Estimation of minimum horizontal stress, geomechanical modeling and hybrid neural network based on conventional well logging data – a case study

Abstract

The minimum horizontal stress (Shmin) is one of the three principal stresses and is required for evaluation of the hydraulic fracturing, sand production, and well stability. Shmin is obtained using direct methods such as the leak-off and mini-frac tests or using some equations like the poroelastic equation. These equations require some information including the elastic parameters, shear sonic logs, core data and the pore pressure. In this study, a geomechanical model is constructed to obtain the minimum horizontal stress; then, an artificial neural network (ANN) with multilayer perceptron and feedforward backpropagation algorithm based on the conventional well logging data is applied to predict the Shmin. Cuckoo optimization algorithm (COA), imperialist competitive algorithm, particle swarm optimization and genetic algorithm are also utilized to optimize the ANN. The proposed methodology is applied in two wells in the reservoir rock located at the southwest of Iran, one for training, and the other one for testing purposes. It is found that the performance of the COA–ANN is better than the other methods. Finally, Shmin values can be estimated by the conventional well logging data without having the required parameters of the poroelastic equation.