Reducing Subsurface Uncertainty in Geo-energy Projects Using Microseismic and Geodetic Data
Doctoral Candidate: Tian Guo
Supervisor: Víctor Vilarrasa
Host institution: IMEDEA UIB-CSIC
Output type: literature review and summary research topic
Research questions:
- How fault can impact on fluid flows and how can this influence the safety of geological storage projects;
- How fluid injection or extraction will change the subsurface environment;
- How to characterize subsurface structures (such as fault system) effectively to reduce risks during fluid injection or extraction;
- How do microseismic monitoring and geodetic methods improve fault detection and subsurface assessment in geo-energy projects?
Brief Summary of Research
In low-carbon geo-energy projects, such as carbon capture and storage (CCS) and enhanced geothermal systems (EGS), large-volume fluid commonly injected into subsurface rocks in depth. This process may active or reactive the faults through various mechanisms. Fault zones are widely present in the Earth’s crust, which can either act as conduits or barriers to fluid movement, directly influencing the safety and long-term stability of geological storage projects. An incomplete understanding of fault systems may lead to fluid leakage or induced seismicity, pose risks to project integrity.
Recent advances in microseismic monitoring and InSAR techniques have greatly improved fault zone detection and subsurface assessments. Microseismic monitoring allows for the detection of minor seismic events that may indicate subsurface changes, while geodetic methods (e.g. InSAR) provides accurate ground deformation data. The combination of these techniques enables more effective monitoring of fluid-induced subsurface changes, for example, at In Salah CCS project, the ground uplift patterns were monitoring by InSAR. Similarly, at the Decatur site, microseismic monitoring was employed to analyze the subsurface structure and monitor changes resulting from fluid injection. These approaches enhance the understanding of fault behavior and help mitigate risks during these large-scale geo-energy projects.
Figure: Schematic illustration of geo-energy applications linked with induced seismicity. Earthquakes have reportedly been induced by tight and shale gas fracturing, conventional oil and gas development activities, deep wastewater disposal, geologic storage of natural gas or CO2, and geothermal energy exploitation and research projects. (Kivi et al.,2023)
Kivi, I. R., Boyet, A., Wu, H., Walter, L., Hanson-Hedgecock, S., Parisio, F., & Vilarrasa, V. (2023). Global physics-based database of injection-induced seismicity. Earth System Science Data, 15(7), 3163–3182. https://doi.org/10.5194/essd-15-3163-2023
Key References:
Anderson, E. M. (1905). The dynamics of faulting. Transactions of the Edinburgh Geological Society, 8, 387–402. https://api.semanticscholar.org/CorpusID:131149531
Aswathi, J., Binoj Kumar, R., Oommen, T., Bouali, E., & Sajinkumar, K.(2022). InSAR as a tool for monitoring hydropower projects: A review. Energy Geoscience, 3 (2), 160–171. https://doi.org/10.1016/j.engeos.2021.12.007
Biggs, J., & Wright, T. J. (2020). How satellite InSAR has grown from opportunistic science to routine monitoring over the last decade [Publisher: Nature Publishing Group]. Nature Communications, 11 (1),3863. https://doi.org/10.1038/s41467-020-17587-6
Caine, J. S., Evans, J. P., & Forster, C. B. (1996). Fault zone architecture and permeability structure. Geology, 24 (11), 1025–1028. https://doi.org/10.1130/0091- 13(1996)024⟨1025:FZAAPS⟩2.3.CO2
Cappa, F., & Rutqvist, J. (2011). Modeling of coupled deformation and permeability evolution during fault reactivation induced by deep underground injection of CO2. International Journal of Greenhouse Gas Control, 5 (2), 336–346. https://doi.org/10.1016/j.ijggc.2010.08.005
C´el´erier, B. (2008). Seeking anderson’s faulting in seismicity: A centennial celebration. Reviews of Geophysics, 46 (4). https://doi.org/10.1029/2007RG000240
Engelder, T. (1993). Stress regimes in the lithosphere. Princeton University Press.
