Data and code supporting: Unsupervised Learning Workflow for Clustering Pressure Transient Responses in Fractured Reservoirs

This repository provides a complete and reproducible unsupervised learning workflow for clustering pressure transient responses in fractured reservoirs. The dataset and code support systematic analysis of pressure transient behaviour simulated for a large ensemble of geologically constrained discrete fracture networks under multiple matrix and fracture permeability configurations. The zip file includes:<br><strong>DFN dataset:</strong> 4,850 geologically consistent DFN geometries generated with GeoDFN from a literature-informed design of experiments and Latin Hypercube Sampling. The detailed DoE and the code used to generate the DFNs are provided.<strong>Pressure transient dataset:</strong> Synthetic well-test pressure responses for the same DFNs under three matrix–fracture permeability configurations (Datasets A–C), together with the MRST (EDFM) scripts used to generate the synthetic pressure transients from the DFN geometries.<strong>Machine-learning workflow:</strong> Code for preprocessing pressure and derivative signals, computing DTW distances, performing K-medoids clustering to group responses by transient shape, and training a Random Forest classifier to quantify which fracture network properties control flow-behaviour separation.<strong>Cross-dataset comparison:</strong> Scripts for cluster alignment between datasets, Sankey visualisation of transitions, and comparison of diagnostic signatures across permeability configurations.<br>This release is intended as a reusable research resource. The DFN dataset can serve as a benchmark for DFN modelling, upscaling, and flow simulation in fractured media. The pressure transient dataset supports development and evaluation of pressure-transient interpretation methods and machine-learning workflows. The analysis code provides an extensible template for clustering time series data, and can be adapted to other transient data types, such as thermal transients in geothermal systems.