Big Data: Mess versus Value

Big Data: Mess versus Value

Internship Description

The problem of “messy” drilling and downhole data, and how to extract maximum value from it, is a pertinent challenge for the petroleum industry – in particular when it comes to advancing the understanding of near‐wellbore physics and chemistry. This is particularly important since technological advancements, workflow optimizations and integrated project processes/execution are directly linked to a reduction in the cost of well construction, which generally is the largest expenditure item during field development. 
We are looking for a highly motivated bachelor or master student who will be responsible for​ loading, processing, and analyzing continuous data streams from wellbores acquired with sensors during the drilling and reservoir monitoring process (e.g., measurement or logging while drilling). The purpose is to find in and extract from these data key information necessary to optimize drilling and improve our understanding of the processes ongoing in the near wellbore region.
Results from this internship project will be integrated into the development of automation systems that are capable of handling massive data streams from drill sites, maximize the quality of these data streams, find key information necessary to optimize drilling (i.e., lower cost), and help reduce risk, lost and non‐productive time (i.e., prevent failures and accidents).
We expect that this research will lead to publications, which the student can contribute to.
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Deliverables/Expectations

​We are seeking a B.Sc. (Bachelor of Science) or M.Sc. (Master of Sciences) student who is interested in the stated topic for his / her thesis research. The project is suitable for candidates interested in rock mechanics, geo‐chemistry, or/and data analysis / statistics.​

Faculty Name

Tadeusz Patzek

Field of Study

Drilling, production, or reservoir engineering.