论文标题
用于深度学习反转的数据库生成2.5D钻孔电磁测量，使用精制的等几何分析
Database Generation for Deep Learning Inversion of 2.5D Borehole Electromagnetic Measurements using Refined Isogeometric Analysis
论文作者
论文摘要
钻孔的电阻率测量在GeoSteering操作期间常规倒置。可以在高级人工智能算法（例如深度学习）的帮助下有效地执行反转过程。这些方法需要一个大型数据集，该数据集将多个地球模型与相应的钻孔电阻率测量相关联。在这里，我们建议使用高级数值方法 - 经过精制的等几何分析（RIGA） - 在考虑任意2D地球模型时执行快速准确的2.5D模拟并生成数据库。数值结果表明，我们可以使用配备两个CPU的工作站生成有意义的合成数据库，该数据库由100,000个地球模型组成，在56小时内进行了相应的测量。
Borehole resistivity measurements are routinely inverted in real-time during geosteering operations. The inversion process can be efficiently performed with the help of advanced artificial intelligence algorithms such as deep learning. These methods require a large dataset that relates multiple earth models with the corresponding borehole resistivity measurements. In here, we propose to use an advanced numerical method --refined isogeometric analysis (rIGA)-- to perform rapid and accurate 2.5D simulations and generate databases when considering arbitrary 2D earth models. Numerical results show that we can generate a meaningful synthetic database composed of 100,000 earth models with the corresponding measurements in 56 hours using a workstation equipped with two CPUs.
