KDD Papers

Learning to Generate Rock Descriptions from Multivariate Well Logs with Hierarchical Attention

Bin Tong (Research and Development Group, Hitachi, Ltd.);Martin Klinkigt (Research and Development Group, Hitachi, Ltd.);Makoto Iwayama (Research and Development Group, Hitachi, Ltd.);Toshihiko Yanase (Research and Development Group, Hitachi, Ltd.);Yoshiyuki Kobayashi (Research and Development Group, Hitachi, Ltd.);Anshuman Sahu (Big Data Laboratory, Hitachi America, Ltd.);Ravigopal Vennelakanti (Big Data Laboratory, Hitachi America, Ltd.)


In the shale oil and gas industry, operators are looking toward big data analytics to optimize operations and reduce cost. In this paper, we mainly focus on how to assist operators in understanding the subsurface formation, thereby helping them make optimal decisions. A large number of geology reports and well logs describing the sub-surface have been accumulated over years. Issuing geology reports is more time consuming and depends more on the expertise of engineers than acquiring the well logs. To assist in issuing geology reports, we propose an encoder-decoder-based model to automatically generate rock descriptions in human-readable format from multivariate well logs. Due to the different formats of data, this task differs dramatically from image and video captioning. The challenges are how to model structured rock descriptions and leverage the information in multivariate well logs. To achieve this, we design a hierarchical structure and two forms of attention for the decoder. Extensive validations are conducted on public well data of North Dakota in the United States. We show that our model is effective in generating rock descriptions. These forms of attention enable the provision of a better insight into relations between well-log types and rock properties with our model from a data-driven perspective. This research is expected to be integrated into a customized solution for Hitachi regarding shale oil and gas.