Maxwell, S., Bennett, L., Jones, M., & Walsh, J. (2010). Anisotropic velocity modeling for microseismic processing: Part 1—impact of velocity model uncertainty. In Seg technical program expanded abstracts 2010 (pp. 2130–2134). Society of Exploration Geophysicists. https://doi.org/10.1190/1.3513267
Rutqvist, J., Vasco, D. W., & Myer, L. (2010). Coupled reservoir-geomechanical analysis of CO2 injection and ground deformations at In Salah, Algeria. International Journal of Greenhouse Gas Control, 4 (2), 225–230. https://doi.org/10.1016/j.ijggc.2009.10.017
Vasco, D. W., Rucci, A., Ferretti, A., Novali, F., Bissell, R. C., Ringrose, P. S., Mathieson, A. S., & Wright, I. W. (2010). Satellite-based measurements of surface deformation reveal fluid flow associated with the geological storage of carbon dioxide. Geophysical Research Letters, 37 (3). https://doi.org/10.1029/2009GL041544
Vilarrasa, V., and J. Carrera (2015), Geologic carbon storage is unlikely to trigger large earthquakes and reactivate faults through which CO2 could leak, Proc. Natl. Acad. Sci. U.S.A., 112(19), 5938–5943. https://doi.org/10.1073/pnas.1413284112
Vilarrasa, V., J. Carrera, S. Olivella, J. Rutqvist, and L. Laloui (2019). Induced seismicity in geologic carbon storage, Solid Earth 10, 871–892. https://doi.org/10.5194/se-10-871-2019
Automated analysis of ground deformation time series
Image European Union, Copernicus Sentinel-2 imagery
Doctoral Candidate: Osmari Aponte
Supervisor: Eugenio Realini
Host institution: GReD
Output type: literature review and summary research topic
Brief Summary of Research
Climate change demands urgent action, leading to innovative solutions to cut greenhouse gas emissions. The COP21 agreement in Paris (2015) set a global target to limit temperature rise from CO2 emissions to 2°C (UNFCCC, 2015). Geological carbon capture and storage is one method to achieve this goal and reduce emissions by 2050 (IEAGHG, 2020). This method, when combined with geothermal energy, offers a promising strategy (Randolph & Saar, 2011). However, the challenge lies in ensuring safety, which requires a thorough understanding and effective monitoring techniques (Zhang et al., 2022).
Recognizing the importance of monitoring underground storage and geothermal activities, space geodetic techniques have been integrated into the multidisciplinary approach to evaluate both natural and anthropogenic surface deformation. GNSS (Global Navigation Satellite System) technologies and interferometric analysis of synthetic aperture radar images (SAR) offer complementary insights into Earth's surface deformation (Gudmundsson et al., 2002).
Global navigation satellite system (GNSS) is a space geodetic technique that can determine the 3D coordinates of a permanent monitoring site with sub-centimeter accuracy (Benetatos et al., 2020). GNSS-based deformation monitoring offers significant advantages such as real-time and weather-independent monitoring, high precision, and monitoring of dynamic and long-term deformations without needing visibility between points (Shen et al., 2019).
Synthetic Aperture Radar (SAR) techniques offer extensive spatial coverage and high-resolution data (Del Soldato et al., 2021) restricted to a one-dimensional (1D) line of sight (LOS) viewing geometry (Fuhrmann & Garthwaite, 2019). Some applications of SAR technology are SAR Interferometry (InSAR) and Persistent Scatterers Interferometry (PSI), which are valuable monitoring and risk management tools (Areggi et al., 2023; Crosetto et al., 2016; Del Soldato et
al., 2018; Farolfi et al., 2019; Rosi et al., 2014).
The challenge persists in merging SAR's high-resolution imaging with GNSS's precise positioning data. While SAR provides accurate Line-Of-Sight (LOS) measurements, it cannot fully capture the magnitude and direction of surface motions (Fuhrmann & Garthwaite, 2019), and the Line-of-Sight (LOS) cannot be directly decomposed into all three coordinate components without introducing bias (Brouwer, 2021). Combining GNSS and InSAR data offers enhanced temporal coverage and improved accuracy in deformation analysis (Del Soldato et al., 2021). Despite advancements, methods for mapping 3D displacements remain a subject of ongoing research, highlighting the need for innovative approaches (Hu et al., 2014; Tondaś et al., 2023).
This research aims to develop an automated tool that integrates GNSS and InSAR data, overcoming the limitations of each method and offering complementary perspectives on ground surface deformation. By developing this tool, we aim to improve our understanding of ground deformation processes with higher spatiotemporal resolution, providing innovative insights for managing geo-energy activities.
