We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Research & business

Research lectures, seminars and events

The events listed in this area are research seminars, workshops and lectures hosted by Durham University departments and research institutes. If you are not a member of the University, but  wish to enquire about attending one of the events please contact the organiser or host department.


Stats4Grads: A Bayesian statistical approach to decision support for petroleum reservoir well control optimisation

Presented by Jonathan Owen, Durham University
27 November 2019 13:00 in CM105

Complex mathematical computer models are used across many scientific disciplines and industry to improve the understanding of the behaviour of physical systems and increasingly to aid decision makers. Major limitations to the use of computer simulators include their complex structure; high-dimensional parameter spaces and large number of unknown model parameters; which is further compounded by their long evaluation times. Decision support, commonly misrepresented as an optimisation task, often requires a large number of model evaluations rendering traditional optimisation methods intractable whilst simultaneously failing to incorporate uncertainty. Consequently, they may yield non-robust decisions.

I will present an iterative decision support strategy which imitates the history matching procedure aiming to identify a robust class of decisions. Bayes linear emulators provide fast, statistical approximations to computer models, yielding predictions for as yet unevaluated parameter settings, along with a corresponding quantification of uncertainty. Appropriate structured uncertainties are accurately quantified and incorporated to link the sophisticated computer model and the actual system in order to obtain robust decisions for the real world problem.

In the petroleum industry, TNO devised a field development optimisation challenge under uncertainty providing an ensemble of 50 fictitious oil reservoir models generated using a stochastic geology model. This challenge exhibits many of the common issues associated with computer experimentation. I will demonstrate the robust decision support strategy applied to the TNO challenge for a greatly reduced computational cost versus ensemble optimisers. This includes the construction of a targeted Bayesian design as well as methods of identifying subsets of models as representatives for the entire ensemble.

Contact for more information