When evaluating oil and gas wells, spectral noise and fiber optic logs are currently employed to detect fluid movement behind pipes, which can indicate issues with tubular or cement integrity, as well as movement through the formation. However, these logs come with a substantial cost and require specialized processing. This study aims to identify fluid movement in wells by developing a thermal energy model based on temperature data from the Production Logging Tool (PLT), as well as obtaining an equivalent relationship with the acoustic energy derived from noise spectral, ultrasonic Doppler and fiber optic logs.
Heat, light, and sound propagate in waves. The methodology involves acquiring spectral noise logs (both in low and high frequency), fiber optic logs (including DTS - distributed temperature sensing, and DAS - distributed acoustic sensing), and PLT logs from the wells. The goal is to establish mathematical model for thermal energy. Initially, the spectral noise, Doppler, DTS, and DAS logs are processed to generate noise spectrum maps and determine the acoustic energy within the reservoir-well system. Concurrently, dynamic temperature data from the PLT logs are utilized to calculate the thermal energy model using the root mean square (RMS) of the first derivative of temperature. Finally, all available information is integrated to create fluid movement patterns in the wells and establish correlations between the various measurements.
The findings revealed that when a fluid (water, oil, or gas) flows perpendicular to a cross-sectional area, it generates vibration, friction, and consequent noise and temperature variations. The temperature variation is directly proportional to the fluid velocity and inversely proportional to the cross-sectional area. Moreover, according to Bernoulli's equation (1738), dynamic pressure decreases as fluid velocity increases within a reduced cross-sectional area. Notably, thermal energy and acoustic energy are found to be equivalent. By comparing the acoustic energy logs from spectral noise with the thermal energy logs, an excellent correlation between the two is observed. Additionally, the spectrum maps derived from Doppler, DAS, and DTS exhibit behavior consistent with thermal energy, allowing for the identification of similar fluid movement patterns within the formation, behind the pipe, and in cases of leaks. These findings demonstrate a high level of reliability in either measurement method.
This methodology proves particularly valuable for wells that possess only temperature logs, as it offers a more cost-effective alternative to specialized logs enabling the identification of fluid movement within the formation and behind the pipe, validating well integrity and facilitating continuous surveillance over time.
