Developing a machine learning-based methodology for optimal hyperparameter determination—A mathematical modeling of high-pressure and high-temperature drilling fluid behavior

•An innovative methodology is introduced for optimizing hyperparameters in predicting the rheological behavior of drilling fluids.•The study demonstrates the effectiveness of the ML-C3 neural network configuration, achieving remarkable accuracy with an MAE of 0.535 and an R² of 0.987.•The research highlights the critical role of balancing the number of training epochs to enhance shear stress prediction accuracy.•Detailed comparisons between traditional rheological models (Power-law and Herschel-Bulkley) and neural networks reveal the superior predictive capability of neural networks for drilling fluid behavior under extreme HPHT conditions.•The presented methodology provides a solid foundation for future applications in well design and drilling operation optimization, emphasizing the potential of neural networks in drilling engineering. Drilling fluids exhibit complex rheological behavior due to a non-linear response to shear rate variations and high sensitivity to changes in temperature, time, and pressure conditions. The prediction of drilling fluid rheological behavior is crucial for the success of oil well drilling, and it directly impacts the fluid's performance. The dataset used in this study was obtained from extensive rheometric tests of water-based and olefin-based drilling fluids in steady-state flow curves. The optimal hyperparameters were guided by performance metrics and compared with alternative models such as Power-law and Herschel-Bulkley rheological models. Different configurations with different hidden layers, using neuron sequences of 16, 32, and 64, learning rates of 0.001 and 0.01, and the ReLU activation function were used to improve the model's performance. Additionally, the paper delved into the impact of the number of training epochs on the accuracy of shear stress predictions. Finding this equilibrium was identified as a crucial factor in achieving precise results. The neural network model demonstrated remarkable accuracy when using the ML-C3 configuration, with MAE values of 0.535 and R2 of 0.987 in predicting the steady-state flow curves of drilling fluids, establishing itself as a powerful tool for forecasting the rheological behavior of these fluids under diverse operational conditions. The present research significantly contributes to the field of drilling fluid rheology and provides valuable insights for optimizing drilling operations in HPHT environments.