proposed treatment lacks pertinence. Given this, the oil reservoir-well-integrated intelligent diagnostic model, based on the ensemble learning consisting of the random forest and convolution neural network (CNN), was developed. For different oil reservoir and well anomalies such as ineffective water injection and pump leakage, the decision-making tree based on the random forest was used to investigate abnormal conditions of oil reservoirs, the CNN was adopted to diagnose well anomalies, and finally, the integrated diagnosis was produced by integrating the two classifiers via ensemble learning. The field validation of the diagnosis showed the presented method improves the performance and normalization capacity of the constituent classifier via ensemble learning and the integrated diagnosis of reservoir and well anomalies is delivered, with an application accuracy of over 90%. This invention can strongly support the intelligent management of oilfields.