Automatic 3D fault detection and characterization — A comparison between seismic attribute methods and deep learning

Abstract

Seismic interpretation is crucial for identifying faults, fluid concentrations, and flow migration pathways in the oil and gas industry. Algorithms have been developed to identify faults using seismic data and attributes such as changes in amplitude, phase, polarity, and frequency. Despite technological advancements, challenges remain in seismic interpretation due to noise, quality of data, and fault dimensions. Deep learning has recently been applied to image faults from seismic data, making the process faster and more reliable. This paper evaluates the performance of deep neural networks (DNN) in fault interpretation by comparing the results with traditional seismic attributes in onshore seismic data. Our results indicate that the DNN reveals more structural detail, which is essential in characterizing 3D fault geometry. In addition, DNN results show better continuity, fewer false positives, and are less affected by noise in the onshore seismic data used in this case. The 3D fault model from DNN identifies faults and their fault segments with greater variability of strikes and reveals more minor faults. Based on the DNN fault model, we characterized the 3D geometry of a new fault in the Rio do Peixe Basin without noise influence.