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 massive 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 h using a workstation equipped with two CPUs.

在地质导向操作期间，钻孔电阻率测量值通常会实时反演。借助深度学习等先进的人工智能算法，可以有效地执行反演过程。这些方法需要大量数据集，将多个地球模型与相应的钻孔电阻率测量值联系起来。在这里，我们建议使用一种先进的数值方法——精细等几何分析 (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 的工作站在 56 小时内生成一个由 100,000 个地球模型和相应测量值组成的有意义的合成数据库。