Downhole Leak Detection Using High Frequency DAS and DTS Signals from Fiber Optics Cables

The aim of this work is to assess the feasibility of detecting well fluid leaks using the high-frequency part of the acoustic field in Distributed Acoustic Sensing (DAS) signals. Particularly, this advance signal analysis is carried out as part of permanent monitoring using distributed fiber optic sensing (DFO). A comprehensive approach combining numerical simulations and analytical modeling was employed to analyze the high-frequency (1-10 kHz) component of the leak-generated acoustic field. Our approach overcomes the complexities of simulating the entire wavefield spectrum by decoupling the low-frequency and high-frequency components, thus enabling a more focused investigation of the high-frequency component. A custom-built DAS model based on the previous studies (Liokumovich et al, 2015; Alekseev et al, 2020) was used to simulate the high-frequency measurement process, considering the effects of spatial averaging and filtering introduced by the wellbore and surrounding formations. Spectra of the simulated acoustic field and DAS response are calculated and analyzed to identify features that may indicate leakage. Our results demonstrate that the high-frequency component of the DAS signal can provide valuable insights into leak characteristics, including location, severity, and type. However, accurate interpretation requires careful consideration of the effects of well and DAS measurement systems on the acoustic noise. Frequency bands with high noise signals can be identified by analyzing the well system, including wellbore geometry and material, and fluids and formation elastic properties. Additionally, the DAS measurement system introduces spatial averaging of the signal, leading to smoothed and smeared signals over depth. By understanding these relationships, we have established guidelines for optimizing leak detection strategies using DAS. Notably, our findings highlight the importance of selecting optimal frequency bands and considering the impact of spatial averaging on signal quality. This research contributes significantly to the development of advanced leak detection methods using DAS technology. By emphasizing the need to consider both well and DAS measurement system effects, our work provides new perspectives on interpreting high-frequency DAS signals. These insights enable more effective optimization of leak detection strategies, ultimately enhancing the efficiency and reliability of well integrity monitoring.