Online identification of evolved Takagi Sugeno fuzzy model for CO2 sequestration process

In recent years, carbon capture and storage (CCS) has been recognized as a promising technology to achieve a considerable reduction of greenhouse gas emissions from large local industries. Among different methods of CCS, carbon dioxide (CO2) sequestration in underground saline aquifers has gained much attention due to its long-term storage and low cost benefits. This type of sequestration, however, poses over-pressurization as a potential risk. This paper aims at effective monitoring of critical parameters which directly impact the CO2 sequestration performance due to over-pressurization and cap rock failure. A synthetic reservoir model is simulated in reservoir simulator (ECLIPSE-100) environment and an online fuzzy model is identified using an evolving Takagi Sugeno (eTS) algorithm. The approach recursively develops an evolving fuzzy rule-base model structure with linear rule antecedent parts using Recursive Least-Squares (RLS) parameter estimation to track reservoir dynamic changes during the CO2 sequestration. Suitability of the presented adaptive identification approach in modeling CO2 sequestration dynamic performance in an underground saline aquifer is verified via various test studies.