Enhancing Fault Location Precision in CCS Storage Studies Using AIML: A Progressive Adaptive Model Training Approach

Summary This study explores the application of Artificial Intelligence and Machine Learning (AIML) in interpreting overburden faults within Carbon Capture and Storage (CCS) studies. By employing a progressive adaptive model training approach, the research aims to achieve efficient and precise fault prediction. The results demonstrate that the AIML system significantly enhances fault location precision, identifies previously undetected faults, and provides valuable insights into potential hydrocarbon migration pathways. The study underscores the critical importance of detailed overburden structural and fault seal analysis for ensuring safe CO2 containment. Additionally, it contributes novel information on risk zones for CO2 storage in depleted oil and gas reservoirs, highlighting the effectiveness of AIML technology in improving fault location accuracy.