Drilling operation real-time centre is envisaged to move forward from having a reactive workflow to being a proactive and predictive monitoring centre. Future state of a fully capable wells real-time centre will systematically integrate previously isolated and underutilized well engineering systems and applications and implement big data analytics by manipulating machine learning algorithm to automate recognition of potential non-productive time incidents. Current setup of real-time centre is presented and compared with future framework corresponding to service delivery process map, real-time system architecture, drilling data flow, and applications which include interventions protocol. Service delivery process map will progress from a directly linear to a close-loop integrated workflow. Applications and databases integration in a single platform on cloud will constitute a two-way connectivity of real time actual measured versus simulated and historical data for an enriched predictive monitoring of drilling operations via established machine learning algorithm. The machine learning algorithm will perform symptom analysis and automation of flag tagging to recognize potential events with inputs from existing combination of static and dynamic monitoring. This data driven approach is capable of leveraging on unprecedented amount of data to provide unbiased algorithm with rigorous trainings. Dynamic monitoring drilling system allows dynamic drilling parameters to be observed continuously and generates cutting simulations to forecast hole conditions and dynamic equivalent circulating density. Running along these systems is the technical limit and benchmarking tool which creates numerous key performance indicators for improvement in drilling operations and pushing well engineering design to its technical limit. The implementation of these systems indicates that intelligent wells systems are achievable in the future by virtues of having enhanced and relevant information available at the opportune moment to help deliver time, cost and effort savings.

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