This review assesses hydraulic fracture diagnostic techniques and highlights their integration challenges, suggesting improvements.
Hydraulic fracture diagnostics are essential for characterizing fracture geometry, connectivity, and effectiveness in unconventional reservoirs. However, the diversity of available techniques and fragmented understanding of their physical mechanisms hinder multidisciplinary communication and lead to inconsistent field decisions. This review provides a systematic assessment of diagnostic methods, focusing on their physical foundations, applicability, and limitations, and proposes a unified reference framework. Direct diagnostics, including microseismic monitoring, fiber-optic sensing (DTS and DAS), and tiltmeter measurements, are evaluated in terms of data characteristics, interpretation challenges, and field applicability. Indirect methods based on pressure, production, and tracer data—such as DFITs, pressure interference tests, and tracer analysis—are examined for their roles in fracture closure evaluation and interwell connectivity. The review further distinguishes between single-well and multi-well applications, providing a structured classification framework. It highlights that individual methods are constrained by non-uniqueness, modeling assumptions, and non-ideal field conditions, especially in complex fracture networks. Therefore, reliable characterization requires integrating multiple diagnostics with physics-based modeling and uncertainty-aware interpretation. Recent advances in AI and machine learning are also briefly discussed as tools to enhance automated analysis and support real-time, predictive diagnostics.
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Bai et al. (2026) studied this question.
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