Smart RTOC Digital Twins: Transforming Oil & Gas Operations

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Introduction

In the paper we propose a framework for well construction full cycle support that integrates essential well construction stages: well design/pre-job phase, real-time operational support, and job performance analysis—into a unified digital platform. This integration ensures seamless interconnectivity among the components, which can be configured to specific customer requirements. Developed toolkit complies with International Association of Drilling Contractors (IADC) standards and addresses the needs of the oil and gas sector by incorporating a comprehensive data suite. The system is enhanced by artificial intelligence, which analyzes large volumes of data and identifies critical areas for improvement in well construction design and real-time decision-making. A key aspect of this strategy is the flexibility to either consolidate well design, real-time support, and performance analysis within a single platform or to integrate functionalities from various suppliers. This approach aims to effectively leverage accumulated analytical data, facilitating strategic planning for future operations with advanced AI systems, thereby optimizing decision-making processes.

Deployment

Primarily, it is crucial to establish a continuous, real-time data collection from drilling operations. Two viable options are available: 1. Deploying EDGE device (Gateway) on the drilling rigs. 2. Connecting to a WITSML or OPC server to ensure real-time access to realtime data remotely. Each option presents unique advantages and challenges. The advantages of EDGE devices include: 1. Enhanced capabilities for managing an Auto-driller. 2. Immediate alerts on technological deviations to rig site personnel. 3. Fully functional autonomous in case of network issues. The advantage of using a remote data server is a Simplified connectivity while the other side of the coin is potential scalability and data quality risks and internet connectivity dependencies. For maximizing benefits, it is recommended to utilize EDGE devices, which, ensure prompt delivery of decisions made in the RTOC to the rig site personnel and particularly to the driller.

The diagram on the Fig.1 illustrates the integration of physical components into a digital platform designed for comprehensive well construction management. It consists of four main elements: the Field, Data Center – Cloud, Remote Monitoring, and Remote Control Center. In the field, the EDGE device collects real-time sensor data, performs digital twin analyses, visualize and set operational objectives to personnel or directly to PLCs. All gathered data undergoes quality control checks and then transmitted to the Data Center. The Data Center serves as a central hub, processing this data from all drilling rigs and performing advanced multiwell analytics to provide actionable results. Remote Monitoring allows clients to oversee rig operations. The Remote Control Center enables engineers and managers to make informed decisions and remotely control drilling parameters based on actual data. Data is transferring continuously from the drilling rigs to the Data Center and back, forming a feedback loop that ensures continuous optimization of drilling operations. This integration facilitates real-time monitoring, operational support, and performance analysis, leveraging advanced analytics and AI to improve decision-making and operational efficiency.

Framework description

The functional diagram the proposed framework is shown on the Figure 2.

The diagram illustrates comprehensive system for managing well construction, integrating various components such as well design, real-time monitoring, real-time operations, job performance analysis, and AI systems. In the well design phase, static well data is collected and processed to create detailed drilling plans and geological hazard roadmaps based on AI analytics data results. Real-time monitoring involves the use of sensors and digital twins to collect and analyze drilling data, supported by tools like smart alarms, real time anticollision analysis, automated broomstick plots, etc. Real-time operations are managed through automated driller systems and operational roadmap. Job performance analysis utilizes advanced data analytics for gathering important drilling data, calculate key performance indicators and detect non-productive time automatically. Analytics AI-based data acquisition and processing systems analyze large volumes of data and identify critical areas for improvement in well construction design and real-time decision-making.

Well Design

The outcome of this module is a comprehensive well design. A mandatory requirement for this module is the ability to historical drilling data to optimize the design. The module consists of the following components:

1.Well data and planning calculations as a result.
2. Autogenerated digital drilling program – a roadmap for drilling operations performance.
3. Depth vs Day schedule and Potential geological hazards roadmap.

1. Well Data. The primary output of this module is the design of well using accumulated historical drilling data. This includes well section plan, well trajectory, anticollision analysis, mechanics/hydraulics calculations, selection of drill string and BHA components, BHA vibration analysis, and selection of drilling fluids properties.

2. The autogenerated digital drilling program is Planning Drilling Operation Roadmap that automatically assesses all technological limitations based on the initial data determined at step 1 in the Static well data acquisition/calculation system. Then the optimal operational parameters define for ROP maximizations considering potential geological hazards. The resulting operational parameters are compared with the optimal operational parameters identified through the analysis of accumulated historical data using advanced AI analytics. Importantly, it provides recommendations for changes to input data such as BHA, fluids, trajectory, sections, etc. A key feature of the autogenerated digital program is the ability to provide feedback message regarding the calculated initial data. These modules are fully integrated and complement each other. A very important result of AI analytics is the potential geological hazards roadmap, which is used to highlight specific intervals in the Drilling Operation Roadmap and operational parameters clarifications within them.

