Applying data analytics to mitigate risk for hole enlargement while drilling at Deepwater

Speaker Xianping “ Sean” Wu
Xianping “Sean” Wu is a staff well engineer at Shell based in Houston. He started his career in 2006 as a research engineer for ExxonMobil working on rock cutting physics and BHA dynamics modeling. He joined Smith International (acquired by Schlumberger in 2010) as engineering advisor to lead the technical ...

Xianping “Sean” Wu is a staff well engineer at Shell based in Houston. He started his career in 2006 as a research engineer for ExxonMobil working on rock cutting physics and BHA dynamics modeling. He joined Smith International (acquired by Schlumberger in 2010) as engineering advisor to lead the technical development of the industry-leading drilling dynamics optimization service (i-DRILL) for North American market from 2008 to 2012. During his current work at Shell since 2012, he provides technical consulting for Shell’s global drilling operation in Deepwater and Unconventional to optimize ROP and reduce NPT. He also worked as rig site drilling supervisor for Shell’s Permian asset during 2015 and 2016. His latest work is focused on applying data analytics and machine learning to generate actionable insights for reducing well construction cost during well planning and real-time operation. He has a PhD in Materials Science and B.S. in Mechanical Engineering. He has served as Technical Editor for SPE Drilling and Completion Journal since 2012. He is recipient of SPE Outstanding Service Award in 2014 and 2016. He is co-chair of SPEGCS Drilling Symposium in 2016.

Full Description

Deepwater drilling often requires simultaneously hole-enlargement- while-drilling to improve project economics and preserve hole size for reaching deep reservoir. One potentially disastrous scenario is when underreamer has been severely damaged, but drill bit is still in good condition. If such situation is not detected promptly and drilling continues, the hole enlargement while drilling operation could potentially create a significantly undergauged hole section whose diameter is similar to drill bit. Such consequence can cause enormous difficulty for running/cementing the casing string, and often result in costly remedial operations, or in severe cases, hole abandonment.

An advanced data analytics method was developed to early detect the failure of underreamer by utilizing the standard drilling mechanics data from both surface and downhole sensors. This patent-pending method includes the development of new performance metrics based on rock cutting physics and wearing mechanism. A warning signal can be displayed immediately to the drilling crew on the rig and the drilling engineers at office when the underlying failure pattern is detected by the new method. Historical drilling data and failure events are used to train the model with the additional inputs from subject matter experts. This presentation will include the development roadmap of this data analytics method and its implementation results at Deepwater wells in Gulf of Mexico.

Organizer Tom Wick

Event Contact Phone Number: 713-806-2631


Event Contact Email: fieldwick2@gmail.com

When?

Wed, Mar. 8, 2017
11:30 a.m. - 1:30 p.m. US/Central

How Much?

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Where?

PETROLEUM CLUB , 35th Floor, TOTAL Plaza Building
1201 LOUISIANA STREET
Houston, Texas 77002

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