LAUSR

Abstract As the drilling environment became more challenging nowadays, managing equivalent circulating density (ECD) is a key factor to minimize non-productive time (NPT) due to many drilling obstacles such as stuck pipe, formation fracturing, and lost circulation. The goal of this work was to predict ECD prior to drilling by using artificial neural network (ANN). Once ECD is recognized, the crucial drilling variables impact ECD can be modified to control ECD within the acceptable ranges. Data from over 2000 wells collected worldwide were used in this study to create an ANN to predict ECD prior to drilling. Into training, validation, and testing sets, the data were splitted. 70% of the data utilized for training, the other part used for validation and testing to avoid overfitting and create a generalized network that can perform well on new data. Based on the mean square of error (MSE), a decision was made to have one hidden layer with twelve neurons, this scenario was selected since it gave the lowest MSE among other scenarios. Multiple training functions were tested to train the network, Bayesian regularization (BR) algorithm was chosen from the other algorithms since it had the lowest MSE and the highest R-squared. After optimizing the weights and biases, the results revealed that the created network has the ability to estimate ECD with an overall R-squared of 0.982, which is very high. This result gives an indication that the created network can predict ECD prior to drilling globally within a very small margin of error. Due to the availability of large historical data sets in the petroleum industry, the ANN can be used to make better future decisions to minimize NPT and the cost of drilling.