Development of an ANN-based soft-sensor to estimate the apparent viscosity of water-based drilling fluids

Abstract

Oil exploration in marine fields located in deepwater and ultra-deepwater has been one of the challenges in oil production. To enable exploration in such conditions, huge investments have been made, and extensive research has been carried out in order to improve technology to the drilling fluids. In this way, the understanding and the monitoring of the properties of the drilling fluids are of vital importance to the exploration and production of oil in deepwater and ultra-deepwater conditions. The aim of this work was to develop a soft-sensor based on an artificial neural network (ANN) to estimate the apparent viscosity of the water-based drilling fluids. In the initial phase of this work, the influence of the additives on the apparent viscosity of the drilling fluids was investigated. This procedure was carried out by means of a complete three-level factorial experiment design, with central point in triplicate. The additives (factors) used were the xanthan gum, bentonite and barite. Since the temperature of the drilling fluid that returns from the well is different than that in the injection point (the drill string) this variable was also considered in the factorial experimental design in order to evaluate its influence on the apparent viscosity. The artificial neural network used in this work was a feedforward multilayer perceptron (MLP) with hyperbolic activation functions in the hidden layer and linear activation function in the output neuron. It was found that the ANN model with six neurons in the hidden layer presented the best predictions. The performance of the selected ANN and the statistical regression models were compared each other in the sense of the mean squared error, and the neuron model showed better predictions of the apparent viscosity.