Artificial Neural Networks for Modeling Rheological Properties under High-Temperature and High-Pressure in Gas Well Cement Slurries

Abstract

Artificial neural networks (ANN) was used to predict the rheological properties of high temperature high pressure gas well cement slurries. Seven different materials were used as additives which includes: Fresh water, dyckerhoff, silica flour, antifoam, extender, fluid loss, dispersant, retarder, anti-settling agent, gas control agent, dry viscosifier, potassium chloride and accelerator. Four recipes were prepared using these additives in different mixtures. Recipe four have all the additives. The rheological properties were investigated at different temperatures in the range of 23 to 60ºC using an advanced shear-stress/shear-strain controlled rheometer. Experimental data thus obtained were used to develop predictive models based on back- propagation artificial neural networks. It was found that ANN depicted good agreement with the experimental data, with ANN achieving more accurate predictions. The developed models could effectively predict the rheological properties of new slurries designed within the range of input parameters of the experimental database with an absolute error of 3.43, 3.17, and 2.82%, in the case of ANN, for the different recipes. The flow curves developed using ANN allowed predicting the Bingham parameters (yield stress and plastic viscosity) of the oil well slurries with adequate accuracy. The goal of the process is to choose the network that minimizes the prediction errors/RMSEs. There is however need to avoid an over-trained network. The result showed that over-training of the networks sets in around the scenario when the number of hidden layer neurons exceeds 9. It also demonstrates that the network with 9 hidden layer neurons gave the least RMSEs, and it is this network that has been adopted as the network for the final model development in this work.