Key References
Areggi, G., Pezzo, G., Merryman Boncori, J. P., Anderlini, L., Rossi, G., Serpelloni, E., Zuliani, D., & Bonini, L. (2023). Present-Day Surface Deformation in North-East Italy Using InSAR and GNSS Data. Remote Sensing, 15(6), Article 6. https://doi.org/10.3390/rs15061704
Benetatos, C., Codegone, G., Ferraro, C., Mantegazzi, A., Rocca, V., Tango, G., & Trillo, F. (2020). Multidisciplinary Analysis of Ground Movements: An Underground Gas Storage Case Study. Remote Sensing, 12(21), Article 21. https://doi.org/10.3390/rs12213487
Brouwer, W. (2021). An analysis of the InSAR displacement vector decomposition: InSAR fallacies and the strap-down solution. https://repository.tudelft.nl/islandora/object/uuid%3A9bea6424-c03b-4e0b-95b9-cc6871959f2d
Crosetto, M., Monserrat, O., Cuevas-González, M., Devanthéry, N., & Crippa, B. (2016). Persistent Scatterer Interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 78–89. https://doi.org/10.1016/j.isprsjprs.2015.10.011
Del Soldato, M., Confuorto, P., Bianchini, S., Sbarra, P., & Casagli, N. (2021). Review of Works Combining GNSS and InSAR in Europe. Remote Sensing, 13(9), Article 9. https://doi.org/10.3390/rs13091684
Del Soldato, M., Farolfi, G., Rosi, A., Raspini, F., & Casagli, N. (2018). Subsidence Evolution of the Firenze–Prato–Pistoia Plain (Central Italy) Combining PSI and GNSS Data. Remote Sensing, 10(7), Article 7. https://doi.org/10.3390/rs10071146
Farolfi, G., Del Soldato, M., Bianchini, S., & Casagli, N. (2019). A procedure to use GNSS data to calibrate satellite PSI data for the study of subsidence:an example from the north-western Adriatic coast (Italy). European Journal of Remote Sensing, 52(sup4), 54–63. https://doi.org/10.1080/22797254.2019.1663710
Fuhrmann, T., & Garthwaite, M. C. (2019). Resolving Three-Dimensional Surface Motion with InSAR: Constraints from Multi-Geometry Data Fusion. Remote Sensing, 11(3), Article 3. https://doi.org/10.3390/rs11030241
Gudmundsson, S., Sigmundsson, F., & Carstensen, J. M. (2002). Three-dimensional surface motion maps estimated from combined interferometric synthetic aperture radar and GPS data. Journal of Geophysical Research: Solid Earth, 107(B10), ETG 13-1-ETG 13-14. https://doi.org/10.1029/2001JB000283
Hu, J., Li, Z. W., Ding, X. L., Zhu, J. J., Zhang, L., & Sun, Q. (2014). Resolving three-dimensional surface displacements from InSAR measurements: A review. Earth-Science Reviews, 133, 1–17. https://doi.org/10.1016/j.earscirev.2014.02.005
IEAGHG. (2020). Monitoring and Modelling of CO2 Storage: The Potential for Improving the Cost- Benefit Ratio of Reducing Risk [IEAGHG Technical Report 2020–01]. https://climit.no/app/uploads/sites/4/2020/05/2020-01-Monitoring-and-Modelling-of- CO2-Storage.pdf
Randolph, J. B., & Saar, M. O. (2011). Combining geothermal energy capture with geologic carbon dioxide sequestration. Geophysical Research Letters, 38(10). https://doi.org/10.1029/2011GL047265
Rosi, A., Agostini, A., Tofani, V., & Casagli, N. (2014). A Procedure to Map Subsidence at the Regional Scale Using the Persistent Scatterer Interferometry (PSI) Technique. Remote Sensing, 6(11), Article 11. https://doi.org/10.3390/rs61110510
Shen, N., Chen, L., Liu, J., Wang, L., Tao, T., Wu, D., & Chen, R. (2019). A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring. Remote Sensing, 11(9), Article 9.
https://doi.org/10.3390/rs11091001
Tondaś, D., Ilieva, M., van Leijen, F., van der Marel, H., & Rohm, W. (2023). Kalman filter-based integration of GNSS and InSAR observations for local nonlinear strong deformations. Journal of Geodesy, 97(12), 109. https://doi.org/10.1007/s00190-023-01789-z
UNFCCC. (2015). The Paris Agreement. United Nations Framework Convention on Climate Change (UNFCC). https://unfccc.int/sites/default/files/english_paris_agreement.pdf
Zhang, T., Zhang, W., Yang, R., Gao, H., & Cao, D. (2022). Analysis of Available Conditions for InSAR Surface Deformation Monitoring in CCS Projects. Energies, 15. https://doi.org/10.3390/en15020672
Quantitative ground deformation monitoring based on advanced DInSAR techniques
Doctoral Candidate: Paula Olea
Supervisor: Oriol Montserrat
Host institution: CTTC
Output type: literature review and summary research topic
Brief Summary of Research
In recent decades, natural dynamics have been modified by anthropogenic activities. There are correlations between rising global temperatures and rising CO2 emissions into the atmosphere because of industrial activity. Addressing these concerns involves pursuing commitments to reduce emissions, with initiatives such as implementing Carbon Capture and Storage (CCS) projects serving as one of the approaches to combat these issues.
