Abstract
Seismic interpretations are, by definition, subjective and often require significant time and expertise from the interpreter. We are convinced that machine-learning techniques can help address these problems by performing seismic facies analyses in a rigorous, repeatable way. For this purpose, we use state-of-the-art 3D broadband seismic reflection data of the northern North Sea. Our workflow includes five basic steps. First, we extract seismic attributes to highlight features in the data. Second, we perform a manual seismic facies classification on 10,000 examples. Third, we use some of these examples to train a range of models to predict seismic facies. Fourth, we analyze the performance of these models on the remaining examples. Fifth, we select the “best” model (i.e., highest accuracy) and apply it to a seismic section. As such, we highlight that machine-learning techniques can increase the efficiency of seismic facies analyses.
Highlights
Seismic reflection data are a key source of information in numerous fields of geoscience, including sedimentology and stratigraphy (e.g., Vail, 1987; Posamentier, 2004), structural geology (Baudon and Cartwright, 2008; Jackson et al, 2014), geomorphology (e.g., Posamentier and Kolla, 2003; Cartwright and Huuse, 2005; Bull et al, 2009), and volcanology (e.g., Hansen et al, 2004; Planke et al, 2005; Magee et al, 2013)
Previous applications of machine learning to seismic reflection data focus on the detection of geologic structures, such as faults and salt bodies (e.g., Hale, 2013; Zhang et al, 2014; Guillen et al, 2015; ArayaPolo et al, 2017; Huang et al, 2017) and unsupervised seismic facies classification, in which an algorithm chooses the number and types of facies (e.g., Coléou et al, 2003; de Matos et al, 2006)
Early studies primarily used clustering algorithms to classify seismic data (e.g., Barnes and Laughlin, 2002; Coléou et al, 2003), recent studies focus on the application of artificial neural networks
Summary
Seismic reflection data are a key source of information in numerous fields of geoscience, including sedimentology and stratigraphy (e.g., Vail, 1987; Posamentier, 2004), structural geology (Baudon and Cartwright, 2008; Jackson et al, 2014), geomorphology (e.g., Posamentier and Kolla, 2003; Cartwright and Huuse, 2005; Bull et al, 2009), and volcanology (e.g., Hansen et al, 2004; Planke et al, 2005; Magee et al, 2013). Previous applications of machine learning to seismic reflection data focus on the detection of geologic structures, such as faults and salt bodies (e.g., Hale, 2013; Zhang et al, 2014; Guillen et al, 2015; ArayaPolo et al, 2017; Huang et al, 2017) and unsupervised seismic facies classification, in which an algorithm chooses the number and types of facies (e.g., Coléou et al, 2003; de Matos et al, 2006). Early studies primarily used clustering algorithms to classify seismic data (e.g., Barnes and Laughlin, 2002; Coléou et al, 2003), recent studies focus on the application of artificial neural networks (e.g., de Matos et al, 2006; Huang et al, 2017). To demonstrate the strength of these advanced algorithms, this study compares 20 different classification algorithms (e.g., K-nearest neighbor, support vector machines, and artificial neural networks)
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