The number of recorded earthquakes in Oklahoma has substantially increased during the last few decades, a trend that coincides with the increases in the injected volume in underground injection control (UIC) wells. Several studies have suggested the existence of spatial and temporal links between earthquakes and injection wells. However, creating a spatial connection between the earthquakes and UIC wells requires making a prior assumption about the radius of induced seismicity. In this study, we use intrinsic features of the UIC wells to find the cohort of wells with associated earthquakes, based on the level of activity and proximity of the wells to the events. For this purpose, a hybrid genetic algorithm–K-means (GA-K-means) algorithm was applied over UIC wells, and the geographical representation of the clustered wells was co-visualized with earthquake data to determine wells with induced seismic activities. The analysis was performed every year since 2002, and the most critical attributes to distinguish the behavior of wells were identified. The analysis showed a distinct change in cluster identifiers before the year 2010, which is believed to be the beginning of increased seismic activities, compared to later dates. Our approach was able to group the earthquake-associated wells from the rest of the data, and centroid analysis of these wells helped us identify the critical pressure and cumulative volume range that result in induced seismicity. These findings can be used as guidelines for designing safer injection sites for sustainable energy production in Oklahoma.

在过去的几十年中，俄克拉荷马州记录的地震数量大大增加，这一趋势与地下注入控制（UIC）井中注入量的增加相吻合。多项研究表明，地震与注入井之间存在时空联系。但是，要在地震和UIC井之间建立空间联系，就需要事先对诱发地震的半径进行假设。在这项研究中，我们根据井的活动程度和井与事件的接近程度，使用UIC井的内在特征来查找与地震相关的井群。为此，在UIC井上应用了混合遗传算法-K-均值（GA-K-means）算法，然后将聚类井的地理表示与地震数据进行可视化，以确定具有诱发地震活动的井。自2002年以来，每年进行一次分析，确定了区分井眼行为的最关键属性。分析显示，与后来的日期相比，2010年之前的聚类标识符发生了明显变化，这被认为是地震活动增加的开始。我们的方法能够从其余数据中对与地震相关的井进行分组，对这些井的质心分析有助于我们确定导致地震活动的临界压力和累积体积范围。这些发现可作为设计俄克拉荷马州可持续能源生产的更安全注入地点的指南。