Zhang et al. (2021) summarize the environmental parameters to consider in CCS projects in vegetation changes due to its sensitivity to changes in CO2 concentrations in the soil, surface thermal anomaly due to leakage, and ground deformation. One of the most exploited techniques to identify and characterize ground deformation dynamics is Persistent Scattered Interferometry (PSI), which allows us to estimate ground deformation over time from radar satellite data.
PSI is based on satellite radar imagery. Radar is a technique that uses microwave wavelength to characterize the surface of the Earth. The sensor emits a microwave signal (beam) and records the backscattered signal (Crosetto & Solari, 2023). A radar image is composed of two values: Amplitude and Phase.
The Phase is used to calculate the difference between two different acquisitions, this difference is called Interferogram. PSI performs an integration of several Interferograms at different times and with different angles (baselines) to obtain the ground deformation (Ferretti et al., 2001).
There is well known the existence of seasonal Phase disturbances in PSI measurement, some sources for this deformation could be clay swelling/shrinkage, groundwater level, soil moisture or vegetation dynamics (Westerhoff & Steyn-Ross, 2020).
Improving the understanding of the environmental factors could improve the understanding of ground deformation over time. These variables can be integrated seamlessly into a workflow since the launch of the Copernicus Programme.
The goal of the project is to build a set of tools designed to characterize the temporal dynamics of environmental factors. Additionally, these tools will be integrated into the PSI results to enhance the quality and interpretation of the land. These tools will be created using multispectral, thermal, hyperspectral, and radar satellite imagery.
Key References
Crosetto, M., & Solari, L. (2023). Synthetic aperture radar interferometry. In Satellite Interferometry Data Interpretation and Exploitation (pp. 7–26). Elsevier. https://doi.org/10.1016/B978-0-443-13397-8.00008-X
Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8–20. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/36.898661
Westerhoff, R., & Steyn-Ross, M. (2020). Explanation of InSAR Phase Disturbances by Seasonal Characteristics of Soil and Vegetation. Remote Sensing, 12(18), 3029. https://doi.org/10.3390/rs12183029
Zhang, T., Zhang, W., Yang, R., Liu, Y., & Jafari, M. (2021). CO2 capture and storage monitoring based on remote sensing techniques: A review. Journal of Cleaner Production, 281, 124409. https://doi.org/10.1016/j.jclepro.2020.124409
Application of Multi-Sensor Images to Improve the Capabilities of PSI (Persistent Scatterer Interferometry)
Doctoral Candidate: Maria C. Ramlie
Supervisor: Michele Crossetto
Host institution: CTTC
Output type: literature review and summary research topic
Research Questions:
1. How to mitigate the common problems with PSI techniques such as vegetation?
2. How to improve the accuracy of PSI technique when it comes to ground deformation monitoring?
3. How will this technique perform in terms of geo-energies related project and other cases that involves ground deformation such as landslide?
Brief Summary of Research:
PSI (Persistent Scatterer Interferometry) is a very powerful remote sensing method that utilizes the Persistent Scatterer by exploiting multiple images captured by SAR (Synthetic Aperture Radar). The technique can detect millimeter scale of ground deformation, making it ideal for every projects or cases that requires effective and efficient monitoring. While it aims to be generally used for every case that involves ground deformation, in this project the main concentration is on geo-energy projects. Geo-energy often imply injecting (and/or extracting) fluids into (from) the subsurface and by this information we know that movement or deformation will occur. There is a phenomena called induced seismicity, that has been detected in some geo-energy projects.
However, PSI can still encounter some issues that can affect the accuracy. Major issue of this technique is the vegetation cover. Usually, there are fewer persistent scatterer that can be detected in heavily vegetated areas. This can be mitigated by using X-band sensor, considered a high resolution sensor. Another aspect that can improve the accuracy of PSI is by having a large set of data.
