A real-time autonomous adjusting process for fluid-fluid displacement in CO2 geological sequestration

Abstract

To achieve net-zero carbon emission, securely and permanently sequestrating CO2 into deep underground is internationally assured as a robust solution, although a few technical challenges on complex in-situ storage process are yet to be overcome. Despite researchers are increasingly familiar with laboratory-scale CO2-brine displacement and how to characterize and improve the process, field implementation is not that simple and of great challenge. In this article, an opportunity on an approach that utilizes fluid-fluid displacement fundamentals is discussed to predict CO2 sequestration using artificial intelligence. A concept of machine learning is introduced, where computer programs can learn and improve automatically via previous experiences. With machine learning model, fluid displacement behaviors that are spontaneously monitored are emphasized to predict the displacement result, which is readily adjusted if needed while training the model from real-time CO2 injection response. Such an approach is a real-time autonomous adjusting process, consisting of three main stages: Selection of first appraisal fluid for trial injection, real-time machine learning from in-situ injection response, and fluid adjustment if needed or continuation on the same injection until achieving a maximum CO2 storage. This approach could play a vital role in the carbon capture and storage industry to develop CO2 storage effectively with adequate resources, and yet has a potential to substitute a conventional design or fluid screening approach for subsurface fluid injection, including underground hydrogen storage and hydrocarbon recovery.

Document Type: Perspective

Cited as: Tangparitkul, S. M., Chantapakul, W., Promsuk, N. A real-time autonomous adjusting process for fluid-fluid displacement in CO2 geological sequestration. Advances in Geo-Energy Research, 2023, 7(2): 71-74. https://doi.org/10.46690/ager.2023.02.01

References

Ajayi, T., Gomes, J. S., Bera, A. A review of CO2 storage in geological formations emphasizing modeling, monitoring and capacity estimation approaches. Petroleum Science, 2019, 16(5): 1028-1063.

Alqahtani, N., Alzubaidi, F., Armstrong, R. T., et al. Machine learning for predicting properties of porous media from 2D X-ray images. Journal of Petroleum Science and Engineering, 2020, 184: 106514.

Anifowose, F., Abdulraheem, A., Al-Shuhail, A. A parametric study of machine learning techniques in petroleum reservoir permeability prediction by integrating seismic attributes and wireline data. Journal of Petroleum Science and Engineering, 2019, 176: 762-774.

Bachu, S. Review of CO2 storage efficiency in deep saline aquifers. International Journal of Greenhouse Gas Control, 2015, 40: 188-202.

Bartels, W. B., Mahani, H., Berg, S., et al. Literature review of low salinity waterflooding from a length and time scale perspective. Fuel, 2019, 236: 338-353.

Celia, M. A., Bachu, S., Nordbotten, J. M., et al. Status of CO2 storage in deep saline aquifers with emphasis on modeling pproaches and practical simulations. Water Resources Research, 2015, 51(9): 6846-6892.

Esmaili, S., Mohaghegh, S. D. Full field reservoir modeling of shale assets using advanced data-driven analytics. Geoscience Frontiers, 2016, 7(1): 11-20.

Jin, M., Ribeiro, A., Mackay, E., et al. Geochemical modelling of formation damage risk during CO2 injection in saline aquifers. Journal of Natural Gas Science and Engineering, 2016, 35: 703-719.

Jun, Y.-S., Zhang, L., Min, Y., et al. Nanoscale chemical processes affecting storage capacities and seals during geologic CO2 sequestration. Accounts of Chemical Research, 2017, 50(7): 1521-1529.

Liang, Y., Tsuji, S., Jia, J., et al. Modeling CO2-water-mineral wettability and mineralization for carbon geosequestration. Accounts of Chemical Research, 2017, 50(7): 1530-1540.

Mohamed, I. M., Nasr-El-Din, H. A. Formation damage due to CO2 sequestration in deep saline carbonate aquifers. Paper SPE 151142 Presented at SPE International Symposium and Exhibition on Formation Damage Control, Lafayette, Louisiana, 15-17 February, 2012.

Noiriel, C., Daval, D. Pore-scale geochemical reactivity associated with CO2 storage: New frontiers at the fluid–solid interface. Accounts of Chemical Research, 2017, 50(4): 759-768.

Núñez-López, V., Gil-Egui, R., Hosseini, S. A. Environmental and operational performance of CO2-EOR as a CCUS technology: A cranfield example with dynamic LCA considerations. Energies, 2019, 12(3): 448.

Rajabi, M. M., Chen, M. Simulation-optimization with machine learning for geothermal reservoir recovery: Current status and future prospects. Advances in Geo-Energy Research, 2022, 6(6): 451-453.

Reynolds, C. A., Blunt, M. J., Krevor, S. Multiphase flow characteristics of heterogeneous rocks from CO2 storage reservoirs in the united kingdom. Water Resources Research, 2018, 54(2): 729-745.

Ringrose, P. How to Store CO2 Underground: Insights from Early-Mover CCS Projects. Cham, Switzerland, Springer, 2020.

Thanasaksukthawee, V., Santha, N., Saenton, S., et al. Relative CO2 column height for CO2 geological storage: A nonnegligible contribution from reservoir rock characteristics. Energy & Fuels, 2022, 36(7): 3727-3736.

Zhang, L., Chen, L., Hu, R., et al. Subsurface multiphase reactive flow in geologic CO2 storage: Key impact factors and characterization approaches. Advances in Geo-Energy Research, 2022a, 6(3): 179-180.

Zhang, Y., Jackson, C., Krevor, S. An estimate of the amount of geological CO2 storage over the period of 1996-2020. Environmental Science & Technology Letters, 2022b, 9(8): 693-698.

Zhang, Y., Jackson, C., Zahasky, C., et al. European carbon storage resource requirements of climate change mitigation targets. International Journal of Greenhouse Gas Control, 2022c, 114: 103568.