3. Depth vs Day Schedule. This module utilizes the results of AI analytics and statistics about best practices in drilling, tripping and rig operations. The key to gather analytical information is the accurate determination of the start and end times of each nonmetered operation. Gathering can be performed in a fully automated mode using rig sensors data and video analytics: recognition of data from cameras installed at the drilling site or the second method: automated mode using rig sensors data with manual validation of the start and end times of each operation. The second method—automated with manual validation—is used in the absence of necessary cameras on the drilling rig or in case of difficulties arise using surveillance cameras in harsh climatic conditions.

Real-time operational support

This module provides extensive operational support for drilling, encompassing two critical aspects: 1. Real-time monitoring, and 2. Real-time operations. The real-time monitoring component incorporates a suite of tools deployed at the Real-time Operations Control Centers (RTOC). These tools facilitate essential calculations and analytics during the drilling process. The primary benefit of employing these tools is equipping the drilling engineers at the RTOC with comprehensive analytics crucial for informed decision-making and the effective management of real-time operations, that Real-time operations module represents. A foundational element of this framework is the integration with the digital well-design, which was created during the initial stage, ensuring that operations are aligned with the planned specifications and enhancing overall operational efficiency.

Applied RTOC tools:

1. Real-time Digital Twin: Executes real-time multifaced analysis being feeded a live instrumentation data to evaluate current state and lookahead.

2. Smart Alarm System: Evaluates operational parameters regimes deviations from the Digital Twin’s calculations. Customizable calculations are available.

3. Drilling Dysfunction Evaluation/Trend Analysis: Analyzes patterns and potential issues while drilling operations.

4. Sensor Data Quality Control: Ensures the sensor data reliability and accuracy control.

5. Real-time Anticollision: Evaluate the collision risks with neighboring wellbores.

6. Automatic Broomsticks (Friction Factors Trend Control)

7. Automated Slide Sheet

8. Virtual Sensors

9. Downhole Mechanical Specific Energy (MSE) real time calculations

10. Real-time Operational Roadmap/Optimization of Rate of Penetration (ROP): Guides well construction operations including rotary drilling, slide drilling, reaming, back reaming, tripping in/out in automated mode to optimize efficiency and effectiveness. The ecosystem toolkit can be customizable to meet the specific needs of each client and is not confined to the components listed above. This toolkit is actively utilized by our clients to enhance operational efficiency and safety in drilling operations.

1. Real-time Digital Twin technology implements mathematical models that describe the physics of downhole processes and evaluates the well construction process in real time, every second. The Digital Twin leverages data and physics insights, considering coupled well hydraulics, geomechanics, well thermodynamics, work string mechanics & drill bit rock interaction effects. This dynamic twin predicts expected values and evaluates the accuracy of current sensors’ readings in real time. The methodology becomes an effective tool to assess sensor data quality online, and it allows timely service instrumentation to resolve raised sensor issues, filter outliers, and switch to alternative sensor readings when available (Karpov et al., 2021).
2. Smart Alarm System: In conjunction with the Dynamic Digital Twin, the smart alarm system operates with dynamically determined threshold values based on Digital Twin calculations as well as manually defined static thresholds. When deviations occur, the system triggers alerts and records events in the database. The alarm system can be customized for any operational parameters, which are categorized into operational, wellbore quality and integrity, work string, and surface equipment:

• Operational thresholds – limits characterizing violations of operational standard procedures, drilling plan roadmap, regulations, and established best practices (e.g., hook load, standpipe pressure, surface torque, running speed tripping in/out, flow rate, RPM, inactive time in open hole, weight on bit, pressure drop on mud motor, etc.).
• Wellbore thresholds – limits violation of which exposes a risk to wellbore quality and integrity (e.g., ECD, friction factors during rotation, tripping in/out, hole cleaning index, pits volume, etc.).
• Work string thresholds – limits the violation of which compromise the integrity of the drilling string and BHA components.
• Surface equipment thresholds – limits identified by safety margins and specifications of surface equipment.

Additionally, users can create their own thresholds using real-time mathematical calculations with any value available in the system, which provides full customization.

The outcomes of the Smart Alarm System include:

• Instant notifications for rig personnel via the drillers’ HMI, messenger or email

• Statistics on deviations, which are collected in an Analytics Data AI Acquisition System.

