一种基于物理的深度学习方法,用于从地震数据中进行3D声阻估计:应用于伊朗西南部的一个离岸场
Arash Ghiasvand1, Abdolrahim Javaherian2, Maryam Amirmazlaghani3
1Department of Petroleum and Geoenergy Engineering, Amirkabir University of Technology, No. 350, Hafez Ave., Tehran, 1591633411, Islamic Republic of Iran.
Scientific reports
|November 13, 2025
概括
本研究介绍了一种混合的地震逆转方法,使用基于物理的伪井和深度神经网络来准确地绘制3D声阻抗. 这种方法提高了数据有限的海上环境中的水库特征.
科学领域:
- 地质物理学 地质物理学
- 石油地质科学 石油地质科学
- 机器学习在地球科学中的应用
背景情况:
- 地质复杂和数据有限的环境对准确的3D声阻估计构成挑战.
- 传统的地震逆转方法通常严重依赖于低频背景模型和密集井校准,这可能很少.
研究的目的:
- 开发一种混合的地震逆转框架,用于估计3D声阻量.
- 为了减少对低频背景模型和密集井校准在数据有限的设置的依赖.
- 为水库表征提供地质上一致和计算效率高的方法.
主要方法:
- 使用岩石物理建模和变形图统计数据整合基于物理的伪井生成.
- 深度前神经网络 (DFNN) 的应用,将多属性地震数据映射到声阻.
- 从有限的真实井数据生成合成弹性日志和石质学上多样化的训练数据集.
主要成果:
- 混合框架有效地解决了数据稀缺问题,通过从3个真实井中生成36个合成伪井.
- 经过训练的DFNN实现了高精度,交叉相关系数高达95.4%,在验证中正常化的相对误差低于1%.
- 该方法表明对低频模型和密集校准的依赖性降低,提供可靠的预测.
结论:
- 混合地震逆转框架为传统方法提供了一个补充和有效的方法.
- 这种技术提高了在具有挑战性的海上环境中水库表征的准确性.
- 未来的工作包括不确定性量化和体积卷积网络,以提高空间分辨率.
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