Flexible Funding 2023: Mohadeseh Motie, Heriot-Watt University

Enhanced Decision-Making in CO2 Storage Projects: A Bayesian Networks Approach for Value of Information Analysis


Key facts about this Flexible Funding research project

Institution: Heriot-Watt University
Department: Sch of Energy, Geoscience, Infrastructure & Society
Start date: 1 October 2023
Principal investigator: Mohadeseh Motie
Co-Investigators: Dr Babak Jafarizadeh
Amount awarded by UKCCSRC: £9,256

Why is this research needed?

Investment decisions in the CO2 storage industry is increasingly uncertain due to various factors. These include the feasibility and performance of CO2 storage technologies, the effectiveness of storage sites in securely trapping and retaining CO2 over the long term, the economic attractiveness influenced by dynamic carbon prices, and the ever-evolving policy frameworks. The lack of comprehensive information and the interplay of these uncertainties make decision-making in CO2 storage projects a complex task, underscoring the necessity for research and advanced analysis to effectively navigate risks and enhance the efficacy of climate change mitigation efforts.

The primary aim of this study is to shed light on the decision-making process for subsurface CO2 storage in the fight against climate change. To address associated challenges in this regard, we propose an integrated valuation approach using Bayesian networks, which acts as a smart tool that takes into account all pertinent factors and calculates the optimal decision based on the available information.

Our approach takes a step further by conducting a value of information (VoI) analysis. By quantifying the benefits of acquiring additional data to reduce uncertainties and improve decision-making, we can determine the most valuable information to collect. For instance, should resources be allocated to conducting more geological surveys to gain a clearer understanding of a storage site’s stability, or would it be wiser to invest elsewhere? This analysis allows decision-makers to prioritize their efforts and resources effectively.

Through sensitivity analysis, we investigate how different factors, including project costs, storage capacity, and environmental risks, influence decision outcomes. By understanding the variables that have the most significant impact, we can make informed choices and optimize decision-making processes.

To validate our method, we apply it to a real-world CO2 storage project, taking into account the specific challenges and uncertainties associated with the site. This practical application enables us to provide actionable recommendations and valuable insights to decision-makers involved in similar projects. Ultimately, our research aims to improve decision-making processes in CO2 storage projects, making them more efficient and effective in mitigating CO2 emissions.

What is this research investigating?


  1. Develop an innovative and advanced approach for enhancing decision-making in CO2 storage projects’ investments using Bayesian networks.
  2. Improve the accuracy and reliability of decision-making processes in CO2 storage projects by incorporating geological uncertainties, technical feasibility, economic considerations, and regulatory constraints within a unified Bayesian network framework.
  3. Analyze the value of information (VoI) in CO2 storage projects by quantifying the potential benefits of acquiring additional data to reduce uncertainties and enhance decision outcomes.
  4. Investigate the impact of different decision variables, such as price volatilities, project cost, storage capacity, and environmental risks (leakage), on the decision-making process using uncertainty analysis within an integrated Bayesian network framework.
  5. Developing a versatile and user-friendly framework for use in various storage initiatives to facilitate decision making and value creation.
  6. Compare the effectiveness of the proposed Bayesian network approach with traditional decision-making methods, such as decision trees or Monte Carlo simulations, to demonstrate its superiority in supporting decision-making in CO2 storage projects.
  7. Apply the developed Bayesian network model to a real-world case study of a CO2 storage project, considering site-specific characteristics and uncertainties, to validate its practical applicability and generate actionable insights for decision-makers.
  8. Quantify the potential value of information provided by different data acquisition strategies, considering their cost and impact on reducing uncertainties, to assist decision-makers in prioritizing data collection efforts and resource allocation.
  9. Provide recommendations and guidelines for decision-makers involved in CO2 storage projects based on the insights and findings from the Bayesian network analysis and value of information assessment, enabling more informed and robust decision-making processes.
  10. Disseminate research findings through academic publications, conference presentations, and workshops, to share knowledge and promote the adoption of enhanced decision-making approaches in CO2 storage projects globally.
  11. Secure additional funding and resources to support further research and development in the field of enhanced decision-making in CO2 storage projects, leveraging the success and impact of this research project.
  12. Contribute to the achievement of national and international goals related to reducing greenhouse gas emissions and addressing climate change by improving the efficiency and effectiveness of CO2 storage projects through enhanced decision-making processes

What does the research hope to achieve?

The research on “Enhanced Decision-Making in CO2 Storage Projects: A Bayesian Networks Approach for Value of Information Analysis” holds significant benefits for various stakeholders involved in CO2 storage projects and the broader global community.

Industry Professionals and Project Developers: By applying the Bayesian networks approach and value of information analysis, they can make more informed decisions regarding projects’ investment, technology deployment, and resource allocation. This will enhance project efficiency, reduce risks, and increase the likelihood of successful CO2 storage implementation, leading to improved financial outcomes for industry players.

Government and Policy Makers: Gaining a deeper understanding of the factors influencing CO2 storage project decisions can inform the development of supportive policies regarding the emissions market, and financial incentives that encourage investments in CO2 storage projects. Effective policies will help accelerate the deployment of CO2 storage projects, contributing to national and international climate change mitigation efforts.

Environmental Organizations and Activists: By improving decision-making processes, the research presents the environmental risks associated with CO2 storage projects. This can help build public trust and acceptance, facilitating the transition to a low-carbon economy and the achievement of sustainability goals.

Local Communities and Stakeholders: By employing a rigorous decision-making framework, potential environmental impacts can be better assessed, ensuring the safety and well-being of communities residing near CO2 storage project sites.

Scientific and Research Community: The research provides a novel approach utilizing Bayesian networks and value of information analysis, which can serve as a foundation for further research and advancements in the field. The scientific community can build upon these findings to refine methodologies, improve models, and develop more sophisticated tools for decision-making in CO2 storage projects.

In essence, the study of enhanced decision-making in CO2 storage projects has wide-ranging advantages for society. By enhancing the efficiency and effectiveness of decision-making processes in these projects, we can incentivize investors and financial institutions to identify lucrative ventures and allocate resources to projects with lower risks and higher potential returns, all based on better-informed judgments.

Research outputs

This research is ongoing. Outputs will be shared below as they become available.