3. Drilling dysfunction module evaluates in real time risks such as cleanout, twist-off, drill string failure, influx, mud loss, washout, and helical buckling risks. Statistics on these risks are also gathered in the Analytics Data AI Acquisition System.

4. Sensor’s Data Quality Control minimizes the amount of incorrect data for real-time system operation subsequent analysis, achieved through the following means:

• Determination of invalid sensor data (absence of value/signal, high/low/static values), ability to select a valid data source in an automatic regime.
• Data QC against alternative data sources.
• Data QC against coupled hydromechanical model – Real time digital twin.

5. Real-Time Anticollision Tool. This system tool is pulling directly from MLWD unit and calculates collision risks of with neighbor wells. The modules features:

• Alerts and Notifications: Provides real-time alerts in case of collision risk.
• 3D Visualization: Displays high well collision risk intervals for enhanced spatial understanding.
• Ellipse of Uncertainty Flexible Settings: Allows adjustment of uncertainty parameters to improve risk assessments.
• Anticollision Analysis Report: Summarizes findings and recommendations for avoiding collisions.

6. Automated T&D Broomstick plots. This tool calculates and visualizes hook load, torque, and standpipe pressure depth plots, based on automatically determined operations for drilling, reaming, and tripping activities. T&D broomstick utilization provides a detailed review of friction factors trends in real time. Friction factors trend analysis and the key drilling parameters rates of change analysis allow tracking and preventing critical drill string overloads, predicting downhole hazards, and evaluating the effectiveness of washing and reaming operations in each wellbore interval.

7. Automated Slide Sheet. This tool aggregates drilling modes data, distinguishing between slide and rotary drilling activities. It visualizes MWD surveys and calculated friction factors by intervals, reducing manual inputs required from the DD (Directional Driller) engineer during well construction. The system automatically creates a table displaying drilled intervals and includes average drilling metrics such as RPM, ROP, flow rate, pressure off bottom, torque, hook load (up, down, rotation), and survey including MD, TVD, inclination, azimuth, and DLS. Slide sheet log is available in Excel report format.

8. Virtual Sensors. Mathematical modeling of well construction operations has proven to be a reliable tool being able to replace a need for a physical sensor with a virtual-soft sensor (Gumus et al., 2014), (van der Geest et al., 2001). The soft sensor approach has been successfully used to enhance the accuracy of derived drilling parameters (Kyllingstad et al., 2019), (Tikhonov et al., 2014) and to determining abnormal readings measured by sensors (Cayeux et al., 2014). The system calculates in real time the following parameters: effective WOB, TOB, bit RPM, ECD on bottom, bit and casing shoe.

9. Mechanical Specific Energy (MSE) calculations profoundly improve real-time decision-making and troubleshooting throughout drilling processes. Suggested methodology integrates downhole MSE, made possible by Virtual sensors through a Digital Twin. Downhole MSE typically yields more precise data regarding the actual conditions encountered downhole, thereby enhancing the accuracy and effectiveness of drilling operations:

• Equipment Failure Prevention: By monitoring MSE in real-time, the platform can detect abnormal spikes that might indicate impending failures in drill bits or other downhole equipment. This proactive approach allows for timely interventions, preventing costly downtime and enhancing safety. Implementing safety MSE limits in Smart Alarm system allows to control MSE automatically.
• Enhanced Wellbore Stability: MSE data provides insights into the strength of the formation, aiding in decisions that enhance wellbore stability. For example, a low MSE value might indicate low formation strength, which can guide adjustments in drilling parameters and additional reaming to prevent wellbore collapse.
• Bit Selection and BHA Design: The integration of MSE with a well-placement methodology includes optimized bottom hole assembly (BHA) design and bit selection based on historical drilling data. This optimization aims to maximize drilling efficiency and accuracy.
• Real-Time Geosteering: Using MSE along with downhole drilling dynamics data from a drilling optimization collar allows for precise real-time geosteering. This system helps maintain the planned trajectory within a thin target layer while minimizing borehole tortuosity and accurately navigating geological challenges.
• Stratigraphic Correlation and Porosity Boundaries: MSE data correlates well with real-time logging-while-drilling data, aiding in quick decision-making regarding stratigraphic shifts or approaching different porosity boundaries. This capability helps reduce undesired borehole azimuth swings and ensures the drill path stays on target.

