Oreonet: Deep Convolutional Network For Oil Reservoir Optimization

In recent years, deep convolutional networks have been successfully used for the tasks of image classification and speech recognition. The highly non-linear modeling combined with its emphasis on local connectivity makes them highly suitable for such tasks. However, their performance in other domains is not well explored. Specifically, in the oil industry, researchers use manual features from time series data as input to various machine learning models. In this paper, we employ deep convolutional autoencoders to extract non linear latent features from time series data. We propose a novel deep network architecture and show its efficacy in two oil field tasks related to reservoir optimization - steam job prediction and slippage detection. We show that our architecture outperforms state-of-the-art methods significantly on steam job prediction. We demonstrate the success of our model on an oil field dataset which consists of production and failure data of over two years. Our architecture achieves a precision of 98% for precision@50 in steam job prediction, and 25% improvement over the methods used in the industry. To the best of our knowledge, we are the first to attempt to automatically detect slippage failures in well pumps. We are able to classify slippage events with 70.3% accuracy, a 10.6% improvement over using manually defined input features.