Artificial neural network (ANN) approach to predict unconfined compressive strength (UCS) of oil and gas well cement reinforced with nanoparticles

Abstract

The prediction of unconfined compressive strength (UCS) of oil well cement class “H” based on the artificial neural network (ANN) modeling approach is presented in this study. 195 cement samples were embedded with varying dosages of strength enhancing pre-dispersed nanoparticles consisting of nanosilica (nano-SiO2), nanoalumina (nano-Al2O3), and nanotitanium dioxide (nano-TiO2) at various simulated wellbore temperatures. The efficacy of the pre-dispersed nanoparticle solutions was analyzed by transmission electron microscope (TEM) images. Nano-SiO2 and nano-Al2O3 displayed excellent dispersibility throughout the solution. However, nano-TiO2 readily agglomerates which, at high concentrations, is detrimental to the UCS of cement. 70% of the data set was used to train the ANN model, 15% was used for validation, and 15% was used to test the model. The model consisted of one input layer with five nodes, one hidden layer with 12 nodes, and one output layer with one node. 12 nodes in the hidden layer resulted in the lowest mean squared error (MSE). The model parameters were saved and used after seven epochs during training, at which point the validation error began to increase leading to overfitting. The statistical performance measures consisting of MSE, the square root of the coefficient of determination (R), and the mean absolute percentage error (MAPE) showed values close to zero, one, and less than five percent, respectively. The statistical performance measures of the ANN model displayed superior results when compared to the measures obtained by the multi-linear (MLR) and random forest (RF) regression algorithms. The developed ANN model displays high predictive accuracy and can replace, or be used in combination with, destructive UCS tests which can save the petroleum industry time, resources, and capital.