Uncertainty in Reservoir Management
|Prof. Peter King (Imperial College London, United Kingdom)|
|Engineering – Reservoir Management|
|10 CPD points|
ALGORITHM PRODUCTION RESERVOIR RESERVOIR MODELING SAND UNCERTAINTY
The course will provide an introduction into many of the concepts behind uncertainty in reservoir modelling. It will start with a description of the origins of uncertainty with a mixture of heuristic treatments and more formal mathematical approaches. It will then develop the appropriate mathematical ideas and tools for estimating uncertainty in practical reservoir modelling. Finally, some ideas for how uncertainty can be managed will be explored.
The aim of this course is to provide some of the basic statistical tools for quantifying uncertainty and some simple strategies for dealing with it.
The course will be given by formal lectures and some simple exercises.
The course is primarily addressed to reservoir engineers involved in building reservoir models but could also be of interest to production engineers who have to deal with the consequences of uncertainty in reservoir performance.
About the instructor
After completing a PhD in theoretical statistical physics from Cambridge University in 1982 Professor Peter King spent 17 years with BP at their technology centre in Sunbury-on-Thames where he worked on a wide variety of subjects applying methods of mathematical physics to reservoir characterisation and modelling. In particular he developed a real space renormalisation approach to both single and two phase upscaling. In collaboration with the members of the Department of Physics at Boston University he has used percolation theory to estimate connectivity of sands as well as uncertainties in production from low to intermediate net-to-gross systems. He had also developed network models of pore scale flow and viscous fingering, object based methods for characterising reservoir heterogeneities. Again in conjunction with Boston University he worked on segregation in avalanches in granular materials as an explanation for the formation of crossbeds in Aeolian systems. Recently he has worked on applying stochastic search algorithms (simulated annealing and genetic algorithms) to optimising business decisions with particular interest to decision making in the presence of uncertainty. He joined the Department of Earth Science & Engineering at Imperial College in 2000. Professor King is a Fellow of both the Institute of Physics and the Institute of Mathematics and its Applications (having served on its Governing Council from 1991-1994).
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