Abstract: Aspects of the technology described herein make legacy hydrocarbon studies accessible to modern computer analysis. Whatever the initial format, the technology described herein analyzes the studies to identify characteristics that are interesting to people who study hydrocarbon environments. As an initial process, various segments within a hydrocarbon study received by the technology described herein are identified. The various segments can include text, maps, charts, and tables. Within each of these segments, specific types of text segments, maps, charts, and tables may be identified. For each segment identified, characteristics of interest may be determined through analysis of the segment. In one aspect, segment-specific analysis is performed on each type of segment. Different technologies may be used for different segments.

Abstract: Aspects of the technology described herein identify geologic features within seismic data using modern computer analysis. An initial step is the development of training data for the machine classifier. The training data comprises an image of seismic data paired with a label identifying points of interest that the classifier should identify within raw data. Once the training data is generated, a classifier can be trained to identify areas of interest in unlabeled seismic images. The classifier can take the form of a deep neural network, such as a U-net. Aspects of the technology described herein utilize a deep neural network architecture that is optimized to detect broad and flat features in seismic images that may go undetected by typical neural networks in use. The architecture can include a group of layers that perform aspect ratio compression and simultaneous comparison of images across multiple aspect ratio scales.