整合NMR和机器学习用于异质阿斯马里碳酸盐储库中的孔型驱动岩石分类
Maryam Veysi1, Ali Kadkhodaie2, Mehran Arian1
1Department of Earth Sciences, SR.C., Islamic Azad University, Tehran, Iran.
Scientific reports
|December 23, 2025
概括
这项研究引入了一种新的NMR电面方法来表征复杂的水库,揭示孔隙连接性,而不仅仅是岩石类型,控制碳化合物流量和生产力.
科学领域:
- 地质科学是地球科学.
- 石油工程是石油工程中的一个.
- 机器学习应用 机器学习应用
背景情况:
- 像阿斯马里岩层这样的异质碳酸盐储库对传统石质学基础的表征提出了挑战.
- 传统的方法无法捕捉动态流体行为和孔尺度控制生产力.
- 现有的技术不足以解决复杂的孔隙结构及其对水库性能的影响.
研究的目的:
- 开发一个集成的NMR电面框架,以改善水库特征.
- 克服传统方法在异质碳酸盐构成中的局限性.
- 建立基于孔隙功能的石质学独立的岩石类型系统.
主要方法:
- 使用可组合磁共振 (CMR) 记录用于高分辨率孔隙结构分析.
- 应用可解释的机器学习算法与SHAP可解释性用于电面的分类.
- 开发了一种新的孔连接性指数和动态透性校正.
主要成果:
- 鉴定了碳化合物传导性的显著变化 (3-5x) 对于具有相似毛孔度的间隔.
- 证明了砂岩子单位 (15-20%厚度) 的优异宏性贡献了60%的生产能力.
- 实现了流量单位的92%预测准确度,以T2几何平均值作为主要控制参数 (47%的贡献).
结论:
- 重新描述阿斯马里形成作为一个由孔隙连接性支配的系统,超越了石质学依赖.
- 通过核心日志和生产数据验证了集成的NMR电面框架,证实了其可靠性.
- 突出了用于优化井完工和增强成熟油田的石油回收的实际应用.
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