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模型简化和数据同化方法增强二氧化碳羽流追踪

作者介绍了一种新颖的框架，将动态模式分解（一种数据驱动的模型简化技术）与直接数据同化相结合，以简化二氧化碳羽流演变模型的校准。

图 1 — 本研究中使用的动态模型有 546,920 个单元。该图突出显示了用于 CO2 储存模拟的伊利诺伊盆地的空间分布和结构配置。
图 1 — 本研究中使用的动态模型有 546,920 个单元。该图突出显示了用于 CO2 储存模拟的伊利诺伊盆地的空间分布和结构配置。
来源：SPE 221411。

通过碳捕获和储存 (CCS) 技术应对气候变化需要先进的地下二氧化碳 (CO 2 ) 储存监测计算方法。本研究重点关注伊利诺伊盆地迪凯特项目 (IBDP)，这是一项旨在将二氧化碳注入深层盐水层的 CCS 示范试点项目。引入了一种结合动态模式分解 (DMD)（一种数据驱动的模型简化技术）和直接数据同化的新框架，以简化二氧化碳羽流演变模型的校准。这种方法增强了快速跟踪能力，并克服了传统高保真数值油藏模拟（称为全阶模型 (FOM)）的计算挑战。

介绍

与大多数降阶模型相比，DMD 代表了一种用于多孔介质流动的更优越的方法，因为它能够有效地捕捉复杂的流动动力学。

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原文链接/JPT
Decarbonization

Model-Reduction and Data-Assimilation Approach Enhances Carbon-Dioxide Plume Tracking

The authors introduce a novel framework combining dynamic mode decomposition, a data-driven model-reduction technique, with direct data assimilation to streamline the calibration of carbon-dioxide plume evolution models.

Fig. 1—The dynamic model used in this study has 546,920 cells. The map highlights the spatial distribution and structural configuration of the Illinois Basin used for CO2-storage simulations.
Fig. 1—The dynamic model used in this study has 546,920 cells. The map highlights the spatial distribution and structural configuration of the Illinois Basin used for CO2-storage simulations.
Source: SPE 221411.

Addressing climate change through carbon capture and storage (CCS) technologies requires advanced computational methodologies for subsurface carbon-dioxide (CO2) storage monitoring. This study focuses on the Illinois Basin Decatur Project (IBDP), a CCS demonstration pilot aimed at CO2 injection into a deep saline reservoir. A novel framework combining dynamic mode decomposition (DMD), a data-driven model-reduction technique, with direct data assimilation is introduced to streamline the calibration of CO2 plume evolution models. This approach enhances rapid tracking and overcomes the computational challenges of traditional high-fidelity numerical reservoir simulations known as the full-order model (FOM).

Introduction

DMD represents a superior approach for flow in porous media compared with most reduced-order models because of its ability to capture complex flow dynamics effectively.

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