With this idea in mind, this research proposes a technique to combine multiple images that sourced from different sensors such as Sentinel-1, TerraSAR-X, COSMO-SKYMED, NISAR, and SAOCOM. This is not an easy feat as these sensors provide different properties, example the band and wavelength of Sentinel-1 (C-band) versus TerraSAR-X (X-band). This requires attention and priority to be able to combine the images. Successfully implementing this technique could improve ground deformation monitoring , offering more insights for a wide range of applications. This technique could also lead to a better monitoring and management ground monitoring related projects, ensuring their safety, efficiency, and awareness. Through continued research and collaboration, we can unlock the full potential of this innovative approach, driving progress towards a more sustainable and resilient future.
Key References
Bamler, R., & Hartl, P. (1997). Synthetic aperture radar interferometry. Inverse Problems, Volume 14, Number 4. https://doi.org/10.1088/0266-5611/14/4/001
Barbier, E. (2002). Geothermal energy technology and current status: An overview. Renewable and Sustainable Energy Reviews, 6(1–2). https://doi.org/10.1016/S1364-0321(02)00002-3
Boot-Handford, M. E., Abanades, J. C., Anthony, E. J., Blunt, M. J., Brandani, S., Mac Dowell, N., Fernández, J. R., Ferrari, M.-C., Gross, R., Hallett, J. P., Haszeldine, R. S., Heptonstall, P., Lyngfelt, A., Makuch, Z., Mangano, E., Porter, R. T. J., Pourkashanian, M., Rochelle, G. T., Shah, N., ... Fennell, P. S. (2014). Carbon capture and storage update. Energy Environ. Sci., 7(1), 130–189. https://doi.org/10.1039/C3EE42350F
Crosetto, M., Monserrat, O., Cuevas-González, M., Devanthéry, N., & Crippa, B. (2016). Persistent Scatterer Interferometry: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 78–89. https://doi.org/10.1016/j.isprsjprs.2015.10.011
Crosetto, M., Monserrat, O., Jungner, A., & Crippa, B. (n.d.). PERSISTENT SCATTERER INTERFEROMETRY: POTENTIAL AND LIMITS.
Devanthéry, N., Crosetto, M., Monserrat, O., Cuevas-González, M., & Crippa, B. (2014). An Approach to Persistent Scatterer Interferometry. Remote Sensing, 6(7), 6662–6679. https://doi.org/10.3390/rs6076662
Dong, J., Zhuang, D., Huang, Y., & Fu, J. (2009). Advances in Multi-Sensor Data Fusion: Algorithms and Applications. Sensors, 9(10), 7771–7784. https://doi.org/10.3390/s91007771
Eiken, O., Ringrose, P., Hermanrud, C., Nazarian, B., Torp, T. A., & Høier, L. (2011). Lessons learned from 14 years of CCS operations: Sleipner, In Salah and Snøhvit. Energy Procedia, 4, 5541–5548. https://doi.org/10.1016/j.egypro.2011.02.541
Elsworth, D., Spiers, C. J., & Niemeijer, A. R. (2016). Understanding induced seismicity. Science, 354(6318),
1380–1381. https://doi.org/10.1126/science.aal2584
Ferretti, A., Fumagalli, A., Novali, F., Prati, C., Rocca, F., & Rucci, A. (2011). A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 49(9).
Ferretti, A., Monti-Guarnieri, A., Prati, C., Rocca, F., & Massonet, D. (2007). InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation. ESA Publications.
Ferretti, A., Prati, C., & Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1), 8–20. https://doi.org/10.1109/36.898661
Fraser-Harris, A., McDermott, C. I., Receveur, M., Mouli-Castillo, J., Todd, F., Cartwright-Taylor, A., Gunning, A., & Parsons, M. (2022). The Geobattery Concept: A Geothermal Circular Heat Network for the Sustainable Development of Near Surface Low Enthalpy Geothermal Energy to Decarbonise Heating. Earth Science, Systems and Society, 2, 10047. https://doi.org/10.3389/esss.2022.10047
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Loyola, D. G., & Coldewey-Egbers, M. (2012). Multi-sensor data merging with stacked neural networks for the creation of satellite long-term climate data records. EURASIP Journal on Advances in Signal Processing, 2012(1), 91. https://doi.org/10.1186/1687-6180-2012-91
Masciulli, C., Berardo, G., Gaeta, M., Stefanini, C. A., Giraldo Manrique, S., Belcecchi, N., Bozzano, F., Scarascia Mugnozza, G., & Mazzanti, P. (2024). A Novel Data Fusion Method for Integrating Multi-Band/Multi-Sensor Persistent Scatterers. https://doi.org/10.2139/ssrn.4762414
Moreira, A., Prats-Iraola, P., Younis, M., Krieger, G., Hajnsek, I., & Papathanassiou, K. P. (2013). A tutorial on synthetic aperture radar. IEEE Geoscience and Remote Sensing Magazine, 1(1), 6–43. https://doi.org/10.1109/MGRS.2013.2248301
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Experimental Study on Caprock’s Integrity to CO2 Injection
Doctoral Candidate: Ümit Koç
Supervisors: Laura Blanco-Martín, Dominique Bruel and Jérôme Corvisier
Host institution: Mines Paris - PSL
Output type: literature review and summary research topic
Research Questions:
