Offset Risk Minimization for Open-loop Optimal Control of Oil Reservoirs

Andrea Capolei, Lasse Hjuler Christiansen, J. B. Jørgensen

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Simulation studies of oil field water flooding have demonstrated a significant potential of optimal control technology to improve industrial practices. However, real-life applications are challenged by unknown geological factors that make reservoir models highly uncertain. To minimize the associated financial risks, the oil literature has used ensemble-based methods to manipulate the net present value (NPV) distribution by optimizing sample estimated risk measures. In general, such methods successfully reduce overall risk. However, as this paper demonstrates, ensemble-based control strategies may result in individual profit outcomes that perform worse than real-life dominating strategies. This poses significant financial risks to oil companies whose main concern is to avoid unacceptable low profits. To remedy this, this paper proposes offset risk mimimization. Unlike existing methodology, the offset method uses the NPV offset distribution to minimize risk relative to a competing reference strategy. Open-loop simulations of a 3D two-phase synthetic reservoir demonstrate the potential of offset risk minimization to significantly improve the worst case profit offset relative to real-life best practices. The results suggest that it may be more relevant to consider the NPV offset distribution than the NPV distribution when minimizing risk in production optimization.
Original languageEnglish
Book seriesIFAC-PapersOnLine
Volume50
Issue number1
Pages (from-to)10620-10625
ISSN2405-8963
DOIs
Publication statusPublished - 2017
Event20th IFAC World Congress 2017 - Toulouse, France
Duration: 9 Jul 201714 Jul 2017
Conference number: 20
http://www.ifac2017.org/

Conference

Conference20th IFAC World Congress 2017
Number20
CountryFrance
CityToulouse
Period09/07/201714/07/2017
Internet address

Keywords

  • Control and Systems Engineering
  • Model-based control
  • Optimal control
  • Production control
  • Risk
  • Stochastic modelling

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