使用机器学习评估阿肯色州南部Smackover形成盐水中的资源
Katherine J Knierim1, Madalyn S Blondes2, Andrew Masterson2
1US Geological Survey, Lower Mississippi-Gulf Water Science Center, Nashville, TN, USA.
Science advances
|September 27, 2024
概括
的需求正在上升,但新的研究显示阿肯色州的储量很大.
科学领域:
- 地质化学 地质化学
- 机器学习 机器学习
- 资源地质 资源地质学
背景情况:
- 全球需求,对离子电池至关重要,超过供应.
- 阿肯色州的Smackover形成显示了盐水中的高度.
研究的目的:
- 在阿肯色州的Smackover形成盐水中绘制预测的度.
- 为了估计这些盐水中的总资源.
主要方法:
- 使用地质,地化学和温度数据训练了一种随机森林机器学习模型.
- 利用已发表的和新的盐水度数据.
- 集成的预测地图与储备和地质数据用于计算.
主要成果:
- 据估计,Smackover形成的盐水中含有510万至1900万.
- 这相当于目前美国资源估计的35%至136%.
- 在2022年,5000作为石油,天然气和开采的废物共同生产.
结论:
- 阿肯色州的Smackover形成拥有大量的资源.
- 从现有的工业盐中共同生产提供了潜在的供应.
- 机器学习有效地预测了地下盐水中的度.
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