I-Corps: A Deep Learning Toolbox for Subsurface Imaging in Exploration for Oil and Gas

University Of Houston, Houston TX

Investigators

Abstract

The broader impact/commercial potential of this I-Corps project is to meet the increasing demand of energy consumption and maintain a healthy oil and gas output in the US. Machine vision and automatic data picking are viable approaches to keep up with the data multiplication of seismic surveys. The proposed product is the first one that is aiming at minimizing labor burden in seismic data processing by using a deep neural network with a novel and efficient transfer learning strategy. If successful, the novel deep transfer learning approach will build the foundation for adopting advanced deep neural network technologies by many other industrial applications. In addition to the Oil and Gas industry, the product can also be used in other important areas such as geothermal energy exploration. This I-Corps project is to explore the market potential for a product to automatically identify unique patterns embedded in the seismic data. The intellectual merit of this proposal lies in the theme of a novel deep transfer learning approach, which will ease the training burden of the deep neural network and solve the training data shortage problem by utilizing discriminative unsupervised feature learning to learn high-level representations that are more invariant to variations between the synthetic and real data. The innovativeness of our proposal is that it is aimed to build a reliable system to perform various pattern recognition tasks required by the seismic data processing and be adaptive to different datasets acquired through seismic surveys. This product has the potential to save up to 20% to 50% of the total seismic processing time. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.