论文标题
学到的耦合反转以与傅立叶神经操作员进行碳固隔监测和预测
Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators
论文作者
论文摘要
对碳储存的地震监测是一个具有挑战性的问题，涉及流体流体物理和波浪物理学。此外，监测通常需要求解器，以使这些物理耦合并可以有效地反转感兴趣的地下特性。为了大幅度降低计算成本，我们基于波浪建模操作员，岩石属性转换和代理流体流量模拟器引入了学习的耦合反转框架。我们表明，我们可以准确地使用傅立叶神经操作员作为流体流量模拟器的代理，以占计算成本的一部分。我们通过合成实验证明了我们提出的方法的功效。最后，我们的框架扩展到碳固并预测，在那里我们有效地使用替代傅里叶神经操作员以接近零的额外成本预测二氧化碳羽流。
Seismic monitoring of carbon storage sequestration is a challenging problem involving both fluid-flow physics and wave physics. Additionally, monitoring usually requires the solvers for these physics to be coupled and differentiable to effectively invert for the subsurface properties of interest. To drastically reduce the computational cost, we introduce a learned coupled inversion framework based on the wave modeling operator, rock property conversion and a proxy fluid-flow simulator. We show that we can accurately use a Fourier neural operator as a proxy for the fluid-flow simulator for a fraction of the computational cost. We demonstrate the efficacy of our proposed method by means of a synthetic experiment. Finally, our framework is extended to carbon sequestration forecasting, where we effectively use the surrogate Fourier neural operator to forecast the CO2 plume in the future at near-zero additional cost.
