Closed-Loop Reservoir Management: Do We Need Complex Models? Conference Paper uri icon


  • Abstract Recently, history matching and optimization were brought together in the oil industry into an integrated and more structured approach called optimal closed-loop reservoir management. Closed-loop control algorithms have already been applied extensively in other engineering fields, including aerospace, mechanical, electrical and chemical engineering. However, their applications to porous media flow, such as - in the current practices and improvements in oil and gas recovery, in aquifer management, in bio-landfill optimization, and in CO2 sequestration have been minimal due to the large-scale nature of existing problems that generate complex models for controller design and real-time implementation. Basically, three sources of high-dimensionality can be identified from the underlying reservoir models: size of parameter space, size of state space, and the number of scenarios or realizations necessary to account for uncertainty. In this paper we will address type problem of high dimensionality by focusing on the mitigation of the size of the state-space models by means of model-order reduction techniques in a systems framework. We will show how one can obtain accurate reduced order models which are amenable to fast implementations in the closed-loop framework by means of model predictive controllers (MPC).

name of conference

  • SPE Digital Energy Conference and Exhibition

published proceedings

  • All Days

author list (cited authors)

  • Gildin, E., & Lopez, T. J.

publication date

  • January 1, 2011 11:11 AM


  • SPE  Publisher