Accurate identification of fluid-entry clusters during hydraulic fracturing, and fine-scale diagnosis of downhole fracturing events, are crucial for optimizing fracturing design and enhancing reservoir stimulation performance. However, existing water hammer pressure-based monitoring methods for fracturing are mostly limited to identifying the dominant fluid-entry cluster within a stage and struggle to provide a fine-scale diagnosis of downhole fracturing events. This study establishes a fracturing monitoring method based on high-frequency water hammer pressure. By performing time-domain and frequency-domain analysis on the high-frequency water hammer signal and employing a composite filtering method, the signal quality was significantly enhanced. Furthermore, this study achieved the identification of multiple fluid-entry clusters within a stage and the fine-scale diagnosis of downhole fracturing events through cepstrum analysis and time-depth conversion, as well as the effective characterization of the dynamic distribution of fracturing fluid under different conditions. Verification with simulated data confirms that the identification results are consistent with the simulation settings, thus validating the reliability of the method. Field application has enabled the fine-scale diagnosis of downhole fracturing events such as diverter effectiveness, plug leakage, and plug slippage, and further analyzed the cepstral response characteristics and treating pressure curve characteristics associated with different events. The proposed method provides an effective tool for diagnosing fracturing conditions during field operations, thereby offering valuable insights for optimizing fracturing design and enhancing reservoir stimulation effectiveness.
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