Optimising the Rate of Penetration (ROP) on Development wells contributes heavily to delivery of projects ahead of schedule and has long been a goal for drilling engineers. Selecting the best parameters to achieve this has often proved difficult due to the extensive quantities of data concerning formation types, bottom-hole assembly (BHA) design and bit specifications. Legacy drilling data can also be vast and not well characterised, making it very difficult to robustly analyse manually. Additionally, multiple stakeholders can each have their own hypotheses on how to improve drilling performance, including bit vendors, directional drilling companies, drilling engineers and offshore supervisors, creating further confusion in this field. Together with its team of data scientists, TotalEnergies E&P UK (TEPUK) has utilised machine learning to analyse field and equipment data and produce guidelines for optimised drilling rate. The machine learning algorithm identifies parameters which have a statistical likelihood of improving ROP performance whilst drilling. The model was developed using offset well data from TotalEnergies' Realtime Support Centre (RTSC) and bit design information. This represented the first use of Machine Learning in the 20+ years of drilling on Elgin Franklin. Adapting to this new data-based method forms part of a wider digital revolution within TEPUK and the Offshore Drilling Industry. In this case, an integrated approach from the data scientists, drilling engineers and supervisors was required to transition to a new way of working. The first trial of using optimised parameters was on a recent Franklin well (F13) in the Cretaceous Chalk formations. The model generated statistically optimised parameter sheets which were strictly executed on site. Within the guideline sheets were suggested ranges of Revolutions per Minute (RPM), Flowrate, Weight on Bit (WOB) and Torque, as well as recommendations for bit blades and cutters. Heatmaps were generated to show what combination of WOB and RPM would likely achieve best ROP in each sub formation. The parameter range defined was specifically narrow to reduce any time spent varying parameters. In practice the new digital approach was successfully adopted offshore and contributed to the delivery of the 12 ½" and 8 ½" sections in record time for the field, resulting in significant savings versus AFE. Following the success of the guideline implementation, steps have been taken to integrate the machine learning model with live incoming data on TotalEnergies' digital drilling online platform. Since the initial trial on Franklin, online ROP optimisation features have been deployed on the Elgin field and currently provide live parameter guidance, a forecast to section TD and data driven bit change scenario analyses whist drilling.