Drilling automation

Big Data and Machine Learning Optimize Operational Performance and Drill-Bit Design

A supervised machine-learning algorithm is developed to classify drilling parameters that increase rate of penetration and bit endurance for use in unconventional fields in Australia.

Design evolution of PDC frames trialed during the drilling campaign.
Fig. 1—Design evolution of PDC frames trialed during the drilling campaign.

Time savings and bit longevity are major challenges in coal-seam gas (CSG) unconventional fields onshore Queensland. Maximizing rate of penetration (ROP) on the basis of optimal drilling parameters was the key to tackling these issues. A formal process for optimizing performance was developed, with a focus on optimizing polycrystalline diamond compact (PDC) bit design and drilling hydraulics and developing a drillers’ road map. As a result, ROP increased from 50 to 150 m/h.

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