Research on passive monitoring mechanisms and optimization for oil-water interface in salt cavity reservoirs based on geothermal-driven thermal perturbations

To address the issues of high energy consumption, difficulty in continuous monitoring, and system complexity associated with active heating methods (e.g., cable heating) for oil-water interface monitoring during salt cavern leaching, this study proposes a novel passive monitoring method based on geothermal-driven thermal perturbation. This method eliminates the need for an active heat source, utilizing the stable underground geothermal field, introducing natural thermal perturbation by injecting low-temperature fluid, and achieving passive interface identification by leveraging the differences in thermophysical properties between oil and water. By establishing a multi-physics numerical model coupled with the geothermal field, the study finds that under baseline conditions (water injection rate: 100 m3/h, temperature: 308.15 K, tubing shoe distance H: 14.7 m), a significant temperature step exceeding 2 K can be generated at the oil-water interface. Furthermore, the slopes of the temperature-depth curves for the oil and water phases show distinct differences (approximately −0.42 K/m for oil and −0.19 K/m for water), with their ratio reaching 2.2. The Distributed Temperature Sensing (DTS) system can effectively capture this dual characteristic. Sensitivity analysis indicates that the characteristic interface temperature difference is positively correlated with the injection flow rate and the tubing shoe distance H, and negatively correlated with the injection temperature. Additionally, the study proposes an adaptive strategy involving channel switching via an optical switch, using the maximization of the interface temperature difference as the criterion, which theoretically can reduce the localization error to 0 m. This research confirms the feasibility of geothermal-driven passive monitoring, providing a new paradigm for establishing a green, low-energy-consumption intelligent monitoring system.