10. Real-time Operational Roadmap/Optimization of Rate of Penetration (ROP) The Real-time operational roadmap facilitates the selection of optimal drilling parameters, considering downhole and surface equipment specifications as well as rock formation properties. This methodology allows for the precise configuration of drilling parameters such as the Rate of Penetration (ROP) or Weight on Bit (WOB), and the computation of expected surface torque, Torque on Bit (TOB), bit RPM, mud motor pressure drop, minimum required flow rate, maximum allowable surface torque, and the maximum WOB allowable before helical buckling occurs.

This Real-time drilling roadmap also ensures that the selected operational parameters are continuously validated against the equipment capabilities and the geological conditions encountered. It alerts users immediately if any parameters or specifications are breached, thus maintaining drilling integrity and efficiency. Additionally, the operational roadmap segments parameter selection by rock formation intervals or stand length, optimizing operations based on the specific geological context.

The real-time operational drilling roadmap can be integrated with the auto-driller system PLC, ensuring seamless rig activities automation performance.

Real-time drilling roadmap integration with rig site auto-driller units.

During the operational phase, the execution of the drilling roadmap is critical. Integration with auto-driller PLC enables the seamless transfer of setpoint values derived from the roadmap. The ecosystem leverages a dynamic digital twin and advanced analytical tools to ensure continuous monitoring and adjustment of the operational regimes based on real-time surface sensor data and MLWD unit.

The system performs real-time recalculations of the drilling roadmap based on actual drilling data, continuously optimizing the drilling parameters to align with the evaluated in real-time boundaries and achieving the best possible drilling performance. If deviations from the planned trajectory or geological inconsistencies are detected, the system dynamically adjusts the drilling plan, maintaining alignment with geological targets and operational efficiency.

Throughout drilling, the system plans the slide and rotary intervals required to adhere to the trajectory, adjusting the plan based on new MWD data to ensure compliance with geological targets. The third party PLCs maintains drilling parameters according to the updated by the digital ecosystem setpoints, with the digital twin overseeing the adherence to these parameters in real-time.

In the case of drilling parameter deviations or changes in drilling conditions, the integrated smart alarm system alerts the drilling personnel, ensuring immediate corrective actions. The system also manages the weight on the bit during slide drilling operations and activates oscillation modes as needed, optimizing drill tool rotation angles and speeds.

Upon completion of drilling operation, the system commands the initiation of reaming operations, adjusting setpoints based on the wellbore’s current state, and incorporating safety margins for tensions and compressions calculated by the digital twin.

This comprehensive control and monitoring ensure that each section of the drilling operation is executed with precision, enhancing overall operational safety and efficiency while adhering to the designed drilling plan. The drilling log is automatically generated, documenting all drilling operations and wellbore conditions, providing a detailed account of the operation’s progression and state.

Job performance analysis
The “Job Performance Analysis” block integrates data from the Applied RTOC tools (see the module Real Time operational support) and the Drilling Data Acquisition System (sensors, MLWD), generating several analytical outcomes:

1. Statistics of Dysfunctions/Violations/Hazards by Depth.
2. Master Log (Drilling Parameters by Depth).
3. KPI’s (Key Performance Indicators).
4. Day vs Depth Plan/Actual Operational Data.
5. Automatic Detection of VTN/NPT

1. Statistics of Dysfunctions/Violations/Hazards by Depth.

Generated from the Smart Alarm System, these statistics include deviations which are aggregated in an Analytics Data AI Acquisition System.

Each deviation is categorized with attributes including: parameter, units, limit value, sensor/reference value, start/end time/duration, and category (Operational/Wellbore/Work string/Surface equipment thresholds), description.

This data forms the basis for a database of dysfunctions, violations, and hazards, segmented by depth, serving as critical input for analytics in AI-driven well design optimization.

Visual representation of this database is depicted in Figure 3.

2. Master Log (Drilling Parameters by Depth).

This is a comprehensive database of drilling parameters indexed by depth, incorporating MLWD data and a detected complications database.

The log includes drilling parameters like ROP/WOB, RPM/Torque, Flow rate/SPP, along with logs such as Gamma log/Gas, Sludge diagram, and Caliper log, enriched with survey parameters like inclination, azimuth, and dog leg severity (DLS), drilling complications.

3. Key Performance Indicators (KPIs).

Automatically identified for both drilling and tripping operations:

Key performance indicators (KPIs), for drilling and tripping operations are automatically identified:

• Pumps on time. Rotation on time. At surface time.

• Drilling KPI’s: distance drilled, average ROP by section/time interval. Effective ROP (IADC). ROP Rotary, ROP Slide. Distance drilled, Rotary drilling distance, Slide drilling distance, rotary/sliding ratio.

