The oil and gas energy industry is witnessing a renewed interest in optimization technology across the value chain. With applications spanning exploration, development, production, and transportation of extracted material, there is greater awareness of the value optimization technology can provide. The questions of whether this resurgence of optimization in this sector of the economy is sustainable or is a part of the cyclic technology interest remains to be seen. This talk will provide an overview of some classical optimization models and other newer ones. Examples from the area of production optimization will be presented. Links to machine learning will be emphasized in multiple applications. Opportunities and challenges will be discussed.
We analyze risk models representing distributional characteristics of a functional depending on the decision maker's choice and on random data. Very frequently, models of risk are non-linear with respect to the underlying distributions; we represent them as structured compositions. We discuss the statistical estimation of risk functionals and present several results regarding the properties of empirical and kernel estimators. We compare the performance of the estimators theoretically and numerically. Several popular risk measures will be presented as illustrative examples. Furthermore, we consider sample-based optimization problems which include risk functionals in their objective and in the constraints. We characterize the asymptotic behavior of the optimal value and the optimal solutions of the sample-based optimization problems. While we show that many known coherent measures of risk can be cast in the presented structures, we emphasize that the results are of more general nature with a potentially wider applicability. Applications of the results to hypothesis testing of stochastic orders, portfolio efficiency, and others will be outlined.