(753e) Deep Learning Based Hybrid Model of Hydraulic Fracturing Process
Hydraulic fracturing is a highly complex and non-linear process of extracting oil/gas from rock formations which have low porosity and permeability. Its first principles based models consist of a system of nonlinear highly-coupled PDEs with time-dependent spatial domain and usually assume homogeneity of reservoir rock properties [5, 6]. But in reality the rock properties such as Youngâs modulus, rock permeability and porosity etc. vary even within the same formation. Other process parameters such as fluid leak-off coefficient, proppant particle size are assumed to be constant throughout the process which is not true. The effect of these uncertainties cannot be undermined and they need to be integrated with the first principles based model in order to improve its prediction accuracy during the propagation of hydraulic fracture. We aim to achieve this by developing a hybrid model which includes a deep neural network that will approximate the uncertainties within the rock formation and a first principles based model that will explain the dynamics of the process. The overall structure of the hybrid model is similar to the first principles model which helps in interpreting the quantified uncertainties. We will demonstrate the superiority of this hybrid model in comparison to the strictly first principles based model in terms of prediction accuracy and also in comparison to strictly deep neural network based model in terms of reliability and extrapolation.
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