TY - THES
T1 - Value of information in closed-loop reservoir management
AU - Gonçalves Dias de Barros, Eduardo
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - value of information
KW - closed-loop reservoir management
KW - reservoir surveillance
KW - geological uncertainty
KW - robust optimization
KW - data assimilation
KW - plausible truths
KW - representative models
KW - clustering
KW - stochastic programming
UR - http://resolver.tudelft.nl/uuid:9667dc41-c736-47e6-b818-78c7c50fb08d
U2 - 10.4233/uuid:9667dc41-c736-47e6-b818-78c7c50fb08d
DO - 10.4233/uuid:9667dc41-c736-47e6-b818-78c7c50fb08d
M3 - Dissertation (TU Delft)
SN - 978-94-6366-009-9
ER -