用于建模地质碳封存中可变操作与量化不确定性的多模态自回归Transformer代理模型
Multimodal Auto-regressive Transformer Surrogate for Modeling Variable Operations and Quantifying Uncertainty in Geological Carbon Storage
- Stanford University（斯坦福大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出多模态自回归Transformer代理模型，用于地质碳封存中可变操作建模与不确定性量化，经4000次GEOS流模拟训练后，可精准预测相关参数并降低关键元参数的不确定性。
AI中文摘要：
可变井射孔与注入策略的应用可提升地质碳封存作业的效率。我们开发了一种新型多模态自回归Transformer代理模型，用于在地质不确定性下对这些操作进行建模。研究采用了改进的SEAM CO2地质模型，该模型包含一个带断层的系统及三个叠置含水层。两口注入井采用自下而上的分段射孔方式，将段持续时间与单井注入速率作为控制变量。该代理模型通过独立编码器处理三种输入模态——3D地质模型、表征相对渗透率函数的标量参数及控制变量，这些模态在Transformer编码器中通过自注意力机制融合，时序解码器则通过编码器-解码器交叉注意力机制自回归地生成预测结果。该代理模型使用4000次GEOS流模拟进行训练，以预测监测位置的饱和度与压力、总注入及可移动CO2质量，以及饱和度分布范围。针对包含随机采样地质模型与控制变量的新测试集，该代理模型实现了0.028的中位数饱和度平均绝对误差（MAE），以及其他目标量0.2%-5%的中位数相对误差，且能捕捉从速率控制到井底压力控制的切换。该代理模型被用于分层马尔可夫链蒙特卡洛数据同化流程中，以针对合成真实模型在三种作业策略下开展研究，关键元参数（尤其是断层渗透率）的不确定性得到显著降低，饱和度分布范围、总注入及可移动CO2质量的后验预测也与真实模型结果总体一致。
英文摘要：
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. 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. 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.
