Thilo Wrona

Seismic facies analysis using machine learning

Thilo Wrona¹, Håkon Fossen¹, Robert L. Gawthorpe¹, Indranil Pan²

¹Department of Earth Science, University of Bergen, Norway

²Department of Earth Science and Engineering, Imperial College London, UK

Seismic interpretations are, by definition, subjective and often require significant time and expertise from the interpreter. We demonstrate that machine learning techniques can help address these problems by performing seismic facies analyses in a rigorous, reproducible 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 1000 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 and reproducibility of seismic facies analyses.