Digitally & AI-enabled CCS: Lessons from the oil & gas industry for scalable decarbonisation

In this chapter, we draw lessons from oil & gas to show how digital and subsurface expertise can accelerate carbon capture and storage as a cornerstone of decarbonisation.

Authored by Dr. Hooman Haghighi of Wood, this chapter examines how digitalisation and artificial intelligence can accelerate Carbon Capture and Storage (CCS), drawing on decades of oil and gas expertise. It argues that while oil and gas provide a strong technical foundation, CCS requires new digital frameworks to overcome its unique scale, cost, and risk challenges. 

The chapter begins by highlighting the paradox of CCS: while it is a cornerstone of decarbonisation, projects remain complex, costly, and data-poor. Unlike oil and gas, CCS lacks a robust operational history, making investment and insurance difficult. To bridge this gap, the author proposes leveraging physics-based models combined with AI to simulate, optimise, and de-risk the full CCS value chain—from capture to long-term storage. 

Key challenges explored include limited subsurface data (especially in saline aquifers), high operational costs, measurement and monitoring difficulties, and insurance barriers. The text emphasises the importance of shared infrastructure hubs to reduce costs and risk, alongside the use of AI-enabled modelling to explore thousands of “what if” scenarios, optimise design, and strengthen confidence in project bankability. 

The chapter also addresses post-closure obligations, where monitoring and verification can extend for decades. Here, AI platforms become indispensable in managing vast datasets, detecting leaks, and optimising costly seismic and monitoring campaigns. A strong case is made for physics-aided AI, combining the structure of physical laws with the adaptability of machine learning to provide reliable, scalable insights. 

Finally, the author sets out a roadmap from pilot projects to scaled CCS operations, stressing that digital systems and AI must evolve from optional add-ons into core operational competencies. In doing so, the energy industry can transform CCS into a predictable, efficient, and commercially viable decarbonisation pathway. 

Benefits of reading the chapter

• Industry Lessons: Gain an understanding of how decades of oil and gas experience provide a foundation for CCS while highlighting where new approaches are needed. 

• Risk & Investment Insight: Learn how digitalisation and AI can address investor, insurer, and operator concerns by improving transparency, risk modelling, and bankability. 

• Practical Frameworks: Discover how physics-aided AI bridges the gap between limited real-world CCS data and the need for reliable large-scale models. 

• Operational Value: Understand the role of AI-enabled digital twins, monitoring, and measurement in optimising CCS operations and reducing long-term monitoring costs. 

• Strategic Roadmap: Follow the step-by-step progression from pilot projects to scaled CCS, and see how digital systems become a strategic necessity rather than an optional tool. 

• Future Outlook: Recognise why success in CCS will depend on early investment in digital infrastructure, common data standards, and AI integration. 

This chapter is vital for energy professionals, policymakers, and investors looking to understand not only the technical and operational challenges of CCS but also the digital and AI-enabled pathways that make scalable decarbonisation achievable.