Published: 13 October 2023

Exploring CO2 storage potential in Lithuanian deep saline aquifers using digital rock volumes: a machine learning guided approach

Shruti Malik1
Pijus Makauskas2
Ravi Sharma3
Mayur Pal4
1, 2, 4Kaunas University of Technology, Department of Mathematical Modelling, Kaunas, Lithuania
3Department of Earth Sciences, Indian Institute of Technology, IIT Roorkee, India
Corresponding Author:
Mayur Pal
Views 41
Reads 19
Downloads 71

Abstract

The increasing significance of carbon capture, utilization and storage (CCUS) as a climate mitigation strategy has underscored the importance of accurately evaluating subsurface reservoirs for CO2 sequestration [1]. In this context, digital rock volumes, obtained through advanced imaging techniques such as micro-Xray computed tomography (MXCT), offer intricate insights into the porous and permeable structures of geological formations [2]. This study presents a comprehensive methodology for assessing CO2 storage viability within Lithuanian deep saline aquifers, namely Syderiai and Vaskai, by utilizing petrophysical properties estimated from digital rock volumes [3, 4]. These petrophysical properties were derived from core samples collected from these formations. Utilizing machine learning algorithms, porosity was estimated while the Lattice Boltzmann method (LBM) was applied to determine permeability [5]. The methodology employed for estimating these petrophysical parameters was initially validated using samples from formations analogous to Lithuanian formations. Subsequently, it was applied to rock samples specifically obtained from Lithuanian formations. The estimated petrophysical properties were compared with peer-reviewed data from published literature. When fluids such as CO2 or H2 are injected into sub-surface reservoirs, they can alter pore and grain characteristics. Therefore, it is crucial to extract representative element volumes (REVs) from segmented volumes to study the impact of fluids on porosity and their distribution [6]. These mini models, representing small portions of the larger formation, assist in predicting fluid flow within the formation, which is vital for assessing the efficiency and safety of carbon capture and storage (CCS) operations. Subsequently, numerical modelling was conducted using the petrophysical parameters as inputs to assess the storage capacity of the Lithuanian formations using tNavigator software [7]. This research contributes to an enhanced understanding of pore space distribution and its role in various aspects of long-term CO2 storage. It also demonstrates the potential of integrating advanced imaging techniques, machine learning, and numerical modeling for accurate assessment and effective management of subsurface CO2 storage. This study shall aid in enhanced understanding of pore space distribution and their contribution towards various aspects of long-term storage. The results can be extended to study the geochemical reactions and geo-mechanical behaviour of the rocks. Such studies shall further facilitate identification of reservoir(s) wherein sequestration potential can be reliably explored.

References

  • A. Shogenova, S. Šliaupa, K. Shogenov, R. Vaher, and R. Šliaupienė, “Geological Storage of CO2 – Prospects in the Baltic States,” in 69th EAGE Conference and Exhibitioon, Incorporating SPE EUROPEC, 2007.
  • N. Alqahtani, F. Alzubaidi, R. T. Armstrong, P. Swietojanski, and P. Mostaghimi, “Machine learning for predicting properties of porous media from 2d X-ray images,” Journal of Petroleum Science and Engineering, Vol. 184, p. 106514, Jan. 2020, https://doi.org/10.1016/j.petrol.2019.106514
  • R. Šliaupienė and S. Šliaupa, “Prospects of CO2 geological storage in deep saline aquifers of Lithuania and adjacent territories,” Geologija, Vol. 53, No. 3, pp. 121–133, 2011.
  • S. Šliaupa and R. Šliaupiene, “Prospects of geological storage of CO2 in Lithuania,” in Baltic Carbon Forum, Oct. 2021.
  • S. Malik, P. Makauskas, V. Karaliute, R. Sharma, and M. Pal, “Assessing Long-term fate of geological CO2 storage in Lithuania: A machine learning approach for pore-scale processes and reservoir characterization,” in 12th Trondheim Conference on CO2 Capture, Transport and Storage, 2023.
  • P. Mostaghimi, M. J. Blunt, and B. Bijeljic, “Computations of absolute permeability on micro-CT images,” Mathematical Geosciences, Vol. 45, No. 1, pp. 103–125, Jan. 2013, https://doi.org/10.1007/s11004-012-9431-4
  • M. Pal, S. Malik, V. Karaliūtė, P. Makauskas, and R. Sharma, “Assessing the feasibility of carbon capture and storage potential in lithuanian geological formations: a simulation-based assessment,” in 84th EAGE Annual Conference and Exhibition, Vol. 2023, No. 1, pp. 1–5, 2023, https://doi.org/10.3997/2214-4609.202310502

Cited by

Assessing Geothermal Energy Production Potential of Cambrian Geothermal Complexes in Lithuania
Pijus Makauskas | Ieva Kaminskaite-Baranauskiene | Abdul Rashid Abdul Nabi Memon | Mayur Pal
(2024)
Unlocking Geothermal Energy: A Thorough Literature Review of Lithuanian Geothermal Complexes and Their Production Potential
Abdul Rashid Memon | Pijus Makauskas | Ieva Kaminskaite-Baranauskiene | Mayur Pal
(2024)

About this article

Received
06 September 2023
Accepted
11 September 2023
Published
13 October 2023
Keywords
carbon capture
utilization and storage (CCUS)
saline aquifers
storage potential
digital rock volumes
machine learning
lattice Boltzmann method
numerical modeling
Acknowledgements

The authors would like to acknowledge the Lithuanian Research Council Funding for postdoctoral research fund proposal registration No. P-PD-22-022-PATIKSLINTA and the support from UAB Minijos Nafta for sharing data for reservoir modeling and simulation.