• Tripping KPI’s: Average trip speed running in/pulling out, open hole/cased hole, Average stand per hour running in/ pulling out, stand quantity open hole/cased hole,

• Drilling connection KPI’s averages: Weight to Slip, slip to slip, Weight to Weight.

• Tripping connection KPI’s averages: Slip to slip.

• Drilling parameters fact vs plan per stand. ROP, WOB, SPP, flow rate, RPM, torque, hook load, pressure drop on mud motor.

• Crew KPI’s: Phase (drilling, tripping, rig activities, other) per shift. Drilling KPI’s per shift: Penetration, ROP, ROP in rotary/slide, rotary/slide ration. Drilling connection KPI’s per shift: Weight to Slip, in slip, Weight to Weight. Tripping connection KPI’s per shift: avg speed trip in/out, for open/cased hole; avg stand quantity trip in/out, in slips time, run in/out time, avg in slips time, avg run in/out time.

• Drilling KPI’s multi-well comparison with well filtering and section selection: Distance drilled, Avg ROP in rotary/slide, effective ROP, circulation time, average connection time, NPT.

• Slide sheet log: Average drilling parameters: RPM, ROP, flow rate, pressure off bottom, torque, hook load (up, down, rotation), and survey including MD, TVD, inclination, azimuth, and DLS. Friction factors (up, down, rotation). Slide sheet log is available in Excel report format.

4. Day vs Depth Plan/Actual Operational Data. This component involves a Day vs Depth graph, illustrating the timeline of drilling phases, the time spent on various activities, and their percentage breakdown.

5. Automatic Detection of VTN/NPT (Violation Time Norms/Non-Productive Time).

The system automatically detects Violation Time Norms (VTN) during drilling/tripping connections, facilitating real-time adjustment and operational efficiency. The database for these detections is presented in a table view, highlighting the efficiency of operations and pinpointing areas for improvement.

This comprehensive approach to job performance analysis leverages modern data acquisition and analytics tools to provide a thorough examination of drilling operations, offering actionable insights and facilitating continual operational improvements

Effects

The RTOC empowered by the described digital platform since 2021, has continuously shown improvement and robustness across the fleet of the rigs been deployed. The effects expressed in monetary averaged terms per single drilling results in 300 thousand EUR total annual savings per drilling unit annually.

It is worth noting that these achievements up till second half 2024 do not yet consider the real-time drilling map with the automated drilling digital feature system, that will boost potential benefits achievable by described digital framework.

The proposed approach offers robust capabilities for managing the construction multiple wells simultaneously and demonstrates substantial scalability potential. By integrating real-time digital twins and advanced analytics, this method enables precise control and optimization across various drilling operations, ensuring efficiency and responsiveness to dynamic conditions. Its scalable framework supports seamless expansion, allowing for the effective management of increased drilling projects without compromising performance or safety. This approach enhances operational flexibility and efficiency, making it well-suited for large-scale and complex drilling environments.

Conclusion

This article delineates the comprehensive integration and operational advancements within the Smart RTOC platform, tailored for the oil and gas industry’s well construction and performance analysis. Combining critical stages—well design, real-time operational support, and job performance analysis—into a cohesive digital ecosystem, the platform revolutionizes traditional drilling operations, enhancing efficiency and reducing non-productive time.

The platform’s capacity to leverage EDGE devices and/or remote data lakes, facilitates a robust, real-time data collection and analysis framework. This integration enables proactive operational adjustments and strategic decision-making based on dynamic field data and sophisticated AI analytics. Such capabilities ensure that operations are not only compliant with the international drilling standards but also tailored to specific customer needs, promoting optimal drilling efficacy and safety.

Further, the utilization of tools like the Real-time Digital Twin, Smart Alarm Systems, and Real-time Anticollision modules exemplifies the platform’s advanced technological integration. These tools provide critical, real-time insights and automated controls that significantly enhance the decision-making processes at the Real-time Operations Control Centers (RTOC). The benefits of these integrations are quantitatively significant, as evidenced by the operational savings and efficiencies observed in by digital ecosystem user, report substantial economic benefits achieved.

In conclusion, the Smart RTOC platform sets a new benchmark for operational excellence in the oil and gas industry. It not only addresses the immediate challenges of well construction and performance analytics but also sets the stage for future advancements by incorporating continuous learning and adaptation mechanisms. The platform offers a promising avenue for achieving heightened operational efficiency and economic returns, critical in the competitive energy market landscape.

This strategic insight aligns with the industry’s move towards digital transformation, offering a roadmap for other companies to enhance operational efficiencies and achieve significant cost savings through technological innovation and integration.

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