東華大學圖書館 |

Dynamic Modelling and Monitoring of Geological Carbon Sequestration Assets /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dynamic Modelling and Monitoring of Geological Carbon Sequestration Assets // Yunan Li.
Author:	Li, Yunan,
Description:	1 electronic resource (221 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-09, Section: B.
Contained By:	Dissertations Abstracts International86-09B.
Subject:	Geological engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31951944
ISBN:	9798310307872

Dynamic Modelling and Monitoring of Geological Carbon Sequestration Assets /
Li, Yunan,

Dynamic Modelling and Monitoring of Geological Carbon Sequestration Assets /Yunan Li. - 1 electronic resource (221 pages)

Source: Dissertations Abstracts International, Volume: 86-09, Section: B.

Geological carbon storage (GCS) holds a significant place in the energy transition towards a sustainable future, because it enables the capacity for large amounts of carbon dioxide storage in the subsurface. This approach is recognized as one of the few powerful strategies to reduce atmospheric carbon dioxide concentration and meet IPCC goals to control global warming under 1.5 degree Celsius. However, the development of GCS assets faces numerous challenges. These include complex, heterogeneous, and dipping storage formations, geomechanical risks, controlling carbon dioxide plume migration and size, managing subsurface pressure responses, addressing nearby faults, and achieving or further exceeding injection targets. Furthermore, GCS projects demand innovative and cost-effective monitoring techniques that can cover extensive areas in near real-time, crucial for managing financial budgets and monitoring, measurement, and verification (MMV). In response to these challenges, we developed a systematic, fully automated toolbox named pyCCUS. This toolbox standardizes approaches to address these challenges, reduces the learning curve for multiple stakeholders, enhances operational efficiency, and provides flexible optimization strategies throughout the lifespan of GCS projects. By enabling agility and accommodating plan changes, pyCCUS aids the scaling of GCS assets globally, accelerating progress towards a net-zero society. We illustrate our solutions in this dissertation using a field case in Kern County, California. In this dissertation, we introduce pyCCUS as a comprehensive toolbox designed to tackle challenges for GCS projects. pyCCUS addresses a broad spectrum of subsurface issues, from natural resources production to gas or fluids storage. It has been enhanced with features and evaluation metrics specifically tailored for carbon dioxide storage. It is capable to delineate dynamically and evaluate the evolution of carbon dioxide plume shapes and sizes. Concurrently, it quantifies pressure footprints to monitor their development under various strategies. It maximizes carbon dioxide storage security while minimizing geomechanical risks through a novel deviated well trajectory optimization module. Additionally, pyCCUS integrates several modules to bolster its utility as a versatile toolbox, including sensitivity analysis, statistical analysis, optimization, and AI models. These enhancements facilitate its role in addressing a wide range of subsurface engineering challenges. Next, we illustrate the process of optimizing well design to identify the best deviated injector for the GCS site in Kern County, California. In response to surface exposed Quaternary faults, we propose a fault-aware, risk-averse strategy for the subsurface, though further details remain to be specified. Our objective function is defined to balance the trade-off between pressure build-up at key places in the storage formation and carbon dioxide plume expansion in a risk-averse manner. pyCCUS allows us to visualize the optimization dynamics across all candidate designs in order to identify the optimal solution. We also validate the robustness of this optimal well design under various injection schemes and geological model realizations. The results demonstrate that the strategy deployed has the potential to increase the total injection amount by 52%, while maintaining acceptable carbon dioxide plume sizes and pressure responses at the study site. Additionally, we propose a novel monitoring approach to utilize satellite imagery for real-time monitoring of GCS sites. To begin with, we conducted a global sensitivity analysis to distinguish between factors that are sensitive or non-sensitive to the rate of land deformation. The results pave the way to understand information that is confidently recovered by interpreting InSAR satellite imagery. It also sets the foundation to identify ideal candidate sites for InSAR surveillance on GCS assets. Further, we have characterized the detectability limits for the field case, demonstrating the strong potential of this site for effective InSAR surveillance. As a final point, we present a systematic workflow that incorporates InSAR imagery into GCS asset surveillance. Adhering to traditional history-matching algorithms, we calibrate our numerical model using InSAR observations of land deformation. The numerical model is updated annually as the geological carbon dioxide storage progresses with the land uplift signals measured by InSAR in near real-time. A good history match is obtained using the initial eight years of InSAR imagery within an 18-year injection period. This history-matching process helps us to calibrate the parameters that influence land deformation in order to reduce simulation uncertainties for time beyond. Furthermore, we have developed AI models utilizing the U-Net based deep learning architecture for image-to-image prediction tasks. These models are specifically trained to interpret subsurface pressure footprints and carbon dioxide plumes from InSAR observational images. Preliminary study in this work has shown a strong correlation between land surface movements and subsurface pressure change. The AI models open the door for rapid predictions and enable real-time InSAR monitoring of GCS assets. We also assess the applicability and transferability of this approach to evaluate its potential for broader implementation and limitations. The integration of history-matching with innovative AI models provides a comprehensive view of GCS surveillance.