1. How do CO2 injections interact with clayey caprock formations?
2. How to develop a coupled thermal, hydraulic, geomechanical and geochemical (THMC) model to analyze CO2 interactions in caprock integrity based on experimental investigation?
3. How this experimental investigation could be further improved, transferred and implemented for different conditions, sites and formations?
4. What monitoring strategies and workflows can be established for observing and comprehending natural phenomena and anthropogenic effects at CO2 sequestration sites with prolonged injection practices?
Brief Summary of Research:
The experimental investigation into the coupled thermal-hydraulic-mechanical-chemical (THMC) responses of caprock to CO2 injection is crucial for assessing the long-term viability of CO2 storage in geological formations. When CO2 is injected into subsurface reservoirs, it alters temperature, pressure, chemical composition, and mechanical stress in the surrounding rock formations, particularly caprock, which acts as the primary seal preventing CO2 escape. Understanding these coupled responses is essential to predicting caprock integrity and ensuring safe and effective carbon capture, utilization and storage (CCUS) which is pivotal for mitigating climate change.
In pilot test sites, laboratory experiments and field studies are commonly used to assess how caprock behaves under these conditions. Various approaches, including numerical modeling and monitoring of in-situ conditions, are integrated to observe changes in permeability, mineral composition, and fracture propagation due to CO2 injection. These investigations help to simulate long-term storage scenarios, addressing key challenges such as CO2 leakage and caprock failure due to chemical alterations or mechanical stress.
The Opalinus Clay formation, primarily found in Switzerland, is a significant focus of these studies,since it is considered as a potential caprock for CO2 storage due to its low permeability and favorable mineralogical composition. There have been many studies conducted to understand behavior of the formation under various conditions, including CO2 injection. The Mont Terri Underground Rock Laboratory (URL) plays a crucial role in these investigations, providing a controlled environment where scientists can simulate and study the effects of CO2 injection on the Opalinus Clay.
The state of the art in this study involves a multidisciplinary approach that combines experimental, modeling, and field studies to assess the suitability of caprocks for CO2 storage. This approach addresses critical questions, such as how to develop a coupled thermal, hydraulic, geomechanical, and geochemical (THMC) model to analyze CO2 interactions and their impact on caprock integrity based on experimental investigations. Additionally, it considers how these experimental findings can be further improved, transferred, and applied to diverse geological conditions, sites, and formations. The Mont Terri URL is a key site for these investigations, offering valuable data on the THMC behavior of the Opalinus Clay. For those seeking to gain a deeper understanding of the background to caprock
integrity in CO2 storage, the studies listed below could provide a valuable key references for further information.
Key References
Bossart, P. et al. (2017). “Mont Terri rock laboratory, 20 years of research: introduction, site characteristics and overview of experiments”. Swiss J Geosci 110.1, pp. 3–22. issn: 1661-8726, 1661-8734. doi: 10.1007 s00015-016-0236-1.
Busch, A., Amann, A., Bertier, P., Waschbusch, M., and Krooss, B. M. (2010). “The Significance of Caprock Sealing Integrity for CO2 Storage”. In: All Days. SPE International Conference on CO2 Capture, Storage, and Utilization. New Orleans, Louisiana, USA: SPE, SPE–139588–MS. doi: 10.2118/139588-MS.
European Commission. Directorate General for Climate Action. (2011). Implementation of directive 2009/31/EC on the geological storage of carbon dioxide: guidance document 2, characterisation of the storage complex, CO2 stream composition, monitoring and corrective measures. LU: Publications Office. Available at: https://data.europa.eu/doi/10.2834/98293 (Accessed at: 04/11/2024).
IEA-GHG (2011). Caprock Systems for CO2 Geological Storage. Technical Report 2011/01, May, 2011. International Energy Agency Greenhouse Gas Research and Development Programme (IEA-GHG).
