Today's oil and gas drilling operations often face significant technical challenges, especially in remote locations with increasingly difficult geological settings. Stuck pipe incidents have become a major operational challenge for the exploration and production industry, with events typically resulting in substantial amounts of lost time and associated costs. Real-time monitoring has emerged as an important tool to achieve drilling optimization in avoiding downtime, particularly stuck pipe incidents. With the addition of a predictive monitoring system, this process becomes much more effective and competent. Predictive monitoring is used for advanced real-time monitoring in Wells Real Time Center (WRTC) and operational workflows to aid in the drilling execution of complex or critical well sections. The emphasis will be on reducing the complexity of real-time data analysis by utilizing trends and deviations between modelled and actual data to monitor wellbore conditions. This monitoring system and trend-based predictive capability enable drilling teams to detect borehole changes and take preventive action up to several hours in advance. By maximizing productive time, it improves operational efficiency. Predictive monitoring can provide early warning of stuck pipe symptoms, allowing the rig and operations team to take corrective and step-by-step actions. In raw drilling data, the conditions that lead to the stuck pipe can be difficult to read and detect. Various factors may indicate potential problems, but these are frequently missed until the situation has progressed to the point where the drill string becomes stuck. This system could have provided the rig crew with advance notice of changes in downhole conditions, in this case, avoiding the stuck pipe situation. We will look into predictive monitoring adoption in Field B operation as an example. Well E is a highly deviated extended reach well (ERD), with a 12,000ft long horizontal section, exceptionally challenging in terms of geomechanics perspective as well as the well design. When original Well E was drilled, a stuck pipe was encountered which caused the wellbore to be sidetracked. Predictive monitoring was implemented to assist drilling operation for the sidetracked well, and it had been completed successfully with minor hole condition issues. The predictive monitoring system is built around a trio of tightly coupled real-time dynamic models consisting of hydraulic, mechanical, and thermodynamic that simulate the wellbore state and physical processes during drilling operations. These models work together continuously to assess drilling performance, borehole conditions, and any other associated risks. It uses dynamic modelling to accurately model key drilling parameters and variables such as hook load, surface torque, cuttings transport, tank volumes, standpipe pressure, and equivalent circulating density (ECD) in real-time.

The execution phase of the wells technical assurance process is a critical procedure where the drilling operation commences and the well planning program is implemented. During drilling operations, the real-time drilling data are streamed to a real-time centre where it is constantly monitored by a dedicated team of monitoring specialists. If any potential issues or possible opportunities arise, the team will communicate with the operation team on rig for an intervention. This workflow is further enhanced by digital initiatives via big data analytics implementation in PETRONAS. The Digital Standing Instruction to Driller (Digital SID) is a drilling operational procedures documentation tool meant to improve the current process by digitalizing information exchange between office and rig site. Boasting multi-operation usage, it is made fit to context and despite its automated generation, this tool allows flexibility for the operation team to customize the content and more importantly, monitor the execution in real-time. Another tool used in the real-time monitoring platform is the dynamic monitoring drilling system where it allows real-time drilling data to be more intuitive and gives the benefit of foresight. The dynamic nature of the system means that it will update existing roadmaps with extensive real-time data as they come in, hence improving its accuracy as we drill further. Furthermore, an automated drilling key performance indicator (KPI) and performance benchmarking system measures drilling performance to uncover areas of improvement. This will serve as the benchmark for further optimization. On top of that, an artificial intelligence (AI) driven Wells Augmented Stuck Pipe Indicator (WASP) is deployed in the real-time monitoring platform to improve the capability of monitoring specialists to identify stuck pipe symptoms way earlier before the occurrence of the incident. This proactive approach is an improvement to the current process workflow which is less timely and possibly missing the intervention opportunity. These four tools are integrated seamlessly with the real-time monitoring platform hence improving the project management efficiency during the execution phase. The tools are envisioned to offer an agile and efficient process workflow by integrating and tapering down multiple applications in different environments into a single web-based platform which enables better collaboration and faster decision making.

Significant technical challenges are prominent in today's oil and gas drilling operations, especially in remote locations with increasingly difficult geological settings. Stuck pipe incidents have become a major operational challenge, with events typically resulting in substantial amounts of lost time and associated costs. Real-time monitoring has emerged as an important tool to achieve drilling optimization in avoiding downtime, particularly stuck pipe events. With the addition of a predictive monitoring system, this process becomes much more effective and competent. Predictive monitoring is used for advanced real-time monitoring in the remote centre and operational workflows to aid in the drilling execution of complex or critical well sections. The emphasis will be on reducing the complexity of real-time data analysis by exploiting trends and anomalies between modelled and actual data to monitor wellbore conditions. This monitoring system and trend-based predictive capability enable drilling teams to detect borehole changes and take preventive action up to several hours in advance. Predictive monitoring can provide early warning of stuck pipe symptoms, allowing the rig and operations team to take corrective and step-by-step actions. The circumstances that lead to the stuck pipe can be difficult to detect as various factors may indicate potential problems. These are frequently missed until the situation has progressed to the point where the drill string becomes stuck. This system could have provided the rig crew with advance notice of changes in downhole conditions. An example of predictive monitoring adoption in a highly deviated extended reach well (ERD), with a 12,000ft long horizontal section is presented. It is exceptionally challenging in terms of geomechanics perspective as well as the well design. Predictive monitoring was implemented to assist drilling operation for the sidetracked well, and it had been completed successfully with minor hole condition issues. The predictive monitoring system is built around a trio of tightly coupled real-time dynamic models consisting of hydraulic, mechanical, and thermodynamic that simulate the wellbore state and physical processes during drilling operations. These models work simultaneously in a seamless process to assess drilling performance, borehole conditions, and related associated risks. It uses dynamic modelling to accurately model key drilling parameters and variables, allowing better monitoring.

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.