oa Unravel Complex Strike-Slip System in Frontier Collision Margin of Banggai-Sula Basin, Eastern Indonesia: A Machine-Learning Augmentation for 3D Seismic Interpretation

This paper presents a case study and an answer for 3D seismic interpretation dilemma, where the application of machine-learning technology for automated fault detection could offer various advantages over manual interpretation. The most notable advantage came in form of unbiassed and rapid computation time that would free geoscientist from myopic task of manual fault picking in change for more focus into more holistic view of the fault system in the subsurface.

The automated fault detection exercise utilized Paradise platform – a machine-learning software specialized for 3D seismic and big data analytic, equipped with robust synthetic fault models to adapt with wide range of seismic data.

Automated Fault Detection with Deep Learning method in XNR Field proved to be a success. The applied workflow was able to generate a complete and un-biased picture of all major fault trends, provide new insight of structural fabric that controlled Neogene reefal build-ups and reservoir quality in the XNR Field, and significantly create added value to the company’s 3D seismic dataset.