Multimodal Auto-regressive Transformer Surrogate for Modeling Variable Operations and Quantifying Uncertainty in Geological Carbon Storage

用于地质碳封存中变工况建模与不确定性量化的多模态自回归 Transformer 代理模型

Abstract: The use of variable well perforation and injection strategies can improve the efficiency of geological carbon storage operations. We develop a new multimodal auto-regressive transformer surrogate to model these operations under geological uncertainty. A modified SEAM CO2 geomodel, which involves a faulted system with three stacked aquifers, is considered. The two injection wells are perforated in stages, from bottom to top, with the stage durations and individual well injection rates treated as control variables.

摘要： 采用可变的井眼射孔和注入策略可以提高地质碳封存作业的效率。我们开发了一种新的多模态自回归 Transformer 代理模型，用于在地质不确定性条件下对这些作业进行建模。研究考虑了一个改进的 SEAM CO2 地质模型，该模型涉及一个包含三个叠置含水层的断层系统。两口注入井从下到上分阶段射孔，并将各阶段持续时间和单井注入速率作为控制变量。

The surrogate model processes three input modalities - the 3D geomodel, scalar parameters characterizing relative permeability functions, and control variables - through separate encoders. These are fused via self-attention in a transformer encoder, and a temporal decoder generates predictions auto-regressively through encoder-decoder cross-attention. The surrogate is trained, using 4000 GEOS flow simulations, to predict saturation and pressure at monitoring locations, total injected and mobile CO2 mass, and saturation footprints.

该代理模型通过独立的编码器处理三种输入模态：三维地质模型、表征相对渗透率函数的标量参数以及控制变量。这些模态通过 Transformer 编码器中的自注意力机制进行融合，随后时间解码器通过编码器-解码器交叉注意力机制自回归地生成预测结果。该代理模型利用 4000 次 GEOS 流动模拟进行训练，旨在预测监测点的饱和度和压力、注入及流动的 CO2 总质量以及饱和度分布范围。

For a new test set, involving randomly sampled geomodels and control variables, the surrogate achieves a median saturation MAE of 0.028 and median relative errors of 0.2-5% for the other quantities of interest. Importantly, it captures the switch from rate to bottom-hole-pressure control. The surrogate model is used within a hierarchical Markov chain Monte Carlo data assimilation procedure for a synthetic true model under three operational strategies. Substantial uncertainty reduction is achieved for key metaparameters, particularly the fault permeabilities. Posterior predictions for saturation footprints and total injected and mobile CO2 mass are also shown to be generally consistent with true model results.

对于包含随机采样地质模型和控制变量的新测试集，该代理模型的饱和度平均绝对误差（MAE）中位数为 0.028，其他目标量的相对误差中位数为 0.2-5%。重要的是，它能够捕捉到从注入速率控制到井底压力控制的切换。该代理模型被应用于一种分层马尔可夫链蒙特卡洛（MCMC）数据同化程序中，针对三种作业策略下的合成真实模型进行了验证。结果表明，关键元参数（特别是断层渗透率）的不确定性得到了显著降低。饱和度分布范围以及注入和流动 CO2 总质量的后验预测结果也显示与真实模型结果基本一致。