Abstract
We introduce a new approach using computer vision to predict the land surface displacement from subsurface geometry images for Carbon Capture and Sequestration (CCS). CCS has been proved to be a key component for a carbon neutral society. However, scientists see there are challenges along the way including the high computational cost due to the large model scale and limitations to generalize a pre-trained model with complex physics. We tackle those challenges by training models directly from the subsurface geometry images. The goal is to understand the respons of land surface displacement due to carbon injection and utilize our trained models to inform decision making in CCS projects. We implement multiple models (CNN, ResNet, and ResNetUNet) for static mechanics problem, which is a image prediction problem. Next, we use the LSTM and transformer for transient mechanics scenario, which is a video prediction problem. It shows ResNetUNet outperforms the others thanks to its architecture in static mechanics problem, and LSTM shows comparable performance to transformer in transient problem. This report proceeds by outlining our dataset in detail followed by model descriptions in method section. Result and discussion state the key learning, observations, and conclusion with future work rounds out the paper.
Abstract (translated)
我们使用计算机视觉提出了一种新的方法来预测地下几何图像中的土地表面位移，用于碳捕获和储存（CCS）。已经证明，CCS 是实现碳中性社会的关键组件。然而，科学家们认为在道路上存在挑战，包括由于大型模型规模而产生的高计算成本以及无法对复杂物理进行推广的限制。我们通过直接从地下几何图像中训练模型来应对这些挑战。目标是理解由于碳注入导致的土地表面位移的责任，并利用我们训练好的模型来指导CCS项目中的决策。我们在静态力学问题中采用了多种模型（CNN，ResNet和ResNetUNet），这是一种图像预测问题。接下来，我们使用LSTM和Transformer来处理瞬态力学场景，这是一种视频预测问题。结果和讨论部分说明了关键的学习、观察和结论，以及未来工作的展望。
URL
https://arxiv.org/abs/2403.06025
