Modelling the Effects of Temperature and Pressure on Equivalent Circulating Density (ECD) During Drilling Operations Using Artificial Neural Networks

Abstract

Incorrect evaluation of equivalent circulating density (ECD) while drilling oil and gas wells may result in drilling problems such as lost circulation, kicks, differential pipe sticking etc especially in narrow drilling margins. Due to the incompressible nature of liquids, increase in wellbore pressure will only have appreciable effect on the fluid rheology at higher pressures, whereas a small increase in temperature may cause a decrease in the rheology. One thousand and eleven (1,011) field data obtained from high pressure; high temperature (HPHT) wells were used to develop artificial neural networks (ANNs) for this study. Training data were used to train the network while validation data were used to guarantee that the network generalizes at the training stage. Test data were used to evaluate the prediction capability of the developed model. Four error metrics, namely R-square (R2), mean square error (MSE), root mean square error (RMSE) and average absolute percentage error (AAPE) were used to assess the performance of the developed networks. Forecasts from the testing data indicate the optimized ECD model produced a prediction accuracy; R2 of 0.9993, MSE of 0.000265, RMSE of 0.01628 and AAPE of 0.337. The optimized ECD model performed better than existing ECD models in terms of the prediction accuracy and the calculated errors. The developed ECD model will help in improving the ECD prediction during the pre-drill design phase, which is quite critical in narrow drilling margin wells.