Methane emissions have a significant impact on increasing global warming. Satellite-based methane detection methods can help mitigate methane emissions, as they provide a constant and global detection. The Sentinel-2 constellation, in particular, offers frequent and publicly accessible images on a global scale. We propose a deep learning approach to detect methane plumes from Sentinel-2 images. We construct a dataset of 5200 satellite images with identified methane plumes, on which we train a U-Net model. Preliminary results demonstrate that the model is able to correctly identify methane plumes on training data, although generalization to new methane plumes remains challenging. All code, data, and models are made available online.