Research Output per year
In this thesis we propose a methodology to assess the value of future measurements as a first step towards the development of a framework to optimize the design of reservoir surveillance plans. We also investigate alternatives to improve current reservoir management approaches by recommending actions which anticipate the availability of future information and account for the impact of immediate decisions on the decisions to be made in the future.
Throughout the chapters, we discuss how to combine a variety of topics (e.g., model-based optimization, data assimilation, uncertainty quantification) with other unusual ingredients (e.g., plausible truths, clairvoyance, flexible plans) to develop a methodology which can be applied in many problems involving decision making and learning. Despite being motivated by a real application, this research addresses abstract concepts such as value and information, but always from an engineering perspective. This makes us approach the problem in a different way, which, we hope, will inspire innovative solutions in the future.
|Qualification||Doctor of Philosophy|
|Award date||22 Jan 2018|
|Publication status||Published - 2018|
- value of information
- closed-loop reservoir management
- reservoir surveillance
- geological uncertainty
- robust optimization
- data assimilation
- plausible truths
- representative models
- stochastic programming
Research output: Contribution to journal › Article › Scientific › peer-review