机器学习分析在密集的储存岩石中对CO2和甲吸附的分析
Mehdi Maleki1, Mohammad Rasool Dehghani1, Moein Kafi1
1Department of Petroleum Engineering, Faculty of Petroleum, Gas, and Petrochemical Engineering, Persian Gulf University, Bushehr, Iran.
Scientific reports
|July 8, 2025
概括
机器学习准确地预测了紧密的水库中的温室气体吸附. 这项研究优化了气体储存和注入过程,以缓解气候变化.
科学领域:
- 地质化学 地质化学
- 储水库工程 储水库工程
- 机器学习 机器学习
背景情况:
- 温室气体 (GHG),特别是二氧化碳 (CO2) 和甲 (CH4),推动了气候变化.
- 温室气体排放的有效管理,特别是地下储存,对能源部门至关重要.
- 吸附是减轻化石燃料开采和利用过程中二氧化碳和CH4排放的关键技术.
研究的目的:
- 为了研究二氧化碳和CH4的吸附行为在紧密的水库.
- 应用先进的机器学习 (ML) 技术来预测气体吸附能力.
- 确定影响地下环境中气体吸附的关键因素.
主要方法:
- 使用的实验数据包括温度,压力,岩石类型,总有机碳 (TOC),水分含量和CO2分数.
- 使用并评估各种ML模型,包括CatBoost和ExtraTrees,用于预测分析.
- 进行了灵敏度分析,以确定不同参数对气体吸附的影响.
主要成果:
- CatBoost和Extra Trees模型显示出高的预测准确性 (R2>0.99对于CO2和CH4).
- 压力被确定为影响吸附的最重要因素,其次是TOC和CO2百分比.
- 温度表现出一种限制性影响,而岩石类型和水分含量则具有次要影响.
结论:
- 机器学习模型,特别是CatBoost,为预测气体吸附能力提供了强大而准确的方法.
- 优化的超参数调整提高了气体存储应用的ML模型的预测能力.
- 这些发现为优化地下储存和封存项目的气体组成和操作参数提供了关键的见解.
关键词:
CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH4 CH5 CH4 CH4 CH4 CH2 CH4 CH4 CH4 CH2 CH2 CH2 CH4 CH2 CH2 CH4二氧化碳二氧化碳二氧化碳气体吸附吸附方式温室气体 温室气体 是一种温室气体.生命周期评估 生命周期评估热力学参数分析的方法地下储存天然气的地下储存.更多相关视频
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