Optimizing Single-Well Investment Decisions for Overseas Oil and Gas Portfolio using 0-1 Integer Programming

Summary Under China’s 100-million-ton annual oil and gas production target, optimizing new well investments is critical for stabilizing output and sustaining national energy security, yet existing models face two key challenges: (1) Single-objective frameworks fail to balance economic viability, geopolitical risks, and environmental sustainability, and dynamic political environments in overseas regions lack embedded risk-benefit analysis for strategic planning; (2) high-dimensional feasibility spaces (involving over a thousand well combinations) render traditional methods computationally inefficient. This paper focuses on evaluating the effectiveness of a single well in oil and gas production outside mainland China. This study pioneers the integration of the support vector machine (SVM) and a hybrid genetic algorithm (GA) with particle swarm optimization (PSO) into a multiobjective framework tailored for overseas oil and gas investment decisions, addressing the critical challenge of balancing economic viability, geopolitical risks, and environmental sustainability. New wells are essential for meeting this stable production goal, which significantly impacts overall oil and gas output. Unlike conventional single-objective models, our approach simultaneously maximizes economic returns, minimizes risks from host nation policies and carbon emissions, and optimizes portfolio diversification by balancing Chinese equity stakes and regional preferences. The SVM method utilized in this paper effectively transforms multiobjective optimization into single-objective optimization through a weighted approach. The novel mutation mechanism, inspired by PSO’s position displacement strategy, enables dual-phase search dynamics: initial global exploration via swarm intelligence to identify high-potential solution regions, followed by GA-driven local refinement to converge on optimal portfolios. The efficacy of the constructed GA-PSO algorithm is subsequently validated through a case study. In comparison with traditional methods, this algorithm enhances computational performance, yields superior economic benefits, optimizes the production of single-well investment portfolio, and mitigates investment risks. By generating actionable frontiers, the framework supports annual capital allocation strategies that reconcile conflicting objectives—such as short-term profitability vs. long-term decarbonization, and simulate outcomes under varying geological and geopolitical conditions. Beyond technical innovation, it establishes a replicable template for aligning economic efficiency, risk mitigation, and environmental compliance in politically dynamic regions, directly informing national energy security policies while demonstrating how hybrid metaheuristics advance global energy projects toward sustainability amid evolving geopolitical complexities. Beyond technical optimization, the framework provides a replicable template for balancing economic efficiency, risk mitigation, and environmental compliance in politically dynamic regions, demonstrating how advanced computational tools can enhance strategic planning in global energy projects while advancing the sector’s transition toward sustainable practices.