论文标题
基于网格的模拟的图形神经网络框架
A Graph Neural Network Framework for Grid-Based Simulation
论文作者
论文摘要
储层模拟在井控制和井位优化方面的计算昂贵。通常，为了达到最佳井位置，需要进行大量的模拟运行（实现）。在本文中，我们提出了一个图形神经网络（GNN）框架，以构建一个代替馈电模型，该模型替代模拟运行以加速优化过程。我们的GNN框架包括一个编码器，一个过程和一个解码器，该框架从设计和生成的仿真原始数据中的处理图数据中获取输入。我们使用6000个样本（等效于40井配置）训练GNN模型，每个样本包含上一个步骤状态变量和下一步状态变量。我们使用另外6000个样本测试GNN模型，在模型调整后，一步预测和推出预测都与模拟结果密切匹配。我们的GNN框架在应用良好相关的地下优化（包括石油和气体）以及碳捕获隔离（CCS）方面具有巨大潜力。
Reservoir simulations are computationally expensive in the well control and well placement optimization. Generally, numerous simulation runs (realizations) are needed in order to achieve the optimal well locations. In this paper, we propose a graph neural network (GNN) framework to build a surrogate feed-forward model which replaces simulation runs to accelerate the optimization process. Our GNN framework includes an encoder, a process, and a decoder which takes input from the processed graph data designed and generated from the simulation raw data. We train the GNN model with 6000 samples (equivalent to 40 well configurations) with each containing the previous step state variable and the next step state variable. We test the GNN model with another 6000 samples and after model tuning, both one-step prediction and rollout prediction achieve a close match with the simulation results. Our GNN framework shows great potential in the application of well-related subsurface optimization including oil and gas as well as carbon capture sequestration (CCS).
