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<p>Naturally fractured reservoirs are essential for subsurface energy production and storage. However, the complexity and uncertainty inherent to fracture network properties make it difficult to characterise fluid flow within them. This study presents an unsupervised machine learning workflow that constrains uncertainty by establishing a systematic link between the pressure transient response observed at the well and the underlying fracture network properties. We generate a geologically consistent ensemble of 4,850 discrete fracture networks (DFNs) and simulate pressure transient responses for the same geometries under three matrix-fracture permeability configurations. For each dataset, we group pressure derivative responses into geologically interpretable flow behaviour clusters using Dynamic Time Warping (DTW) based K-medoids clustering. The resulting cluster medoids provide representative pressure derivative responses that summarise the dominant flow regime sequence within each class. The workflow consistently identifies four stable clusters across all datasets, each characterised by a distinct and repeatable sequence of diagnostic flow regimes consistent with a bounded range of fracture network properties. Feature importance ranking and SHAP values derived from a random forest classifier show that fracture intensity, wellbore fracture length, and backbone fracture fraction provide the strongest geological controls on cluster separation and hence on the emergent diagnostic signatures. Comparing clusters across datasets shows that 64.1% of DFNs retain their cluster membership, indicating that the clustering structure is primarily controlled by DFN geometry. However, for a fixed DFN, the pressure transient response varies across matrix-fracture permeability configurations, producing systematic shifts in derivative levels and in the dominance of specific flow regimes. The representative pressure derivative responses associated with each flow behaviour cluster are therefore not identical across datasets and must be interpreted within the matrix-fracture permeability configuration. Overall, the proposed framework provides a basis to constrain geological uncertainty and prioritise high-impact parameters for data acquisition in naturally fractured reservoirs, thereby improving reservoir characterisation and decision making in the early appraisal stage.</p>

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Unsupervised learning for geologically consistent fluid flow analysis in fractured reservoirs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Elahe Kamel Targhi,
  • Guillaume Rongier,
  • Pierre-Olivier Bruna,
  • Alexandros Daniilidis,
  • Sebastian Geiger

摘要

Naturally fractured reservoirs are essential for subsurface energy production and storage. However, the complexity and uncertainty inherent to fracture network properties make it difficult to characterise fluid flow within them. This study presents an unsupervised machine learning workflow that constrains uncertainty by establishing a systematic link between the pressure transient response observed at the well and the underlying fracture network properties. We generate a geologically consistent ensemble of 4,850 discrete fracture networks (DFNs) and simulate pressure transient responses for the same geometries under three matrix-fracture permeability configurations. For each dataset, we group pressure derivative responses into geologically interpretable flow behaviour clusters using Dynamic Time Warping (DTW) based K-medoids clustering. The resulting cluster medoids provide representative pressure derivative responses that summarise the dominant flow regime sequence within each class. The workflow consistently identifies four stable clusters across all datasets, each characterised by a distinct and repeatable sequence of diagnostic flow regimes consistent with a bounded range of fracture network properties. Feature importance ranking and SHAP values derived from a random forest classifier show that fracture intensity, wellbore fracture length, and backbone fracture fraction provide the strongest geological controls on cluster separation and hence on the emergent diagnostic signatures. Comparing clusters across datasets shows that 64.1% of DFNs retain their cluster membership, indicating that the clustering structure is primarily controlled by DFN geometry. However, for a fixed DFN, the pressure transient response varies across matrix-fracture permeability configurations, producing systematic shifts in derivative levels and in the dominance of specific flow regimes. The representative pressure derivative responses associated with each flow behaviour cluster are therefore not identical across datasets and must be interpreted within the matrix-fracture permeability configuration. Overall, the proposed framework provides a basis to constrain geological uncertainty and prioritise high-impact parameters for data acquisition in naturally fractured reservoirs, thereby improving reservoir characterisation and decision making in the early appraisal stage.