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ABSTRACT: A true tri-axial experiment was conducted to simulated hydraulic fracture propagating process with a 30 cm cubic block cement sample. At the same time, optic fiber in the cement sample recorded strain signals. In this paper, a fiber strain/strain rate calculation method based on wavelet noise reduction algorithm is proposed. The experimental results show that different spatial relationships between the optic fiber and the fracture surface correspond to different optic fiber signal responses. When the fracture surface is perpendicular to the optical fiber, obvious red stretching strips will be formed due to the fracture hitting the optical fiber. However, when the fracture surface is parallel to the optical fiber, the optical fiber will produce a large-scale micro-stretching zone. At the same time, the coupling effect between the fiber and the formation can also affect the signal responses. These results are profitable to understanding Distributed Acoustic Sensing (DAS) signals in the field. 1. INTRODUCTION Horizontal well fracturing technology in low permeability reservoirs greatly improves the efficiency of unconventional oil and gas development (Warpinski et al., 1991). Distribution and morphology of hydraulic fractures are extremely important for oil and gas production. But it has never been fully understood what the expansion pattern of underground hydraulic fractures actually looks like. Oil companies and service companies have developed technologies such as microseismic, tiltmeters, tracers, etc. for this purpose (Fisher et al., 2005, Maxwell et al., 2009, Magdalene et al., 2022). Due to the complexity of the subsurface, all monitoring techniques can only indirectly reflect some of the characteristics of the hydraulic fracture. Low-frequency DAS(LF-DAS) signals (0.05 Hz) are emerging as a new tool for monitoring fracture morphology, providing a completely new approach to hydraulic fracture monitoring (Jin et al., 2017). (Liu et al., 2020 and 2021) used the displacement discontinuity method to simulate the response of a fracture hitting a fiber under single and multiple clusters, and used the fiber data to invert the fracture hitting point location and fracture width information. (Chen et al., 2022) analyzed and summarized the evolution of the strain rate waterfall diagram before and after the fracture hit the fiber. (Smith Leggett et al., 2022) proposed a "zero-strain" localization method for assessing the dynamic location of fracture leading edges by indoor fiber-optic monitoring of fracturing. (Chen et al., 2021) applied machine learning algorithms to fiber optic data processing in an attempt to assist in determining information such as the location of fracture hits.
Hao et al. (Sun,) studied this question.