Over the past decades, many technological advances have unlocked new opportunities to boost efficiency in the oil and gas industry (e.g., complex well drilling, injection of advanced chemicals, sophisticated instrumentation). The real engineering challenge is to apply these technologies in the best possible way for each particular case. This leads to very difficult decisions to be made, mainly because every oil and gas field is one of its kind and our knowledge of the subsurface is very limited. Many efforts have been made to develop tools to support these decisions by applying a more systematic approach to determine smart exploitation strategies. Yet, very little has been done on the optimization of reservoir surveillance plans to establish the best observations to monitor de field response to the exploitation strategies, which, in turn, can also contribute to a better exploitation of the reservoir.
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|
- Delft University of Technology
- Jansen, J.D., Supervisor
- Van den Hof, Paul M.J., Supervisor, External person
|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