The multi-objective portfolio model for oil and gas exploration drilling projects selection and its operator-enhanced NSGA-II based solution

TL;DR

This paper introduces a multi-objective portfolio model for oil and gas drilling project selection, incorporating geological uncertainty and an enhanced NSGA-II algorithm to optimize risk-return trade-offs.

Contribution

It develops a novel probabilistic portfolio model and an operator-enhanced NSGA-II algorithm tailored for oil and gas exploration drilling project selection.

Findings

Effective risk-return trade-off achieved

Enhanced search ability with new genetic operators

Validated approach on real exploration data

Abstract

Drilling investment is pivotal to operational planning in oil and gas (O\&G) exploration. Conventional deployment relies heavily on fragmented expert assessments of geological and economic factors, with limited integration ability of information. As the tool of portfolio show strong potential for mitigating uncertainty and selecting superior drilling plans, this study develops a multi-objective mean-variance portfolio model that accounts for geological-parameter uncertainty, enabling an effective risk-return trade-off and optimal selection. First, the probabilistic distribution of geological-parameters for prospect-list projects is obtained through expert-elicited priors. And considering the selection of the drilling projects as a portfolio, an optimization model is formulated jointly to describe the return and risk of short-term plan, under different constraints. Second, an improved OE-NSGA-II algorithm is proposed specifically for this model, in which (1) a directional crossover operator is designed to embed improving directions in objective space-derived from dominance and objective differences-into recombination, and (2) a structure-aware mutation operator is designed to prioritize high-utility bit flips via probabilistic sampling with feasibility repair, thus improving the search ability for superior Pareto solutions. Finally, using the case of 2023 exploration drilling deployment for verification, and then apply the validated method to the 2024 deployment to support decision-making. The results indicate that the proposed approach offers a reusable solution for drilling portfolio optimization in O\&G exploration.