AI in CCUS Workshop

Overview – AI in CCUS Workshop

The main aim of this workshop is to foster an understanding of the use of artificial intelligence (AI) in carbon capture, utilisation and storage (CCUS). The workshop focuses on fact-finding, i.e. reviewing the current status of AI in CCUS, with case studies showcasing what AI can bring to the table now and an outlook of what it might deliver in the future. The outcome of the workshop is to define the ‘grand challenges’ in this area and make the information usable for researchers, the CCUS community, SET plan developers and funding organisations.

Agenda

13:15 – Opening remarks & housekeeping

 Speakers:

• Abdul’Aziz Aliyu – IEAGHG
• Jasmin Kemper – IEAGHG

13:30 – Keynote: Opportunities and challenges of AI in CCUS: insights from the ICEF roadmap

AI brings unparalleled new abilities for computers to manage complex tasks. Potential applications (and impact) of AI and machine learning to CCUS are immense, including discovery of new carbon capture materials, design of new reactors, improved integration and operation at existing facilities, multivariate optimization of pipelines, greenfield and brownfield characterization of CO₂ storage, in-field operation of storage sites, accelerated permitting, and public acceptance. Real risks (e.g., bias, hallucinations) and barriers (e.g., access to data, limited workforce readiness) can be and should be managed. Practitioners and policy makers should take up these applications with speed. 

Speaker:

Julio Friedmann – Carbon Direct

14:00 – AI in CO2 capture

Generative AI and high-performance computing for the accelerated discovery of materials for energy storage and conversion

I will describe a computational workflow that combines generative AI, high performance computing, computational chemistry software, and high throughput screening to accelerate the discovery and screening of metal organic frameworks that may be used for carbon management and hydrogen storage. I will discuss the capabilities of this workflow to quantify the performance of energy materials in realistic conditions, including the presence of water and mixture of gases.

Speaker:

Eliu Huerta – ANL

14:30 – AI in CO2 transport

Speaker:

• TBC

15:00 – Break

15:15 – AI in CO2 storage

Transforming Subsurface Analysis with Advanced AI and Machine Learning Techniques

This project employs advanced AI and Machine Learning to transform geological data into insights crucial for carbon capture and storage (CCS) and mineral exploration. Enhancing seismic imaging, generating synthetic datasets, and interpreting depositional environments using Generative AI, Computer Vision and CNNs, it improves the identification of CCS sites and mineral deposits. By integrating multi-scale geological data, this research aims to provide a holistic approach that enhances the reliability of CCS operations and improves precision in mineral resource exploration.it

Speaker:

• Athos Nathanial – Colorado School of Mines

Speakers:

• Jeroen Snippe – Shell
• Sara Minisini – Shell

15:45 – AI in Supply Chain Analysis

AI for analysing biomass supply for BECCS

Speaker:

• TBC

AI use cases for CCUS permitting

CCUS permitting involves preparing and reviewing extensive documentation of project narratives that span subsurface, wells, injection operations, monitoring, reporting and verification, emergency response procedures, financial responsibilities, environmental impact, and community engagement. In the case of the US, notices of deficiencies and requests for additional information for Class VI permits have increased EPA review times from 24-month targets to 40+ months, impacting project economics and delaying decarbonization efforts. We discuss AI use cases in the context of Class VI permits, and how it can improve process efficiencies in preparation, administrative completeness and technical reviews.

Speaker:

• Eric Redmond – ClassVI.ai

16:15 – AI in Supply Chain Analysis

Advancing CCUS with Generative AI: Agent George and The Open Footprint Data Model

Net Zero Matrix is advancing a transformative AI framework for emission data management that is tightly integrated with the Open Footprint data model. This enables the precise alignment of data with different emission sources and sinks, calculation methodologies and standards used across the CCUS ecosystem. At the heart of this framework is Agent George combining generative AI with the tools needed to address the complex challenge of collecting emissions data from the varied actors within a CCUS value chain. Agent George provide a novel data Extraction, Transformation and Loading (ETL) toolkit that intelligently aggregates and harmonizes diverse data streams for use in digital Measurement, Reporting and Verification (dMRV) systems.

Speakers:

• Jon Curtis – Petrolink
• Bertrand Rioux – Net Zero Matrix

AI-powered activity-based GHG emission calculation

At Demetrics, we help sustainability teams evaluate the greenhouse gas emissions of their corporations’ purchased goods. We leverage generative AI to help map goods with complex life cycle inventory databases and calculate the associated CO₂ emissions.

Speaker:

• Jerome Mutschler – Demetrics

16:45 – Closing remarks and key take-aways

Speaker:

• Abdul’Aziz Aliyu – IEAGHG
• Jasmin Kemper – IEAGHG