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Improving the Efficiency of Drilling for Geothermal Energy through University Collaboration

New open-source software for making better decisions in uncertain conditions developed by Schlumberger New Energy and Imperial College London

At Schlumberger we pride ourselves in investing in talented, driven students and recent graduates, giving them responsibility and support to make a difference to our business from day one.

Geothermal gyser

Johannes Wiebe, a PhD student at Imperial College London, worked with Schlumberger New Energy as part of his placement year, focusing on optimizing drilling operations for sustainable geothermal energy.

Geothermal wells require specialized drilling, often through particularly hard rocks. When drilling a new well, there is a risk that the drill bit may break and need to be replaced. Engineers do not always have complete knowledge about rock composition or how quickly a drill bit will degrade, but they do have predictions.

Johannes worked with colleagues at Imperial and Schlumberger to define a complex mathematical problem of interest to both academic and industry communities. He then went through an iterative process of developing his theory, and applying it to our needs. This led to an optimization method for drilling in the face of incomplete information, trading off the time cost of drilling more slowly against the reduced risk.

The solution Johannes offered was nuanced, but in summary involved drilling quickly at the beginning, accepting that the drill bit may have to be replaced. Then drilling more slowly at the end when additional caution could prevent equipment failure at a stage when it’s more challenging to fix. Johannes’ work with the Schlumberger geothermal energy team has led to a published piece of open source software as well as an academic paper and a YouTube video.

“As part of my fellowship, I got to spend time in the Schlumberger facility in Cambridge”, said Johannes. “I enjoyed the office atmosphere–it enabled different kinds of conversations from the ones I would usually have at university. I appreciate the opportunities I received to present my work to my industrial collaborators several times. They gave me detailed, immediate and thoughtful feedback that helped me check my thinking and research in real time.”

Johannes subsequently realised that the tools he had developed for geothermal energy might also solve broader optimization problems involving uncertainty.

Significant research in recent years has explored algorithms for optimising decisions in uncertain conditions. But it can be difficult for practitioners to try out the many state-of-the-art algorithms. In response to this, Johannes has gone on to develop and release ROModel– a new online Python software package freely available to everyone. This generalizes his collaboration with Schlumberger by modelling a way of representing optimization under uncertainty, known as robust optimization, using the algebraic modelling language Pyomo.

“This has truly been an ideal collaboration with Johannes Wiebe and Imperial’s Computational Optimisation Group”, said Imperial alumnus Dr Inês Cecílio, Manager of Digital Automation Systems at Schlumberger New Energy and one of Johannes’ collaborators on the project. “Johannes understood the problem, and adapted well to the dual challenge of developing academic theory and practical applications to solve an industry-wide issue. We are delighted that his software packages are now available to everyone and I look forward to seeing them being used to improve decision-making in all sorts of uncertain environments.”

Learn more about early career opportunities with Schlumberger here. Industry partnerships at Imperial are facilitated by the College’s Enterprise Division.

Authored by Natasha Martineau, Imperial College London