Big Data and Machine Learning Optimize Operational Performance and Drill-Bit Design
Title
Big Data and Machine Learning Optimize Operational Performance and Drill-Bit Design
Subject
Machine learning
Big data
Coal deposits
Infill drilling
Description
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. Time savings of more than 150 hours for the drilling campaign was achieved. 2021 Society of Petroleum Engineers. All rights reserved.
49-50
73
Creator
Carpenter, Chris
Date
2021
Type
conferencePaper
Identifier
1492136
10.2118/1221-0049-JPT
Citation
Carpenter, Chris, “Big Data and Machine Learning Optimize Operational Performance and Drill-Bit Design,” Lamar University Midstream Center Research, accessed May 4, 2024, https://lumc.omeka.net/items/show/29394.