Forecasting of the interaction between hydraulic and natural fractures using an artificial neural network

In recent years, the increasing energy demand has led the oil and gas industry to explore unconventional reservoirs. The hydraulic fracturing technique (fracking) has been adopted in order to increase the reservoir drainage area. Nevertheless, there is an environmental concern about the contamination of aquifers due to this technique. The operation design requires predicting the induced fracture geometry to avoid hazards related to fracking. Hydraulic fracturing changes the state of stress at crack tip leading to more uncertainties in the definition of crack geometry, especially in naturally fractured formations. For such, analytical solutions and numerical simulations have been employed in recent decades. Nevertheless, the numerical models require high computational effort. This paper proposes an artificial neural network (ANN) to predict the interaction between hydraulic fracture and natural fractures. We performed over 800 simulations to build the training database varying the rock mechanical properties and model parameters, such as the approach angle between hydraulic fracture and natural fracture, in-situ stress magnitudes, friction angle, and fracture energy. The ANN results are compared against analytical solutions and numerical models, showing excellent agreement. These results show that the trained neural network can predict fracture interaction accurately. They also suggest that the most sensible parameters were taken into account in the proposed ANN.