Determination of Drilling Mud Density Change with Pressure and Temperature Made Simple and Accurate by ANN

An artificial neural networks (ANN) model has been developed to provide accurate predictions of mud density as a function of mud type, pressure and temperature. Available experimental measurements of water-base and oil-base drilling fluids at pressures ranging from 0 to 1400 psi and temperatures up to 400°F were used to develop and test the ANN model. With the knowledge of the drilling mud type (water-base, or oil-base) and its density at standard conditions (0 psi and 70°F) the developed model provides predictions of the density at any temperature and pressure (within the ranges studied) with an average absolute percent error of 0.367, a root mean squared error of 0.0056 and a correlation coefficient of 0.9998.