It is common knowledge that CO2 emissions are on rise globally and there is an emergent need to curb carbon emission to stop the climate change. In this regardtwo important themes have gathered a lot of att enti on. One is related to CO2 capture and storage in subsurface reservoirs and the other one is hydrogenproducti on and storage. Since the recent COP26 summit on climate acti on there has been an increased interest in research and demonstrati on acti viti es related tocarbon capture sequestrati on (CCS) and hydrogen producti on and storage in the Balti c Sea Region countries, which are promoti ng such acti viti es and are highlyinvolved in CO2 and Hydrogen related research acti viti es. In Lithuania too Hydrogen platf orm has been launched and CO2 injecti on pilots have also beenconducted. Focus on dedicated acti viti es to understand long term fate of geological storage of CO2 and Hydrogen has been somewhat limited in Lithuania. It istherefore important to advance these areas of research in Lithuania, which is the primary aim of this project. This project aims at improving our understanding of the pore-scale processes associated with the storage of CO2 and Hydrogen into the subsurface to assess thelong-term impact CO2 and Hydrogen retenti on in subsurface reservoir rocks in Lithuanian. We plan to develop a new method with the help of arti fi cial intelligenceand machine learning algorithms, which will help to predict the dynamic properti es of the subsurface reservoir from the 3-D digital volume images of thesubsurface core sample obtained from Micro X-ray Computed Tomography scan. The new method will help in enhanced understanding of the distributi on of porespace and their contributi on towards various aspects of long term storage like fl uid fl ow, geochemical reacti ons and geo-mechanical behaviour of the rock. Thisshall further aid in identi fi cati on of reservoir(s) wherein sequestrati on possibiliti es can be reliably explored.
In this research we will also build 3-D mechanisti c models using the quanti fi ed porosity and permeability models from AI and ML algorithms. The models will helpto evaluate the impact of long term fate of CO2 and Hydrogen storage with regards to fl uid migrati on, geo-chemical, geo-mechanical eff ects and potenti al of anyleakage through faults/fracture corridors.
Project funding:
Research Council of Lithuania (RCL), Projects of Postdoctoral fellowships funded by the state budget of the Republic of Lithuania
Project results:
Postdoctoral project started in Nov 2022. There is currently a paper been submitted for Journal review paper is titled “Exploring the potential of Carbon Capture, Utilization, and Storage in Baltic sea region Countries: A Review of CCUS Patents from 2000-2022”
Period of project implementation: 2022-11-03 - 2024-11-22
Project coordinator: Kaunas University of Technology
