The application of improved NeuroEvolution of Augmenting Topologies neural network in Marcellus Shale lithofacies prediction

Authors:
Guochang Wang;Guojian Cheng;Timothy R. Carr
Affiliations:
Department of Geology & Geography, West Virginia University, Morgantown, WV 26506, USA and College of Earth Science, University of Chinese Academy of Sciences, Beijing 100049, China;School of Computer Science, Xi'an Shiyou University, Xi'an, Shaanxi 710065, China;Department of Geology & Geography, West Virginia University, Morgantown, WV 26506, USA
Venue:
Computers & Geosciences
Year:
2013

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Abstract

The organic-rich Marcellus Shale was deposited in a foreland basin during Middle Devonian. In terms of mineral composition and organic matter richness, we define seven mudrock lithofacies: three organic-rich lithofacies and four organic-poor lithofacies. The 3D lithofacies model is very helpful to determine geologic and engineering sweet spots, and consequently useful for designing horizontal well trajectories and stimulation strategies. The NeuroEvolution of Augmenting Topologies (NEAT) is relatively new idea in the design of neural networks, and shed light on classification (i.e., Marcellus Shale lithofacies prediction). We have successfully enhanced the capability and efficiency of NEAT in three aspects. First, we introduced two new attributes of node gene, the node location and recurrent connection (RCC), to increase the calculation efficiency. Second, we evolved the population size from an initial small value to big, instead of using the constant value, which saves time and computer memory, especially for complex learning tasks. Third, in multiclass pattern recognition problems, we combined feature selection of input variables and modular neural network to automatically select input variables and optimize network topology for each binary classifier. These improvements were tested and verified by true if an odd number of its arguments are true and false otherwise (XOR) experiments, and were powerful for classification.