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This is the conference paper accompanying an oral presentation at the 58th US Rock Mechanics/Geomechanics Symposium (ARMA) June 23 to 26 in Golden, CO. The paper highlights using machine learning to quantify fracture network for carbon storage and is part of the SMART Initiative project applications.
CCUS technologies are crucial solutions for mitigating climate change by reducing CO2 emissions from industrial operations and energy sectors. This review critically examines the current state of CCUS technologies, and highlights advancements, challenges, regulatory frameworks, and future directions. It comprehensively analyzes carbon capture methods, such as pre-combustion, post-combustion, and oxy-fuel combustion capture, while comparing their efficiencies and limitations. The review also explores carbon utilization techniques, such as direct and indirect utilization, emphasizing their potential applications and technological constraints. Additionally, it assesses various carbon storage methods, focusing on geological, ocean, and mineralization storage, and discusses their capacity, feasibility, and environmental implications. The study reviews the policy and regulatory frameworks, economic viability, market trends, and environmental sustainability of CCUS. By identifying research gaps and recommending future research priorities, this review aims to guide the development of more efficient/effective, and cost-effective CCUS technology, ensuring their role in a sustaining low-carbon future. This review provides a forward-looking perspective, a critical and interdisciplinary analysis that assesses the current state of CCUS technologies, and further provides a roadmap for future development.
Recent advances in instrumentation have made it feasible to measure the transient strain tensor caused by small changes in fluid volume or pressure in the subsurface and this has opened the door to new opportunities for characterization and monitoring during CCUS. We have demonstrated this method by deploying strainmeters at shallow depths (30 to 40m) and then conducting injection well tests in an underlying reservoir at 530m depth. The resulting data indicated that the horizontal strain at shallow strainmeters was tensile and the vertical strain was compressive. The radial strain was less than the horizontal strain, and the strain rates decreased from 100 nanostrain/day to roughly 10 ne/d over a few days (1 nanostrain = 1 part per billion strain). We then used the strain data to estimate reservoir properties, geometry and pressure through inversion of poroelastic forward models using both numerical and novel analytical methods. The average horizontal strain in the caprock resembles the transient pressure in the underlying reservoir and classic type-curve methods from transient well testing can be used for preliminary interpretations of strain data. We have developed fast, closed-form analytical solutions to a pressurized poroelastic inclusion and inhomogeneity in a half-space. Numerical models developed using finite element methods allow more details of the subsurface to be included in the inversion, but they require much longer run times and this makes inversion cumbersome using standard methods. We have developed an inversion approach that uses a proxy model created using machine learning to do most of the forward calculations. This approach markedly reduces the computational requirements and makes it feasible to use Bayesian inversion with large numerical models. Bayesian inversion is important because it provides predictions with uncertainties, which makes the results useful for decision making. We have shown with field tests and simulations that the strain tensor in the caprock is sensitive to pressure in the reservoir, reservoir properties and boundaries, and pressure in the caprock caused by leaks. These results indicate that measuring and interpreting the shallow strain tensor could be a valuable tool for both initial reservoir characterization efforts and long-term monitoring during CCUS. Recent advances in instrumentation have made it feasible to measure the transient strain tensor caused by small changes in fluid volume or pressure in the subsurface and our objective was to evaluate opportunities for strain monitoring during characterization and monitoring for CCUS. Our approach was to deploy strainmeters at shallow depths (30 to 40m) and then conduct injection well tests in an underlying reservoir at 530m depth. The results indicate that the horizontal strain at shallow strainmeters was tensile and the vertical strain was compressive. The radial strain was less than the horizontal strain, and the strain rates decreased from 100 nanostrain/day to roughly 10 ne/d over a few days (1 nanostrain = 1 part per billion strain). We then used the strain data to estimate reservoir properties, geometry and pressure through inversion of poroelastic forward models using both numerical and novel analytical methods. The average horizontal strain in the caprock resembles the transient pressure in the underlying reservoir and classic type-curve methods from transient well testing can be used for preliminary interpretations of strain data. We have developed fast, closed-form analytical solutions to a pressurized poroelastic inclusion and inhomogeneity in a half-space. Numerical models developed using finite element methods allow more details of the subsurface to be included in the inversion, but they require much longer run times and this makes inversion cumbersome using standard methods. We have developed an inversion approach that uses a proxy model created using machine learning to do most of the forward calculations. This approach markedly reduces the computational requirements and makes it feasible to use Bayesian inversion with large numerical models. Bayesian inversion is important because it provides predictions with uncertainties, which makes the results useful for decision making. In conclusion, we have shown with field tests and simulations that the strain tensor in the caprock is sensitive to pressure in the reservoir, reservoir properties and boundaries, and pressure in the caprock caused by leaks. These results indicate that measuring and interpreting the shallow strain tensor could be a valuable tool for both initial reservoir characterization efforts and long-term monitoring during CCUS.
