Fault detection from 3D seismic data using Artificial Intelligence

Accurate delineation and interpretation of the fault network in oil and gas reservoirs and their subsequent impact on reservoir performance is one of the most generalised components within modern basin and reservoir modelling workflows, partly because of the complexity of the task and the time constraints forever present in the G&G workflows. With the help of Artificial Intelligence, the speed and accuracy of fault delineation upon delivery of new seismic data enable regional and field assessment to benefit from the most up to date information and ensure significantly better-informed exploration and development decisions. A Foundation Network was developed to identify faults in a seismic cube. In this network the Artificial Intelligence is closely aligned with the interpreters’ way of working, allowing tightly coupled interaction as appropriate for the dataset and the individual interpreter’s workflow. Case studies show that the automated components of the AI-assisted workflow, combined with capturing the interpreter’s knowledge and experience, have demonstrated tremendous value in delineating the intricate details of a realistic subsurface, significantly reducing interpretation turnaround times while simultaneously increasing accuracy and comprehensiveness of the interpretation itself.