With the current format of SIDs being completely different each project and manually typed in every rigs, improving operational performance based on lessons learned can be quite complicated. The digitalization of Standing Instruction to Driller (SID) can help to overcome this pain point by standardizing and synchronizing SIDs throughout company's operations globally. The paper approaches the subject by introducing a much leaner and seamless method in registering an SID. This can be achieved by having relevant drilling personnel to pick and choose from a common database of SIDs which is frequently updated and verified via web-based platform. The dropdown will be automatically filtered by the application according to the BHA and/or other relevant filter categories chosen. Digital SID drafting process will begin after Notice of Operations (NOOP) has been completed. Information from the NOOP will be populated in the Digital SID platform with all the required associated details including but not limited to BHA designs, hole sections, depths, procedures and lessons learned. These SIDs will have to be agreed and approved by assigned personnel prior to the execution of the operation. Since the solution is capable to create a high quality SID which is readable by both human and machine, it can be integrated with real-time sensors to provide automatic detection for every operation in the SIDs. Therefore, implementation of this Digital SID solution can sustain SIDs consistency across different projects and capable to embed insights in context for future reference. Direct business value created from Digital SID improves process cycle efficiency in a project well life cycle.

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.