A deep learning framework for seismic facies classification
Harpreet Kaur, Nam Pham, Sergey Fomel, Zhicheng Geng, Luke Decker, Ben Gremillion, Michael Jervis, Ray Abma, Shuang Gao
Abstract
Abstract We have proposed a deep neural network-based framework for seismic facies classification. We implement two different neural networks based on the architectures of DeepLabv3+ and generative adversarial network for segmentation and compare the mapping results from seismic reflection data to lithologic facies. DeepLabv3+ predictions have sharper boundaries between the predicted facies whereas generative adversarial network output has a better continuity of predicted facies. We incorporate uncertainty analysis into the workflow using a Bayesian framework. The proposed approach consisting of joint analysis of predicted facies from multiple networks along with uncertainty in prediction accelerates the interpretation process by reducing the need for human intervention and also lessens individual biases that an interpreter may bring. We determine the effectiveness of the proposed algorithm by testing on field data examples, and we find that the proposed workflow classifies facies accurately. This may potentially enable the development of depositional environment maps in areas of low well density.