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 ma...
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ISBN:
(纸本)9781538627150
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.
Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all ...
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ISBN:
(纸本)9781509059102
Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be combined to boost sample size for increased denoising performance. Simplest of networks can reconstruct images with corruption levels so high that noise and signal are not differentiable to human eye.
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