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

Chao Min, Junyi Cui, Stanisław Migórski, Yonglan Xie, Qingxia Zhang, Jun Peng

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.

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

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.
Paper Structure (26 sections, 39 equations, 13 figures, 6 tables)

This paper contains 26 sections, 39 equations, 13 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Description and definitions
  3. Background
  4. Definitions
  5. Methodology
  6. Geological uncertainty factors
  7. Quantification of GPoS
  8. Reserve estimation
  9. Quantification of EPoS
  10. The multi-objective portfolio model for O&G exploration drilling projects selection
  11. Assumptions
  12. Decision variables
  13. Objective function
  14. Constraints
  15. Operator-enhanced NSGA-II (OE-NSGA-II)
  16. ...and 11 more sections

Figures (13)

  • Figure 1: Optimal process for O&G exploration drilling.
  • Figure 2: Overall flowchart of the proposed model and algorithm.
  • Figure 3: Effect of initialization on feasible density and subsequent operator learning.
  • Figure 4: Schematic diagram of the directional cross operator guided by parental decision bits.
  • Figure 5: DC: The directional cross operator
  • ...and 8 more figures