First EAGE Workshop on Geophysical Techniques for Monitoring CO2 Storage

One need is cost-effective, risk-based technology for large scale deployment, especially in the field of MMV. Past demonstrations and first-of-kind projects deployed many MMV strategies for enhance-oil recovery (EOR) and for small to medium scale CO2 sequestration projects with technologies that are not cost-effective under current economic constraints of CCS scale-up. Dedicated emerging MMV field tests and technology verifications are often performed over days or weeks, in shallow injection targets and with limited injection volumes, raising questions for performance efficacy for megaton/year injection scenarios. Therefore, numerous cost-effective tools and techniques are caught in the catch-22 of not being used at large scale because they have not been demonstrated for scale-up at large CCS projects yet.

One potential solution might be a project collaboration between a site operator, peers, service providers, and vendors teaming up and pooling funding to select, prioritize, deploy, and test pre-TRL 9 MMV technologies at a site injection at commercial rates and volumes for demonstration to regulatory entities.

This study evaluates how Montney turbidite sandstones and siltstones respond to brine saturation and four-week super-critical CO2 (Sc-CO2) aging. Laboratory UCS, triaxial, and ultrasonic tests on cores from two wells reveal three strength-stiffness clusters tied to lithology and bulk density. After Sc-CO2 exposure, siltstones lose 30–35 % of strength and stiffness, whereas sandstones weaken by only 5–10 %, highlighting lithology-specific resilience. This geomechanics data for the Montney provide critical calibration points for cap-rock integrity and injectivity models, reducing uncertainty for upcoming CCS projects in the Western Canada Sedimentary Basin

The Sleipner project, offshore Norway marks the first successful large-scale underground carbon sequestration initiative, having stored over 18.5 million tons of CO2 by 2020. Since 1996, CO2 from nearby fields has been injected into the Utsira formation. Although time-lapse seismic data is available, the limited resolution of pre-injection data has constrained a full understanding of the reservoir’s geological structure, hindering accurate predictions of CO2 plume migration.

This paper shows the enhancement of the pre-injection seismic data through sparse layer spectral inversion, improving resolution and creating new seismic attributes, such as stratigraphic continuity and apparent time thickness. This leads to a better understanding of the geological factors influencing plume migration. The findings uncover previously unseen features in the reservoir, including both continuous and discontinuous reflections, as well as an incised channel system. These insights shed new light on the subsurface geology affecting CO2 movement, offering improved predictions for future carbon capture and storage (CCS) projects.

Gamma Ray Flow Path Visualization (GR-FPV) is a new, cost-effective method for monitoring CO2 injection and containment in carbon sequestration projects. Developed from analysis of the Illinois Basin – Decatur Project (IBDP), GR-FPV uses anomalies in natural gamma ray (GR) logs as indirect tracers of subsurface fluid migration. This approach is inspired by in situ recovery (ISR) mining techniques, where CO2 reacts with saline formation water to create a lixiviant that mobilizes uranium, altering GR readings.

GR-FPV enables high-resolution tracking of CO2 movement using standard well-logging tools, offering a passive, continuous monitoring solution. It provides a more affordable and practical alternative to time-lapse seismic imaging and supports real-time injection management and regulatory compliance. Data from IBDP’s CCS1 well supports GR-FPV’s reliability and effectiveness. The method holds promise for improving containment assurance, reducing costs, and supporting adaptive reservoir control, and the team invites collaboration for further development and field deployment.

Geological CO2 storage is a promising climate mitigation option, but its success relies on ensuring long-term containment. We present a proof-of-concept framework that couples synthetic seismic modeling with machine learning to assess the seismic detectability of CO₂ leakage. Based on well log data from the Sleipner field, we create 1000 random synthetic velocity and density models, incorporating realistic stratigraphy and CO2-brine fluid substitution. Using finite difference modelling, we generate 2D acoustic gathers for each model, which serve as input for our machine learning (ML) framework. An unsupervised autoencoder learns latent features from 900 training gathers (validated on the remaining 100 gathers). A regression model (ML1) maps latent features to geologic/leakage parameters, generalizing to 100 new, unseen models. It recovers latent features closely as training loss drops from 9.67 to 0.029 and validation loss to 0.026. A binary classifier (ML2) detects leakage from ML1-predicted features, achieving 80% validation accuracy, but exhibits weak probability calibration, as the training loss decreases from 0.69 to 0.67, consistent with the limited dataset (90 training / 10 validation). Applied to Sleipner 2001 monitor baseline data (expected contained), ML2 returns a leakage probability of 51%, despite its weak performance and domain shift from synthetic to real data. This indicates that synthetic-trained models can distinguish between leaky and contained responses at the threshold level; however, robust deployment will require broader and more diverse training data. This framework provides a path to quantify sensitivity and define detection limits for monitoring CO2 leakage.

Geophysical methods for monitoring permanent underground storage of carbon dioxide (CO2) and other materials associated with the energy and resource sector are demonstrating the potential to generate a new wave of large volume data sets. Driven by the dense spatial and temporal sampling capabilities of distributed fiber optic sensors and decadal post injection site care requirements of regulatory governance, these petabyte scale data volumes provide a challenge to current embedded data workflows methodologies for geophysical data. They also create an opportunity to leverage emerging industry accepted optimum practices for working with time-series data on open-source cloud native data platforms. We report here on our testing and implementation of global data management techniques for site-specific geophysical data. We apply emerging technical capabilities to both legacy data sets utilized for assessment and evaluation of CO2 storage sites, and to data acquired to support long-term monitoring, measurement and verification (MMV) of storage conformance and compliance. Our ambition is to propose plans and approaches to the long-term curation of digital data, which is a critical but often overlooked subset of practical and efficient operational digital data ecosystems for onshore and offshore permanent subsurface carbon storage.

Effective monitoring strategies are essential for demonstrating containment and conformance in carbon capture, utilization, and storage (CCUS) operations to meet regulatory requirements. A comprehensive approach necessitates a toolbox of monitoring solutions, among which passive seismic monitoring plays a critical role in assessing containment and stress changes associated with CO2 injection. This study evaluates the benefits of a hybrid seismic monitoring approach when setting up a MMV plan for CCUS applications. A hybrid system integrates surface-based instruments (seismometers and accelerometers) with downhole monitoring components, including fiber optic arrays, 15 Hz geophone arrays, and low-frequency geophones. The combined approach enhances the detection of microseismic events (−M) to larger-scale seismicity (M+), improves depth accuracy, and increases sensitivity to microseismic activity. Additionally, downhole geophone arrays, depending on their configuration, enable real-time monitoring of caprock and casing integrity. By capturing the full spectrum of seismicity, hybrid monitoring systems provide critical insights for containment assurance and risk mitigation. This paper presents real-world data from CCUS projects and other subsurface injection operations to illustrate the advantages of hybrid seismic monitoring. The findings highlight the effectiveness of hybrid systems in improving the resolution and reliability of seismic data, thereby supporting the safe and efficient deployment of CCUS technologies.