English

ISBN: 9798310307872Subjects--Topical Terms:

2122713
Geological engineering.

Dynamic Modelling and Monitoring of Geological Carbon Sequestration Assets /
LDR:06917nmm a22004093i 4500 001 2434570
005 20250912102044.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798310307872
035 $a (MiAaPQD)AAI31951944
035 $a (MiAaPQD)Stanfordxm725xv9179
035 $a AAI31951944
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Li, Yunan, $e author. $3 3824585
245 1 0 $a Dynamic Modelling and Monitoring of Geological Carbon Sequestration Assets / $c Yunan Li.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (221 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-09, Section: B.
500 $a Advisors: Kovscek, Anthony Committee members: Benson, Sally; Brandt, Adam; Mukerji, Tapan.
502 $b Ph.D. $c Stanford University $d 2024.
520 $a Geological carbon storage (GCS) holds a significant place in the energy transition towards a sustainable future, because it enables the capacity for large amounts of carbon dioxide storage in the subsurface. This approach is recognized as one of the few powerful strategies to reduce atmospheric carbon dioxide concentration and meet IPCC goals to control global warming under 1.5 degree Celsius. However, the development of GCS assets faces numerous challenges. These include complex, heterogeneous, and dipping storage formations, geomechanical risks, controlling carbon dioxide plume migration and size, managing subsurface pressure responses, addressing nearby faults, and achieving or further exceeding injection targets. Furthermore, GCS projects demand innovative and cost-effective monitoring techniques that can cover extensive areas in near real-time, crucial for managing financial budgets and monitoring, measurement, and verification (MMV). In response to these challenges, we developed a systematic, fully automated toolbox named pyCCUS. This toolbox standardizes approaches to address these challenges, reduces the learning curve for multiple stakeholders, enhances operational efficiency, and provides flexible optimization strategies throughout the lifespan of GCS projects. By enabling agility and accommodating plan changes, pyCCUS aids the scaling of GCS assets globally, accelerating progress towards a net-zero society. We illustrate our solutions in this dissertation using a field case in Kern County, California. In this dissertation, we introduce pyCCUS as a comprehensive toolbox designed to tackle challenges for GCS projects. pyCCUS addresses a broad spectrum of subsurface issues, from natural resources production to gas or fluids storage. It has been enhanced with features and evaluation metrics specifically tailored for carbon dioxide storage. It is capable to delineate dynamically and evaluate the evolution of carbon dioxide plume shapes and sizes. Concurrently, it quantifies pressure footprints to monitor their development under various strategies. It maximizes carbon dioxide storage security while minimizing geomechanical risks through a novel deviated well trajectory optimization module. Additionally, pyCCUS integrates several modules to bolster its utility as a versatile toolbox, including sensitivity analysis, statistical analysis, optimization, and AI models. These enhancements facilitate its role in addressing a wide range of subsurface engineering challenges. Next, we illustrate the process of optimizing well design to identify the best deviated injector for the GCS site in Kern County, California. In response to surface exposed Quaternary faults, we propose a fault-aware, risk-averse strategy for the subsurface, though further details remain to be specified. Our objective function is defined to balance the trade-off between pressure build-up at key places in the storage formation and carbon dioxide plume expansion in a risk-averse manner. pyCCUS allows us to visualize the optimization dynamics across all candidate designs in order to identify the optimal solution. We also validate the robustness of this optimal well design under various injection schemes and geological model realizations. The results demonstrate that the strategy deployed has the potential to increase the total injection amount by 52%, while maintaining acceptable carbon dioxide plume sizes and pressure responses at the study site. Additionally, we propose a novel monitoring approach to utilize satellite imagery for real-time monitoring of GCS sites. To begin with, we conducted a global sensitivity analysis to distinguish between factors that are sensitive or non-sensitive to the rate of land deformation. The results pave the way to understand information that is confidently recovered by interpreting InSAR satellite imagery. It also sets the foundation to identify ideal candidate sites for InSAR surveillance on GCS assets. Further, we have characterized the detectability limits for the field case, demonstrating the strong potential of this site for effective InSAR surveillance. As a final point, we present a systematic workflow that incorporates InSAR imagery into GCS asset surveillance. Adhering to traditional history-matching algorithms, we calibrate our numerical model using InSAR observations of land deformation. The numerical model is updated annually as the geological carbon dioxide storage progresses with the land uplift signals measured by InSAR in near real-time. A good history match is obtained using the initial eight years of InSAR imagery within an 18-year injection period. This history-matching process helps us to calibrate the parameters that influence land deformation in order to reduce simulation uncertainties for time beyond. Furthermore, we have developed AI models utilizing the U-Net based deep learning architecture for image-to-image prediction tasks. These models are specifically trained to interpret subsurface pressure footprints and carbon dioxide plumes from InSAR observational images. Preliminary study in this work has shown a strong correlation between land surface movements and subsurface pressure change. The AI models open the door for rapid predictions and enable real-time InSAR monitoring of GCS assets. We also assess the applicability and transferability of this approach to evaluate its potential for broader implementation and limitations. The integration of history-matching with innovative AI models provides a comprehensive view of GCS surveillance.
546 $a English
590 $a School code: 0212
650 4 $a Geological engineering. $3 2122713
650 4 $a Remote sensing. $3 535394
650 4 $a Carbon sequestration. $3 601998
650 4 $a Geotechnology. $3 1018558
650 4 $a Environmental engineering. $3 548583
650 4 $a Sustainability. $3 1029978
690 $a 0466
690 $a 0775
690 $a 0640
690 $a 0799
690 $a 0428
710 2 $a Stanford University. $e degree granting institution. $3 3765820
720 1 $a Kovscek, Anthony $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-09B.
790 $a 0212
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31951944