堆叠组合机器学习用于碳酸盐岩石塞的孔隙性和绝对透性预测
Ramanzani Kalule1, Hamid Ait Abderrahmane2, Waleed Alameri3
1Department of Mechanical Engineering, Khalifa University, Abu Dhabi, UAE. kramanzani@gmail.com.
这项研究使用堆叠组合机器学习来准确预测碳酸盐岩的孔隙性和透性. 这种方法提高了各种地质构造的预测速度和通用性.
科学领域:
- 地质地质地质地质地质地
- 石化物理学 石化物理学
- 机器学习 机器学习
背景情况:
- 预测碳酸盐岩石的性质,如多孔性和透性,对于水库的表征至关重要.
- 碳酸盐岩中的异质性和复杂的孔喉结构对准确的属性预测提出了重大挑战.
研究的目的:
- 开发和验证一个堆叠集团机器学习模型,用于预测碳酸盐岩的孔隙性和绝对透性.
- 通过整合多个机器学习模型来提高预测准确性和概括性.
主要方法:
- 利用碳酸盐核心样本的3D微型CT图像的2D切片.
- 应用了堆叠组合机器学习方法,将基准模型的预测集成到meta-learner中.
- 采用随机搜索用于超参数优化和分水-scikit-image用于特征提取.
主要成果:
- 堆叠组合模型证明了碳酸盐岩石孔隙性的有效预测.
- 该模型还在预测绝对透度方面表现出高准确度.
- 这种方法加速了预测,并改善了模型的通用性.
结论:
- 堆叠集团机器学习是复杂碳酸盐水库中石化物理性质预测的强大技术.
- 开发的方法提供了一个可靠的解决方案,用于基于微型CT图像数据来表征水库潜力.
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