NETL experts on geo-data science and oil and natural gas technology shared their technical insight with more than 250 peers from around the world at this week’s 4th annual Machine Learning in Oil & Gas conference.
NETL Oil & Gas Technology Manager Jared Ciferno and Research Geo-Data Scientist Kelly Rose were among the featured speakers at the event, organized by the Energy Conference Network and held April 17-18 in Houston.
Machine learning refers to a computer system or program’s ability to learn from data using algorithms, statistical models and pattern recognition. Many modern technologies — such as self-driving cars, web search tools and social media sites — employ machine learning to improve performance.
When it comes to oil and natural gas operations, machine learning offers vast opportunities to increase efficiency, eliminate downtime, enhance safety and reduce costs. The conference brought together leading professionals from every stage of oil and gas production to learn about the latest technology developments and future possibilities for machine learning.
Ciferno manages NETL’s broad portfolio of oil and natural gas research and development projects, which encompasses conventional and unconventional natural gas, enhanced oil recovery, deepwater oil and gas production and methane hydrates. His presentation, “Transforming the Use of Subsurface Reservoirs Through Science-Informed Machine Learning,” addressed how to manage unstructured data and maximize current statistical models to go from predictive maintenance to automated maintenance.
Rose is a researcher who uses geologic and geo-data science to characterize, understand and reduce uncertainty about spatial relationships between energy and engineered natural systems. Her work involves development of new data-driven methods and tools for analysis of energy materials, oil and gas, offshore energy and much more. Her presentation, “Two Science-Based Machine Learning Models for Improved Subsurface Geohazard and Resource Assessments,” focused on computational tools developed based on lessons learned from the Deepwater Horizon oil spill in 2010, including NETL’s recently patented, real-time downhole kick detection technique and custom data-driven components from NETL’s Offshore Risk Modeling suite that offer improved prediction and analysis of subsurface resource and reservoir properties.
“These presentations are a great opportunity to demonstrate the powerful impact of NETL’s work to develop technological solutions for America’s energy challenges,” Ciferno said. “We’re sharing cutting-edge tools and techniques that are immediately useful to help industry boost efficiency and cut costs — benefits that ultimately translate to better service and greater affordability for consumers.”
NETL’s oil and natural gas research programs combine foundational characterization science, computational modeling and advanced optimization tools with field experiments and private sector input to design and validate innovative technologies that will maximize the safe, efficient, and economic recovery of America’s abundant oil and gas resources.