Pressure transient analysis for detecting leakages along active and abandoned wells

Leakage out of the target formation is a major risk associated with subsurface injection activities, including those in hydrocarbon reservoirs, geothermal systems, and geological CO₂ storage. While previous studies often focused on individual leakage features in isolation, this feasibility study explores a holistic monitoring framework that simultaneously considers multiple potential leakage pathways, including induced fractures and casing of both active and legacy wells. These features may become conductive due to injection-induced pressure buildup and fluid migration within the reservoir. This simulation study is structured in two main parts. The first addresses the mechanisms of induced fracturing and fracture propagation through caprocks, creating out-of-zone leakage risks. Then, it presents a modeling approach and a Pressure Transient Analysis (PTA) method for early detection of leakage into shallow formations. The second part examines casing-cement-rock integrity failure mechanisms that can result in behind-casing leakage or crossflow. This is followed by a dedicated modeling approach and a PTA-based monitoring method for identifying leakage behind the casing. The proposed monitoring framework utilizes real-time pressure data from Permanent Downhole Gauges (PDGs) installed in active wells or wireless PDGs in abandoned wells, combined with PTA approaches for the data interpretation. A mechanistic well-reservoir model assembled based on a representative geological setup of a sandstone formation offshore Norway was used to test the methodology. The results have demonstrated the capability of the PTA-based methods to detect leakage and estimate leakage rates, with high accuracy for high-rate events and decreasing precision for smaller rates. The simulation study has confirmed the feasibility of leakage detection using PTA-based methods for the leakage scenarios described above. Further testing and validation of the framework using field data is a necessary next step in maturation of the methodology developed.