Development of a Permeability Reduction Model Using Deep Learning for CO2 Hydrate Storage

Global warming is one of the biggest environmental concerns and the necessity to reduce the emission of greenhouse gases is very important. Carbon capture and storage (CCS) emerges as an important and promising process for this task whereas carbon dioxide is captured from large emission sources, and it is later transported and stored under aquifers onshore or seabed regions offshore. The carbon dioxide can be stored under layers which have very low permeability called cap-rock. A risk of leakage exists due to possible fractures in the cap-rock, although such a risk is very small. There is a leak-trapping mechanism that utilizes the natural formation of carbon dioxide (CO₂) hydrate, which can create a new low-permeability layer. This concept can also be extended to be used by injecting CO₂ directly in the hydrate stability zone, i.e., regions with proper conditions of pressure and temperature for its formation, allowing for the formation of a low-permeability layer of gas hydrate.