Our work investigates the economic efficiency of the prevailing "ladder-step" investment strategy in oil and gas exploration, which advocates for the incremental acquisition of geological information throughout the project lifecycle. By employing a multi-agent Deep Reinforcement Learning (DRL) framework, we model an alternative strategy that prioritizes the early acquisition of high-quality information assets. We simulate the entire upstream value chain-comprising competitive bidding, exploration, and development phases-to evaluate the economic impact of this approach relative to traditional methods. Our results demonstrate that front-loading information investment significantly reduces the costs associated with redundant data acquisition and enhances the precision of reserve valuation. Specifically, we find that the alternative strategy outperforms traditional methods in highly competitive environments by mitigating the "winner's curse" through more accurate bidding. Furthermore, the economic benefits are most pronounced during the development phase, where superior data quality minimizes capital misallocation. These findings suggest that optimal investment timing is structurally dependent on market competition rather than solely on price volatility, offering a new paradigm for capital allocation in extractive industries.

翻译：本研究考察了油气勘探中普遍采用的'阶梯式'投资策略的经济效率，该策略主张在整个项目生命周期内逐步获取地质信息。通过采用多智能体深度强化学习框架，我们建模了一种替代策略，该策略优先在早期获取高质量信息资产。我们模拟了包含竞争性投标、勘探和开发阶段在内的完整上游价值链，以评估该方法相对于传统策略的经济影响。结果表明，前置信息投资能显著降低冗余数据采集相关成本，并提高储量估值的精度。具体而言，我们发现替代策略在高度竞争环境中优于传统方法，其通过更精确的投标缓解了'赢家诅咒'效应。此外，经济效益在开发阶段最为显著，优质数据最大限度地减少了资本错配。这些发现表明，最优投资时机在结构上取决于市场竞争而非单纯的价格波动，为采掘业的资本配置提供了新范式。