Insights using machine learning in predicting faults and horizons: A case study onshore Texas

The application of machine learning (ML) technology to seismic interpretation has greatly improved both workflow efficiencies and product quality. Advancements in speed, precision, and accuracy leads to valuable insights in the seismic data and ensure a better understanding of our subsurface geology and drilling targets. In this paper, we apply machine learning technology in predicting faults and horizons in a structurally and geologically complex onshore Texas dataset. By employing ML technology through convolutional neural networks (CNNs) trained on real data we predict multiple layers of faults from small-scale to regional displacements within the study area. In addition, using separate CNNs with sparse local labels, we predict and extract two high-resolution stratigraphic horizons of the middle Frio and top Wilcox events. These horizons consistently track on the proper signal amplitude and are extracted in a fraction of time compared to manual effort. With the assistance of ML, we can automate the fault and horizon predictions which dramatically reduce the picking time to enable interpreters to focus on local complex areas, assist in generating more accurate horizons, accelerate the process toward exploration, understand the reservoir compartmentalization and provide valuable information to de-risk drilling and improve well placement decision making.