  The geology of oil reservoirs is largely unknown. Consequently, the reservoir
models used for production optimization are subject to significant uncertainty.
To minimize the associated risk, the oil literature has mainly used
ensemble-based methods to optimize sample estimated risk measures of net
present value (NPV). However, the success in reducing risk critically depends
on the choice of risk measure. As a systematic approach to risk mitigation in
production optimization, this paper characterizes proper risk measures by the
axioms of coherence and aversion. As an example of a proper measure, we
consider conditional value-at-risk, $\text{CVaR}_{\alpha}$, at different risk
levels, $\alpha$. The potential of $\text{CVaR}_{\alpha}$ to minimize profit
loss is demonstrated by a simulated case study. The case study compares
$\text{CVaR}_{\alpha}$ to real-world best practices, represented by reactive
control. It shows that for any risk level, $\alpha$, we may find an optimized
strategy that provides lower risk than reactive control. However, despite
overall lower risk, we see that all optimized strategies still yield some
unacceptable low profit realizations relative to reactive control. To remedy
this, we introduce a risk mitigation method based on the NPV offset
distribution. Unlike existing methods of the oil literature, the offset risk
mitigation approach minimizes the risk relative to a reference strategy
representing common real-life practices, e.g. reactive control. In the
simulated case study, we minimize the worst case profit offset to reduce the
risk of realizations that do worse than the reactive strategy. 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.
