Journal article
A Least Squares Method for Ensemble-based Multi-objective Oil Production Optimization
Despite a significant potential to improve industrial standards, practical applications of production optimization are impeded by geological uncertainty. As a mean to handle the associated financial risks, the oil literature has devised a range of ensemble-based strategies that seek to optimize proper combinations of sample-estimated risk measures to balance the opposing objectives of risk and reward.
Many of the associated optimization problems are naturally formulated in terms of multi-objective optimization (MOO). Ideally, MOO problems should be solved by generating an approximation to the efficient frontier of optimal tradeoffs between risk and return. However, the large-scale nature of real-life oil reservoirs implies that formation of the frontier often becomes computationally intractable in practice.
To meet this challenge, this paper introduces a generalized least squares (LS) approach that provides an efficient and unified solution strategy for ensemble-based multi-objective optimization problems. At its core, the LS method uses an a priori characterization of desirable trade-offs that allows the method to focus on a single Pareto optimal point.
Consequently, the LS approach avoids the need to generate a representative of the efficient frontier. In turn, this significantly reduces computational complexity compared to most MOO methods. As a result, the LS method poses a practical alternative to conventional strategies when the efficient frontier is unknown and computationally intractable to generate.
Language: | English |
---|---|
Year: | 2018 |
Pages: | 7-12 |
ISSN: | 14746670 and 24058963 |
Types: | Journal article |
DOI: | 10.1016/j.ifacol.2018.06.347 |
ORCIDs: | Christiansen, Lasse Hjuler , Hørsholt, Steen and Jørgensen, John Bagterp |