Here, to understand the system-level interactions between the entities in Carbon Capture, Utilization, and Storage (CCUS), an agent-based foundational modeling tool, CCUS-Agent, is developed for a large-scale study of transportation flows and infrastructure in the United States. Key features of the tool include (i) modular design, (ii) multiple transportation modes, (iii) capabilities for extension, and (iv) testing against various system components and networks of small and large sizes. Five matching algorithms for CO 2 supply agents (e.g., powerplants and industrial facilities) and demand agents (e.g., storage and utilization sites) are explored: Most Profitable First Year (MPFY), Most Profitable All Years (MPAY), Shortest Total Distance First Year (SDFY), Shortest Total Distance All Years (SDAY), and Shortest distance to long-haul transport All Years (ACAY). Before matching, the supply agent, demand agent, and route must be available, and the connection must be profitable. A profitable connection means the supply agent portion of revenue from the 45Q tax credit must cover the supply agent costs and all transportation costs, while the demand agent revenue portion must cover all demand agent costs. A case study employing over 5500 supply and demand agents and multimodal CCUS transportation infrastructure in the contiguous United States is conducted. The results suggest that it is possible to capture over 9 billion tonnes (GT) of CO 2 from 2025 to 2043, which will increase significantly to 22 GT if the capture costs are reduced by 40 %. The MPFY and SDFY algorithms capture more CO 2 earlier in the time horizon, while the MPAY and SDAY algorithms capture more later in the time horizon.
Conference paper presented at 17th International Conference on Greenhouse Gas Control Technologies (GHGT-17), Calgary, Alberta, Canada, October 20–24, 2024. This paper presents an overview of the Plains CO 2 Reduction (PCOR) Partnership Initiative and the current commercial carbon capture, utilization, and storage (CCUS) activity in the region, discusses challenges facing CCUS in the region, and highlights important challenges that have recently been overcome to enable CCUS deployment in the region.
The overall objective of this research project was to develop a non-invasive acoustic-emission (AE)-based technology that can accurately predict the location and movement of CO 2 through a confining layer in a carbon capture, utilization, and storage (CCUS) system. Specifically, the aim was to explore the feasibility of using the Haines jumps AEs as a passive method to track movement of CO 2 in reservoirs used for CCUS applications.
Conference presentation at Carbon Capture & Storage Summit, Minneapolis, Minnesota, June 10-12, 2024. An overview of the Plains CO 2 Reduction (PCOR) Partnership Initiative and active commercial carbon capture, utilization, and storage (CCUS) activity in the region. The presentation discusses solutions for CCUS deployment challenges.
The significant role pressure data can play in CCUS projects
Building Trust: How to Handle the Challenging Conversations with CCUS Stakeholders
Geologic Diversity in the SECARB Region and CCUS Considerations
The overarching goal of this work is to determine how the United States can avoid being constrained by transportation limitations as the CCUS process evolves to a full gigaton-level system. The model is data-driven, open to CO2 supply and demand points defined as input data, and designed to allow for transportation on waterways, rail, or pipeline.
This is an invited guest speaker's presentation. The present covers three use cases by applying machine learning techniques. The use cases include fracture analysis for CCUS: IBDP study, multiple level of fracture network analysis and tool: HFTS1 study, Frac-Hit with Middleland Basin datasets from collaborations with Company A.
The “Direct Air Capture Recovery of Energy for CCUS Partnership (DAC RECO 2 UP)” project employs a team approach and supports the U.S. Department of Energy Office of Fossil Energy and Carbon Management’s (DOE-FECM) goal to decrease the cost of capture through the testing of existing direct air capture (DAC) materials in integrated field units that produce a concentrated carbon dioxide (CO 2 ) stream of at least 95% purity. Solid-amine CO 2 adsorption-desorption contactor technology, proven in the laboratory, is undergoing high-fidelity design/validation. Recoverable energy is readily available from a large number of commercial locations where DAC can be deployed; therefore, advancing the fidelity of energy recovery to directly reduce the cost of DAC is a key project objective. In addition, many commercial facilities have low-concentration CO 2 vents that are uneconomical to treat alone but could provide more efficient mass and thermal transport to DAC systems with integrated energy recovery and flexible CO 2 treatment capabilities. Technology scale-up leverages past research and occurs in a commercially relevant environment at the National Carbon Capture Center. Prescreening techno-economic analysis, risk assessments, and life cycle analysis are being performed by experienced team members. Results of the project will address critical technical barriers that, when solved, will improve the capital and operating costs of DAC while validating commercial relevance of cost and product quality/need.
Ex-situ mineralization processes leverage the reaction of alkaline materials with CO 2 to form solid carbonate minerals for carbon capture, utilization, and storage. Annually, enough alkaline waste is generated to reduce global CO 2 emissions by a significant percentage via mineralization. However, while the reaction is thermodynamically favorable and occurs spontaneously, it is kinetically limited. Thus, a number of techniques have emerged to increase the efficiency of mineralization to achieve a scalable process. In this review, we discuss mineralization of waste streams with significant potential to scale to high levels of CO 2 sequestration. Focus is placed on the effect of operating parameters on carbonation kinetics and efficiency, methods, cost, and current scale of technologies.
Not provided.
The objective of this project was to design a carbon capture system that could be installed on a 220 MMSCFD blue hydrogen (Blue H 2 ) facility (Blue H 2 Plant) utilizing auto-thermal reforming (ATR) technology and identify potential sequestration options for the carbon dioxide (CO 2 ) produced from the Blue H 2 Plant.
Presentation at the 58th US Rock Mechanics/Geomechanics Symposium (ARMA) June 23 to 26 in Golden, CO. The presentation highlights using machine learning to quantify fracture network for carbon storage and is part of the SMART Initiative project applications.
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