Machine Learning Assisted Seismic Interpretation: a Case Study of the Loppa High Area, Barents Sea

Abstract

The interpretation of faults and horizons from seismic data forms a critical part of the geoscience workflow, enhancing our understanding of the subsurface and ultimately the chance of success in extracting hydrocarbons. The depiction of these vital seismic interpretations has long been restricted to conventional manual and semi-automatic techniques, which require geoscientists to work line by line over ever-expanding volumes of seismic data. The advent of machine learning (ML) and cloud computing technology has revolutionised tasks across multiple industries, enabling the identification of patterns in large multi-factor datasets. In this case study, we predict fault locations using 2D U-Net architecture convolutional neural network (CNNs) and predict horizons by employing Radial Basis Functions (RBFs) and Neural Networks (NNs). We aim to demonstrate the gains in efficiency and geological insight found using ML technology as the bedrock of the seismic interpretation workflow, through the interpretation of a broadband seismic dataset from the Loppa High area, Barents Sea.