Millions of abandoned oil and gas wells are scattered across the world, leaching methane into the atmosphere and toxic compounds into the groundwater. Many of these locations are unknown, preventing the wells from being plugged and their polluting effects averted. Remote sensing is a relatively unexplored tool for pinpointing abandoned wells at scale. We introduce the first large-scale benchmark dataset for this problem, leveraging medium-resolution multi-spectral satellite imagery from Planet Labs. Our curated dataset comprises over 213,000 wells (abandoned, suspended, and active) from Alberta, a region with especially high well density, sourced from the Alberta Energy Regulator and verified by domain experts. We evaluate baseline algorithms for well detection and segmentation, showing the promise of computer vision approaches but also significant room for improvement.

翻译：全球范围内散布着数百万废弃油气井，它们持续向大气中释放甲烷，并向地下水中渗透有毒化合物。由于大量井位信息缺失，导致封井作业无法开展，污染效应难以遏制。遥感技术作为大规模定位废弃油井的手段尚未得到充分探索。本研究首次针对该问题构建了大规模基准数据集，利用Planet Labs提供的中分辨率多光谱卫星影像进行开发。经领域专家验证并整合阿尔伯塔能源监管机构数据，我们构建的数据集涵盖阿尔伯塔地区（该区域具有极高油井密度）超过21.3万口油井（含废弃、封存及活跃状态）。通过对油井检测与分割基线算法的评估，本研究既展示了计算机视觉方法的应用潜力，也揭示了其亟待改进的技术空间。