论文标题
通过地质核心X射线微观图的孔隙率和渗透率预测的AI
AI for Porosity and Permeability Prediction from Geologic Core X-Ray Micro-Tomography
论文作者
论文摘要
地质核心是岩石样品，在井钻探过程中从地下深处提取。它们用于石油储层的性能表征。传统上，核心的物理研究是通过手动耗时实验进行的。随着深度学习的发展，科学家们积极开始致力于开发基于机器学习的方法，以识别物理特性，而无需任何手动实验。以前的几项作品使用机器学习来确定岩石的孔隙率和渗透性，但方法不准确或计算昂贵。我们建议使用非常小的CNN转换器模型的自制预处理预处理，以高度准确地预测岩石的物理特性，以时间效率的方式进行预测。我们表明，对于极小的数据集，该技术甚至可以防止过度适应。 github：https：//github.com/shahbozjon/porsosity-and-permeability-prediction
Geologic cores are rock samples that are extracted from deep under the ground during the well drilling process. They are used for petroleum reservoirs' performance characterization. Traditionally, physical studies of cores are carried out by the means of manual time-consuming experiments. With the development of deep learning, scientists actively started working on developing machine-learning-based approaches to identify physical properties without any manual experiments. Several previous works used machine learning to determine the porosity and permeability of the rocks, but either method was inaccurate or computationally expensive. We are proposing to use self-supervised pretraining of the very small CNN-transformer-based model to predict the physical properties of the rocks with high accuracy in a time-efficient manner. We show that this technique prevents overfitting even for extremely small datasets. Github: https://github.com/Shahbozjon/porosity-and-permeability-prediction
