SPE Online Education
Drilling Data Cleaning and Preparation for Data Analytics Application
Recorded On: 12/10/2020
Throughout the history of oil well drilling, service providers have been continuously striving to improve performance and reduce total drilling costs to operating companies. Despite constant improvement in tools, products and processes, data science cannot be applied straightaway due to data quality issues. But when achieved, the implementation of data science in the energy sector brings significant value in operational efficiency and drilling optimization. The challenge is how to efficiently process the massive amounts of data produced by the multitude of internet of thing (IOT) sensors at the rig. Once cleaned, the data can be fed into data analytics platforms and machine learning models to efficiently analyze trends and plan future well more efficiently. This roadmap can serve as a basis for drilling optimization. The objective of this presentation is to detail the various steps needed to prepare field drilling data for business analysis, as well discuss about data analytics and machine learning application in drilling operations.
This webinar is categorized under the Drilling technical discipline.
All content contained within this webinar is copyrighted by Daniel Braga and its use and/or reproduction outside the portal requires express permission from Daniel Braga.
For more information on this topic, please check out the SPE suggested reading links below. Here you will find topic-related books, publications, and papers for purchase in OnePetro.
Mr. Braga is a Mechanical Engineer graduate from University of Campinas – Brazil. After graduation, he worked for the offshore drilling contractor Seadrill, based offshore Brazil and in their office in Rio de Janeiro. Recently, Daniel graduated from Louisiana State University where he obtained his Master’s in Petroleum Engineering. The core of this presentation is in fact a result of his MS thesis work. Since he graduated from LSU, Daniel has been working for Corva as a R&D Engineer. A SPE member since 2008, Daniel held multiple volunteering roles as a student and YP, the most recent being a member of the Data Analytics Group from the Gulf Coast Section.
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