在中间湿条件下,在数字岩石中观察到增强的二氧化碳排放
Jaione Tirapu Azpiroz1, Ronaldo Giro2, Rodrigo Neumann Barros Ferreira1
1IBM Research, Av. República do Chile, 330, Rio de Janeiro, RJ, CEP 20031-170, Brazil.
Scientific reports
|July 9, 2024
概括
这项研究引入了一种用于模拟二氧化碳 (CO2) 在岩石中的地质储存的新方法. 统计样本的毛细血管网络 (ssCN) 准确地预测了二氧化碳捕获潜力,实现了高和水平.
科学领域:
- 二氧化碳 (CO2) 的地质储存.
- 孔尺度流体动力学 孔尺度流体动力学
- 储水池岩石的表征 储水池岩石的表征
背景情况:
- 准确的模拟二氧化碳被困在水库岩石中对于有效的地质储存至关重要.
- 现有的模型难以对孔尺度毛细管网络和流体相互作用进行可扩展的表示.
- 了解孔尺度上的二氧化碳岩石流体相互作用是预测储存能力的关键.
研究的目的:
- 开发一种可扩展的方法来表示岩石孔隙空间,用于二氧化碳储存模拟.
- 研究压力,温度和湿度条件对二氧化碳捕获的影响.
- 为了提高二氧化碳储存潜力的预测储存岩石.
主要方法:
- 发展统计样本的毛细血管网络 (ssCN) 保存岩石形态.
- 模拟使用ssCN方法在砂岩样本中的二氧化碳排水位移.
- 在储压力和温度下探索中间和CO2湿条件.
主要成果:
- 该ssCN方法成功地表示岩石孔隙空间,并模拟二氧化碳排放.
- 增加压力和温度会增加CO2在毛细血管网络中的透.
- 在90度接触角附近观察到80%的显著CO2和最大值,是之前估计的两倍.
结论:
- ssCN方法提供了一个可扩展和准确的方法来预测二氧化碳储存潜力.
- 该研究强调了湿条件对二氧化碳捕获效率的重大影响.
- 开放的数据和工作流程有助于验证和更广泛地应用该方法.
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