Pressure transient analysis for detecting leakages along active and abandoned wells

Leakage out of the target formation is a major risk associated with subsurface wellbores. This study explores feasibility of leakage detection with pressure transient analysis (PTA) in multiple potential leakage scenarios, including induced fractures and casing failures in active and legacy wells, which may become conductive from injection‑induced pressure buildup. A mechanistic well-reservoir model based on a representative offshore Norwegian sandstone reservoir was used in the study. The fluid flow simulation study for a single-phase, isothermal, low-compressible fluid (water) has two parts. The first addresses the mechanisms of induced fracturing and fracture propagation through caprocks, creating out-of-zone leakage risks. Then, it proposes an approach to model fracture propagation into cap-rocks and explores feasibility of early detection of the fracture leakage into cap-rocks with PTA of step-rate tests. The second part examines casing-cement-rock integrity failure mechanisms with focus on behind-casing leakage or crossflow detection with PTA. A dedicated modeling approach for behind-casing leakage resulted in revealing pressure derivative signatures associated with the leakage from the flow simulations, confirming feasibility of the leakage detection from PTA of shut-ins responses of active and abandoned wells. The monitoring approach considered in the study relies on real-time pressure data from permanent downhole gauges (PDGs) installed in active wells or wireless PDGs in abandoned wells, interpreted with the PTA methods evaluated. Results from the simulated cases have demonstrated the feasibility of the PTA methods to detect leakage with a possibility to approximately assess leakage rates in some cases, with the assessment accuracy decreasing with declining leakage rate. The feasibility study results are valid for the cases considered and the assumptions taken, and further testing and validation of the PTA methods using field data is a necessary next step in maturation of the interpretation methodology presented.