Improved Tracking of the Rheological Properties of Max-Bridge Oil-Based Mud Using Artificial Neural Networks

Lab measurements for the rheological properties of mud are critical for monitoring the drilling fluid functions during the drilling operations. However, these measurements take a long time and might need more than one person to be completed. The main objectives of this research are to implement artificial intelligence for predicting the mud rheology from only Marsh funnel (mu(f)) and measuring mud density (rho(m)) easily and quickly on the rig site. For the first time, an artificial neural network (ANN) was used to build different models for predicting the rheological properties of Max-bridge oil-based mud. The properties included the plastic viscosity (mu(p)), yield point (gamma), flow behavior index (eta), and apparent viscosity (mu(a)). Field measurements of 383 samples were used to build and optimize the ANN models. The obtained results showed that 32 neurons in the hidden layer and tan sigmoid function transfer function were the best parameters for all ANN models. The training and testing processes of models showed a strong prediction performance with a correlation coefficient (R) greater than 0.91 and an average absolute percentage error (AAPE) less than 5.31%. New empirical correlations were developed based on the optimized weights and biases of the ANN models. The developed empirical correlations were compared with the published correlations, and the comparison results confirmed that the ANN-developed correlations outperformed all previous work.

Automated summary

This research aimed to predict rheological properties of mud using artificial intelligence from Marsh funnel and mud density measurements, utilizing different artificial neural network models optimized with field data of 383 samples. The study demonstrated strong predictive performance of the models, with correlation coefficients higher than 0.91 and average absolute percentage errors less than 5.31%.