Recent years have witnessed many significant petroleum exploration discoveries in mature basins, particularly in Southeast Asia, but also around the world. Exploration in mature basins has the benefits of well-established infrastructure and extensive geoscience and engineering knowledge and experience. However, unlocking the large hidden potential of mature provinces requires innovative solutions, such as improved imaging of deep targets, new thinking of petroleum plays and geological models and modern engineering solutions for managing challenging subsurface drilling and development conditions.
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.
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