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

• Published in: Computers & geosciences Vol. 54; pp. 50 - 65
• Main Authors: Wang, Guochang, Cheng, Guojian, Carr, Timothy R.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2013
• Subjects: basins; computer hardware; computers; environmental engineering; genes; Lithofacies prediction; Marcellus Shale; mineral content; NEAT; neural networks; Node location; organic matter; Organism population size evolution; population size; prediction; RCC; shale; RCC; Node location; Lithofacies prediction; NEAT; Organism population size evolution; Marcellus Shale
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2013.01.022

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. ► Introduce NEAT neural network into shale lithofacies prediction. ► Improve the efficiency of NEAT by proposing two new attributes: node location and flag of recurrent connection. ► Incorporate growth of population size to NEAT. ► Predict Marcellus Shale lithofacies by conventional logs using NEAT network.