We illustrate novel application of Machine Learning (ML) assisted fault interpretation in the Middle East, interpreting complex fault structures with subtle throws in a large carbonate field onshore Abu Dhabi. Introduced as part of an integrated multi-disciplinary digital excellence initiative at ADNOC, ML-assisted fault interpretation seeks to overcome historic operational bottlenecks caused by traditional seismic interpretation methods which are slow, labour intensive, repetitive, and subjective. Core objectives for deploying ML-assisted fault interpretation were to reduce evaluation time, improve interpretation accuracy, and ensure integration across an intelligent evaluation ecosystem comprised of various disciplines. Envisaged gains from deploying ML-assisted fault interpretation methodology included effective and efficient utilization of multiple seismic datasets to drive rapid multi-scenario analysis, leading to better subsurface understanding within much shorter time frames. Input data used of the project was standard amplitude volume with minimal user-end conditioning. PSTM time and PSDM depth seismic volumes were used in separate runs to confirm that applied ML technology is domain agnostic. The ML-Assisted workflow included: Generating a fault prediction cube based on user-supplied fault interpretation labels made on 6 training lines ( Despite limited fault displacement apparent on the seismic volumes, ML fault predictions were of high quality, closely adhering to the seismic response as guided by user-provided training samples. Advantages envisaged from use of ML-assisted interpretation technology in the project were fully realized as the technology enabled rapid extraction of complicated fault structures within a fraction of the time and effort previously taken using traditional means. Efficiency and precision gains from using ML-assisted fault interpretation presents benefits that single seismic volumes can be evaluated thoroughly, and multiple seismic datasets (e.g. various azimuthal volumes) can be evaluated consistently for multi-scenario analysis to reduce subsurface risk and inform better decisions at all phases of the E&P Asset lifecycle.