Makhnenko, R. Y. and Vilarrasa, V. (2017). “Clay-rich rocks as barriers for geologic CO2 storage”. In: 51st US Rock Mechanics / Geomechanics Symposium. San Francisco, California, USA: American Rock Mechanics Association (ARMA), ARMA 2017–199.
Metz, B., Davidson, O., De Coninck, H., Loos, M., and Meyer, L. (2005). IPCC special report on carbon dioxide capture and storage. Prepared by Working Group III of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA: Prepared by Working Group III of the Intergovernmental Panel on Climate Change, p. 442.
Rebscher, D., Vilarrasa, V., Makhnenko, R., Nussbaum, C., Kipfer, C., and Wersin, P. (2020). “CO2LPIE Project – Combining in-situ, laboratory, and modelling work to investigate periodic CO2 injection into an argillaceous claystone”. Available at: http://hdl.handle.net/10261/224405.
Shukla, R., Ranjith, P. G., Choi, S. K., and Haque, A. (2011). “Study of Caprock Integrity in Geosequestration of Carbon Dioxide”. Int. J. Geomech. 11.4, pp. 294–301. issn: 1532-3641, 1943-5622. doi: 10.1061/(ASCE)GM.1943-5622.0000015.
Song, J. and Zhang, D. (2013). “Comprehensive Review of Caprock-Sealing Mechanisms for Geologic Carbon Sequestration”. Environ. Sci. Technol. 47.1, pp. 9–22. issn: 0013-936X, 1520-5851. doi: 10.1021/es301610p.
Vilarrasa, V. and Makhnenko, R. Y. (2017). “Caprock Integrity and Induced Seismicity from Laboratory and Numerical Experiments”. Energy Procedia 125, pp. 494–503. issn: 18766102. doi: 10.1016/j.egypro.2017.08.172.
Vilarrasa, V and Rebscher, D and Makhnenko, R Y (2019). “Modeling a Long-Term CO2 Injection Experiment at the Underground Rock Laboratory of Mont Terri”. In: 11st Workshop of CODE- BRIGHT Users. UPC, Barcelona, Spain: Spanish National Research Council (CSIC). Available at: https://shorturl.at/Amt06.
Ziegler, M., Jaeggi, D., Grignaschi, A., Kipfer, R., and Rinaldi, A. P. (2024). “CO2LPIE: CO2 Long-term Periodic Injection Experiment (CL)”. In: 1st Caprock Integrity & Gas Storage Symposium. St. Ursanne, Switzerland: Swiss Federal
Use of Invasion Percolation Methods to model migrating CO2 in complex rock-fluid systems
Doctoral Candidate: Mateja Macut
Supervisor: Philip Ringrose
Co-supervisor: Carl Fredrik Berg
Host institution: NTNU
Output type: literature review and summary research topic
CCS is a subsurface geological carbon capture technology, which is being recognised as a necessary element of reducing the effects of CO2 emissions worldwide (Osmond et al., 2022; Ringrose, 2020). It is a powerful tool needed to help meet the meet the long-term global temperature goals (e.g. 1.5°C target) agreed in the Paris Agreement and to reach net zero emissions from the energy sector by 2050 (Holden et al., 2022). CO2 storage requires a reservoir with suitable capacity (enough room for the injected CO2), porosity (percentage of empty spaces in a material) and permeability (capacity of a porous material for transmitting a fluid), as well as a caprock (the part above the reservoir with very low porosity and permeability), which protects the storage site from potential leaking of CO2. The injection must be done below 800 m of depth, where CO2 safely remains in a supercritical phase (Ringrose, 2020). This thesis will be focusing on two CCS sites in Norway: Sleipner and Smeaheia.
The main research question of this topic is how does migrating CO2 interact with complex rock-fluid systems. The objective is a better understanding of gravity segregation of CO2, hydrocarbon (HC) and brine systems by enhancing modelling capabilities of mixing HC and CO2 phase in highly heterogeneous brine-saturated rock (e.g., offshore sandstone with thin layers of shale) coupling Invasion Percolation approach with geomechanics. Similar research topics have already been obtained in the past by comparing various methods, studying the Sleipner case, as one of the pioneering commercial CO2 storage sites.
