Experimental investigation of hydraulic fracturing in random naturally fractured blocks

Currently, it is still challenging to determine the geological parameters of natural caves in deep reservoirs accurately. By inheriting the advantages of Machine Learning (ML) method and physics modelling, a novel ML-Physics method is developed to determine the geological parameters of natural caves based on the data mining of fracturing curves obtained during Hydraulic Fracturing (HF) operation. The computational time of ML-Physics method is divided into two stages, preparation-stage and operation-stage. The preparation-stage happens before HF operation, therefore there is no limitation to the computational time. During this preparation-stage, the implicit relationship between cave property and fracturing curve is generated using ML, which usually fails to ensure the accuracy under different geological and operational conditions. The operation-stage happens during HF operation, in which the computational time is limited because the geological parameters of natural caves are required to be determined in real time. During this operation-stage, the physical modelling based inverse analysis method is carried out, in which the initial value is chosen based on the ML results obtained in preparation-stage. Results show that, with the same target error, the iteration step of ML-Physics method (1 iteration) is much less than that of traditional inverse analysis method (5 iterations). After the same iterations, the error of fracturing curve using the traditional inverse analysis method is 0.40%, while the error using ML-Physics method is 0.02%. Meanwhile, the error of permeability using the traditional ML is up to 10.33%, while the error of ML-Physics method is 0.29%. The present ML-Physics method is potentially useful to optimize the HF design based on the data mining of fracturing curves in real time.