Optimizing the surface parameters to maximize the rate of penetration (ROP) could be achieved through a machine learning (ML) framework. Hence, predicting ROP is a crucial factor in any optimization problem.

The objective of this study is to derive additional options that will improve ROP by providing more flexibility in BHA design than those provided by traditional WOB-rotary speed methods.

The system's (to predict Standpipe Pressure and apparent surface torque) development used non-stochastic gradient decent tools to achieve the global minimum of the solution, contrary to most developed models' approaches to that topic.

This work presents a methodology for estimating localized modified Bingham equation parameters using the Genetic Algorithm (GA). The case study was conducted on a large dataset collected from 260 wells.