Insights using machine learning in predicting faults and horizons: a case study onshore Texas Dan Ferdinand Fernandez, Mustafa Karer, Richard Hearn, Ryan King, Sunil Manikani, Gavin Menzel-Jones Digital and Integration, Schlumberger Summary 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. Introduction Seismic interpretation is a crucial activity in energy exploration and production where the seismic interpreter constructs three-dimensional representations of the subsurface of seismic surveys. To accurately model the subsurface, a skilled interpreter depends heavily on the understanding of their data and their expertise in mapping out and identifying the structural and stratigraphic elements related to well information. Only when the important horizons and faults are completely delineated can modelling commence for the process of data evaluation, exploration, risk assessment, and well planning to ensue. The generation of precise structural and stratigraphic elements is a laborious and time-consuming endeavor. Typically, it extends between multiple days to weeks to complete the extraction of a single horizon. Similar timelines are expected when extracting swarms of faults within the same survey. For horizon interpretation, an interpreter embarks on a coarse grid, building the framework of the stratigraphic horizon extent within the seismic survey. Standard grid interpretation requires providing meticulous control points on both inline and crossline. Interpreters ensure no mis-ties in both directions. When working with a team, merge points must be mis-tie free along the polygon border and the horizon should consistently follow the same correlated event across structural discordances, slope changes, poor seismic image, and natural geologic disruptions. Once the team has completed the process on a coarse grid, the process is repeated to tighter grids. Finally, when the tightest grid is completed, surfacing follows, and line by line evaluation completes the manual intensive interpretation task. Similarly, for fault delineation and extraction, various papers discuss the use of semblance (Marfurt et al., 1998), variance (van Bemmel and Pepper, 2000), curvature (Roberts, 2001), edge detection and enhancement (Pederson et al, 2002), and fault likelihood (Hale, 2013). Others (Randen et al., 2001; Bounaim et al., 2013; Hale, 2013; Wu and Hale 2016; Etchebes et al., 2019) employed advanced structural seismic attributes with better automation for fault extraction that provided some efficiency gains. However, widespread adoption and implementation suffered due to the required proficiency in tuning parameters for optimum results. In this study, we demonstrate that by applying machine learning technology in predicting faults and horizons in a structurally complex onshore Texas dataset, we simplify the seismic interpretation process. With the assistance of ML and minimal upfront labels, the process for experts to commence on data evaluation and gain valuable insights for drilling and exploration is significantly improved and expedited. Details of study area Seismic Dataset This study consists of a land survey acquired using vibroseis and dynamite covering about ~1280 square kms. It consists of a 3D pre-stack depth migrated (PSDM) volume located within northern onshore Gulf Coast basin in South Texas. The main producing hydrocarbon formations within the 3D seismic survey are the Frio Formation, and the Anahuac Formation. The Frio Formation consists of sand-rich fluvio-deltaic systems, while the Anahuac Formation are extensive transgressive marine shale overlying the Frio Formation. The Frio Formation extend as deltaic and slope sandstones in Louisiana and Texas and carbonate rocks in the eastern Gulf of Mexico (Swanson et 10.1190/image2022-3749510.1 Page 1322 Second International Meeting for Applied Geoscience & Energy © 2022 Society of Exploration Geophysicists and the American Association of Petroleum Geologists Downloaded 09/12/22 to 192.23.26.186. Redistribution subject to SEG license or copyright; see Terms of Use at http://library.seg.org/page/policies/terms DOI:10.1190/image2022-3749510.1