For example, Cavanagh et al. (2015) studied a combination of basin modelling and a simple Darcy-based reservoir simulation of the Sleipner CO2 plume. Williams et al. (2018) did a comparison of three Darcy’s Law based simulators with different numerical solver implementations. Nazarian and Furre (2022) created a model that describes the distinctive pattern of movement of CO2 plume in Sleipner. One of the latest research projects is Callioli Santi et al. (2022), which is using invasion percolation concepts for modelling CO2 migration pathways in Sleipner.
In this project the Invasion Percolation solutions will be tested against multiphase-flow models of the same system in order to improve the Invasion Percolation formulation for CO2 migration problems. The Invasion Percolation method is a well-known solution to a broad variety of physical problems (Wilkinson and Willemsen, 1983). It separates two phases of the fluid: (1) wetting phase (e.g. water), which fills every narrow part (pore throat), as well as the corners of the pore spaces, while flowing through the porous media, and (2) non-wetting phase (e.g. oil), which displaces the water and fills the pores (pore body) regarding their size, choosing the widest throats, which must be connected to the inlet (Dong and Blunt, 2009; Yu et al., 2018). This approach could lead to much improved methods for handling CO2 migration problems within saline-aquifer CO2 storage systems.
Literature
Cavanagh, A.J., Haszeldine, R.S., Nazarian, B., 2015. The Sleipner CO2 storage site: using a basin model to understand reservoir simulations of plume dynamics. First Break 33. https://doi.org/10.3997/1365-2397.33.6.81551
Callioli Santi, A., Ringrose, P., Eidsvik, J., Haugdahl, T.A., 2022. Assessing CO2 Storage Containment Risks Using an Invasion Percolation Markov Chain Concept. https://doi.org/10.2139/ssrn.4282992
Dong, H., Blunt, M.J., 2009. Pore-network extraction from micro-computerized-tomography images. Phys. Rev. E 80, 036307. https://doi.org/10.1103/PhysRevE.80.036307
Furre, A.-K., Eiken, O., Alnes, H., Vevatne, J.N., Kiær, A.F., 2017. 20 Years of Monitoring CO2-injection at Sleipner. Energy Procedia, 13th International Conference on Greenhouse Gas Control Technologies, GHGT-13, 14-18 November 2016, Lausanne, Switzerland 114, 3916–3926. https://doi.org/10.1016/j.egypro.2017.03.1523
Holden, N., Osmond, J.L., Mulrooney, M.J., Braathen, A., Skurtveit, E., Sundal, A., 2022. Structural characterization and across-fault seal assessment of the Aurora CO2 storage site, northern North Sea. Petroleum Geoscience 28, petgeo2022-036. https://doi.org/10.1144/petgeo2022-036
Nazarian, B., Furre, A.K., 2022. Simulation Study of Sleipner Plume on Entire Utsira Using A Multi-Physics Modelling Approach. https://doi.org/10.2139/ssrn.4274191
Osmond, J.L., Mulrooney, M.J., Holden, N., Skurtveit, E., Faleide, J.I., Braathen, A., 2022. Structural traps and seals for expanding CO2 storage in the northern Horda platform, North Sea. AAPG Bulletin 106, 1711–1752. https://doi.org/10.1306/03222221110
Ringrose, P., 2020. How to Store CO2 Underground: Insights from early-mover CCS Projects, SpringerBriefs in Earth Sciences. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-030-33113-9
Wilkinson, D., Willemsen, J.F., 1983. Invasion percolation: a new form of percolation theory. J. Phys. A: Math. Gen. 16, 3365. https://doi.org/10.1088/0305-4470/16/14/028
Williams, G.A., Chadwick, R.A., Vosper, H., 2018. Some thoughts on Darcy-type flow simulation for modelling underground CO2 storage, based on the Sleipner CO2 storage operation. International Journal of Greenhouse Gas Control 68, 164–175. https://doi.org/10.1016/j.ijggc.2017.11.010
Yu, C., Tran, H., Sakhaee-Pour, A., 2018. Pore Size of Shale Based on Acyclic Pore Model. Transp Porous Med 124, 345–368. https://doi.org/10.1007/s11242-018-1068-4
Next generation cost-effective monitoring for offshore CO2 storage
Author: Philip Ringrose, NTNU
Event: CO2GeoNet Open Forum, 21-22 May 2024, San Servolo Island, Venice UPSCALING TO GIGATONNES EVERY YEAR - Optimising CCS to meet climate targets
Output type: conference
Description: Operators can point to a long and successful track record of monitoring CO2 stores but these technical achievements do not always ‘connect’ with public concerns about safety. How can we monitor in a way that:- Builds confidence in CCS- Assures stakeholders with concerns about CO2 storage safety