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.

With the current rig acceptance workflow practiced by operators globally, the process efficiency gap has been apparent for years. Redundancy, accountability issues and resource wastages can be quite complicated. In a typical workflow, the issues encountered include lack of accountability by inspectors toward item closure, inability to generate snapshots of current status, limited access due to current update via e-mail distribution only, and inefficient process as updates have to be emailed to inspectors. Report formats are not standardized across different disciplines hence the experience is not seamless as there is no one-stop center to view aviation, marine, and HSE inspection items. In fact, some inspection items across disciplines are redundant to each other. The digitalization of rig acceptance workflow can help to overcome these pain points by having a single platform to allow multidiscipline parties to keep tabs on rig activation status and updates throughout company-wide operations globally during the rig acceptance process. The paper approaches the subject by introducing a much leaner and more seamless method for conducting rig acceptance. This can be achieved by having a web-based one-stop center for all things related to rig acceptance (i.e., marine, rig, HSE, and aviation). It grants the ability for inspectors and designated personnel (e.g., DSV) to insert comments for each finding as well as the ability for inspectors to assign and edit severity levels (P1/P2/P3) for each finding. The single platform approach allows the possibility to link up the other checklist and findings on the same system and immediately reduce the redundancy of certain items that is similar to other checklists, which can be streamlined online. Therefore, implementation of this Digital Rig Acceptance Workflow (DRAW) solution can produce a user-friendly online platform to allow inspectors, project teams, management, and rig equipment subject matter experts to access the system anywhere, anytime. DRAW allows status updates (i.e., open/ongoing/close) and clarifications to be communicated via a single platform. It utilizes data input to produce actionable insights (i.e., pie/bar charts, P1/P2/P3 status, etc.) hence generating direct business value via improving process cycle efficiency in a project well life cycle.

Wellbore geometry stuck pipe mechanism occurs when the string and the well are incompatible with each other. This issue is commonly related to changes in hole diameter, angle, and direction associated with symptoms such as mobile/swelling formation, undergauged hole, key seating, ledges, and high doglegs. An internal study identified that many stuck pipe incidents were associated with mechanical sticking, specifically wellbore geometry sticking with high-cost impact, which warrants proactive prevention. Throughout this paper, we provide and demonstrate how machine learning solutions can foresee the potential stuck pipe related to wellbore geometry issues based on two signs: hookload signature and dogleg severity. The application is based on the Artificial Neural Network (ANN) approach that reads the surface parameters sequence of hookload real-time data and learns with historical wells data. Machine learning (ML) then determines how the hookload behaves for each type of activity (tripping and drilling). The machine learning predictions can then be streamed on a web-based application accessible to the operations and project team. The neural network design for hookload prediction while tripping in/out considers a drag when the string moves towards a region with doglegs severity higher than the threshold chosen based on engineering judgment. This paper also discusses applications beyond real-time estimation, such as predicting the trend of the few subsequent expected hook loads up to 6 to 10 stands ahead based on case studies from previous live wells obtained from the real-time monitoring center where the product is used. The output from the machine learning solution provided a basis for risk identification and further analysis by the monitoring specialist in a proactive intervention effort to prevent stuck pipe incidents. The implementation of applications described in this paper could detect an early symptom of wellbore geometry issue; hence proactive action can be taken to avoid a potential stuck pipe event.

Since 2010, Samarang Alliance, a partnership between Petronas Carigali Sdn Bhd and Schlumberger SPM, has been redeveloping the offshore Samarang oil field. The objective of the Alliance is to maximize asset value through implementation of technology, processes and practices that enable infill drilling, reservoir management, EOR, Integrated Operations (IO) and production enhancement activities. One of the focus areas upon resource extraction initiatives of Samarang Alliance is through rigless intervention activities for production enhancement. The dynamics of Production Enhancement (PE) portfolio in Samarang requires the use of engineering and statistical tools to track their efficiency to be able to channel and strategize resources aiming for the highest return on investment (ROI). The Samarang Well Intervention Performance Evaluation (WIPE) methodology enables the user to assess the profitability of a given well intervention activity over time. Productive layers, time and job type are plotted which helps to triangulate the best avenues for investment. Incremental production is specifically calculated for different time-lines. "Budget Cycle" (Calendar year) to evaluate the efficacy of the current year planned work budget. "PE Cycle" when referring to the duration encompassing one year (12 months) to compare similar PEs irrespective of the month of execution. "PE Life" for the entire duration that the well production will be impacted by the PE. Additionally the calculation of Unit Enhancement Cost (UEC) for the different time-lines is considered, which provides a numerical estimate of the economic value of each PE. Superimposed with intervention history and reserve estimation, WIPE plots enables the user to find a specific profitable intervention solution to declining production. The "Opportunity Cost" can be calculated to a degree of high accuracy which supports the selection of new candidates and fulfills the requirements of a Signature Field like Samarang. Supported on available medium like Microsoft Excel, this powerful tool improves the decision making and planning processes for Brown Field redevelopment.

Digital transformation has always been one of the focuses of the oil & gas industry players in recent years. However, the pandemic and oil downturn last year has put the industry players in a digitization overdrive in the pursuit of leaner and cost-effective operations to stay ahead in these unprecedented times. This paper discusses the strategy, approach, and challenges in the adoption and implementation of the Automated Drilling Performance Measurement (ADPM) onsite and remote approach. This includes Wells Real-Time Center (WRTC), which utilizes the ADPM, an easy access analysis application for operational optimization. The implementation of ADPM falls under the PETRONAS Well Cost Compression focus area of Operational Optimization, which aims to achieve the operational technical limit and non-productive time (NPT) reduction. The stages of operational optimization via ADPM are broken down into pre-spud operations, operations, and post-well analysis. A historical performance study is conducted in pre-spud operations and case study sessions with the project team and Subject Matter Experts (SMEs). Once in operations, best practices for the focused key performance indicator (KPI) and ad-hoc gap analysis are implemented onsite throughout the well construction. Remotely, the KPIs are monitored by WRTC while rig contractors monitor the crew performance. The performance review is studied in post-well analysis, and the best practices are compiled for replication and lesson learned to improve future well's excellence. The evaluation of rig performance is conducted based on the focused KPIs criteria and Rig Scorecard criteria. Implementation of ADPM set clear and defined strategy from top management on digitalization and performance optimization. ADPM also helps foster performance optimization awareness and culture with clearly defined roles, responsibilities, and expectations. For example, the application deployment for the Field B drilling campaign focused on tripping, drilling and casing KPI improvement while utilizing ADPM for data gathering and analysis. The result of this deployment is commendable, with a total actual savings of 2.94 days gained throughout the campaign. From 2016 to 2021, PETRONAS has gained a total of 39.03 days of actual savings for their entire rig fleet.