Theories, technologies, and future prospects of distributed fiber-optic strain monitoring for hydraulic fracturing

Distributed strain sensing (DSS), benefiting from its high sensitivity, high spatial resolution, real-time capability, and continuous distributed measurement, has become an important technique for hydraulic fracturing diagnostics. To provide a comprehensive understanding of recent advances in DSS for hydraulic fracturing monitoring, this paper reviews three representative DSS technologies, namely low-frequency distributed acoustic sensing (LF-DAS), rayleigh frequency-shift distributed strain sensing (RFS-DSS), and optical frequency domain reflectometry-distributed strain sensing (OFDR-DSS), with respect to their sensing principles, downhole fiber-deployment methods, field applications in hydraulic fracturing, and associated diagnostic theories, and further outlines key directions for future development. The review indicates that: (1) LF-DAS, based on interferometric detection of coherent Rayleigh-scattering phase variations, is primarily sensitive to far-field strain-rate perturbations in offset wells and is therefore well suited for monitoring fracture hits, fracture propagation, and inter-well interference. RFS-DSS, which relies on coherent Rayleigh-scattering spectral-shift interrogation, is designed for high-resolution quasi-static strain measurements in the treatment well and is particularly effective for post-fracturing production monitoring. OFDR-DSS, employing swept-frequency coherent detection, is mainly used in laboratory experiments and numerical-model validation; (2) Downhole fiber-optic deployment has evolved from permanent behind-casing installation to permanent/semi-permanent outside-tubing configurations and fully retrievable inside-tubing configurations. Among these, behind-casing installation provides the best coupling to the formation, outside-tubing installation offers improved maintainability and flexibility, and inside-tubing installation provides the greatest operational convenience; (3) The diagnostic theory of DSS has evolved from qualitative interpretation of field measurements and qualitative multi-physics forward modeling to quantitative inversion of fracture parameters using optimization-based methods. With the integration of artificial intelligence, a comprehensive diagnostic workflow is emerging that consists of physical signal acquisition, forward modeling, inversion and interpretation, and intelligent diagnosis; (4) DSS still faces limitations in spatial coverage and temperature sensitivity. Consequently, multiwell deployment and the integration of DSS with DTS, DAS, microseismic monitoring, and other monitoring techniques are increasingly adopted, making multimodal cooperative monitoring an important direction for future development. Looking ahead, DSS is expected to achieve enhanced sensing performance and more robust diagnostic theories, becoming one of the key technologies for hydraulic fracturing monitoring.