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.

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.

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.

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.

Drilling rate of penetration (ROP) is a major contributor to drilling costs. ROP is influenced by many different controllable and uncontrollable factors that are difficult to distinguish with the naked eye. Thus, machine learning (ML) models such as neural networks (NN) have gained momentum in the drilling industry. Existing models were either field-based or tool-based, which impacted the accuracy outside of the trained field. This work aims to develop one generally applicable global ROP model, reducing the effort needed to re-develop models for every application. A drilling dataset was gathered from exploration and development wells in both onshore and offshore operations from a variety of fields and regions. The wells were curated to have different water depths, down hole drive such as Rotary Steerable System (RSS), PDM, Standard Rotary, bit types (Mill Tooth, TCI, PDC) and inclinations (vertical or deviated). A deep neural network was used for modelling the relationship between ROP and inputs taken from real-time surface data, such as Torque, Weight-on-Bit (WOB), rotary speed (RPM), flow and pressure measurements. The performance of the ROP model was analyzed using historical data via summary statistics such as Mean Absolute Percentage Error, as well as graphical results such as residuals distributions, cumulative distribution functions of errors, and plots of ROP vs depth for independent holdout testing wells not included in the model fitting process. Analysis was done both in aggregate, and for each specific well. The ROP model was demonstrated to generalize effectively in all cases, with only minor increases in error metrics for the holdout test wells, where the Mean Absolute Percentage Error averaged across wells was ~20%, compared to 17.5% averaged across training wells. Furthermore, residuals distributions were centered close to zero, indicating low systematic error. This work proves the case for a "global" ROP prediction model applicable "out-of-the-box" to a broad set of drilling operations. A global ROP model has the potential to eliminate learning curves, reducing time and costs associated with having to develop a new model for every field. Furthermore, a model that effectively captures the relationships between parameters controllable by drillers and ROP can be used for automatically identifying drilling parameters that improve ROP. Preliminary field-testing of the ROP optimization system yielded positive results, with many examples of increased ROP realized after following drilling parameter recommendations provided by the software.