Navigating Business Transformation with Digital Twins in Oil and Gas — Strategic Alignment to Implement Advanced Technologies
/By: Kelly Watt, Co-Founder of Digital Twin Consulting
The previous article in this series from digital twin implementation expert Kelly Watt of Digital Twin Consulting explained the critical impact strategic master planning, use case development, and tactical project roadmaps have on effective digital twins for oil and gas enterprises. This article evaluates how this strategic approach drives the effective selection, implementation, and utility of advanced digital twin technologies.
Effective Technology Implementation Begins with Enterprise Strategic Alignment
Strategically, it is valuable for oil and gas enterprises to align digital transformation projects with enterprise planning and construction divisions, as well as operations, maintenance, and emergency preparedness. Each major business value stream is accompanied by a significant quantity of data, infrastructure, teams, processes, and business impacts.
With this level of complexity, change management processes are just as critical as the technologies you select to ensure the digital twin remains accurate and current during regular business operations. You must have trust in the operational data as critical oil and gas business decisions depend upon current and accurate information.
This is especially true for building information modeling (BIM), reality capture data, data schemas, construction handover documentation, industrial internet of things (IIoT), programming schemas, communication protocol standards, and requirements for monitoring and controls, which are vital for the digital twin to be effective.
Too often, a digital twin project is started with the assumption that connectivity, communication, and integration capabilities exist with the current infrastructure. However, starting a project under these assumptions often uncovers that infrastructure improvements are required before a use case can be fully realized. This significantly impacts the project scope, schedule, and outcomes, potentially undermining the entire program.
It is also essential to align the project expectations for “industrial controls” given the data security requirements, platform limitations, and existing control sequences and rules.
Likewise, the anticipated digital twin users must be clear on the business process change, including any workload increases, technical skills required, and expectations.
If the digital twin is producing predictive insights, who will respond to them, and are they qualified to act?
If there is an anticipation of automation or control, what are the expected changes to the sequence of operations and rules, and how do they work in the current environment?
There is a lot to consider here.
Critical to project success is establishing the limitations of the current infrastructure, data, and business processes to meet the desired outcomes. Clear communication with business stakeholders about what the digital twin is expected to perform, what information will be surfaced to whom, for what reason, who would be responsible for acting on the information, and when can we fundamentally determine the success of a project.
Once strategic alignment is reached on the desired outcomes, selecting the appropriate visualization and automation technologies becomes critical to the success of the digital twin.
Reality Capture Technologies
Technologies such as mobile LiDAR and drones are pivotal for constructing large-scale and detailed models that can be foundational for digital twins. Terrestrial LiDAR remains preferable for fabrication work and accurate 3D modeling, where metrology-grade data collection is used for sub-millimeter verification and validation processes.
Reality Capture, in general, facilitates the accurate mapping and modeling of physical assets, lays a robust foundation for the digital twin's architecture, and complements geospatial data and BIM. The utilization of reality capture for integrity inspections and AI/ML workflows to automatically find failures and anomalies is increasing daily. This includes OIG inspections to mitigate non-visible spectrums detecting methane emissions.
Available tools that automatically segment point clouds into object elements present a new opportunity to spatially associate data without a BIM. The world of GIS has been capitalizing on this technique for years to identify topology, roadworks, pavement lines, utilities, and much more. The value of using reality capture to associate a spatial location with information (static, integrity, work management systems, and live data) removes the need to construct a design model such as a BIM. The LiDAR or mesh containerizes elements (like topography, assets, utilities, and components: pipe, or a pump) in robust databases.
Reality capture is a forensic copy at a snapshot in time, not a continuous feed or live data source. However, continuous scanning can be trended to monitor and track change over time, such as cracks, deviations, or more significant infrastructure movement. With the evolution of fixed LiDAR sensors, it is now possible to continuously scan and process in real-time, similar to surveillance cameras, for various use cases and effective decision-making.
Building Information Modeling (BIM)
BIM is a cornerstone within the digital twin ecosystem, providing detailed 3D models that integrate seamlessly with digital twins, but not to be confused as a digital twin on its own. This provides augmented visualization, simulation, and analysis capabilities. The BIM contains layers of information and associated attribution, where users can quickly search and find what they are looking for visually. The user can virtually navigate the BIM to track systems that do not have a line of sight in the real world because they are remote, underground, in ceilings, behind walls, or inside larger assets.
The advancement of AR technology is quickly aligning the real world with the digital world, including reality capture and BIM models. The ability to perform clash detection during construction or fabrication is extremely helpful for making quick and accurate decisions. And is incredibly helpful during brownfield upgrades and modifications of existing infrastructure.
Gaming Platforms (Unity, Unreal Engine, Omniverse, Others)
Depending on the data requirements, size, level of detail, and dynamic features needed for the digital twin use case, a gaming platform interface may be more valuable to the user than a BIM or reality capture. In very large environments, gaming platforms are preferable due to their ability to render large amounts of data. Gaming platforms also incorporate animations and simulations such as fluid dynamics, object motion, stress testing, and energy modeling. Introducing dynamic data to the digital twin can be done with smaller BIM component models, such as a motor, but a gaming platform may be a better fit for large-scale and environmental animations. Gaming platforms are also ideal for supporting advanced applications like virtual reality (VR) and training use cases.
AI and Industrial Automation
The integration of artificial intelligence (AI), machine learning (ML), large language models, and industrial automation into digital twins revolutionizes traditional strategies and decision-making processes. These technologies provide opportunities to automate tasks, dissect vast datasets, and amplify decision-making accuracy, thus enhancing the digital twin's functionality.
An effective AI and automation strategy relies heavily on data quality and data availability. This is why the initial stages of the process are so critical to assess before a roadmap is created or an initial deployment commitment is made.
Likewise, transitioning to sustainable operations poses significant challenges, requiring technological upgrades and a cultural shift within organizations. Quality data is required to calculate energy and utility utilization, simulate optimal schedules, reduce costs, reduce carbon and fuel burn, and improve the quality of outputs. Data may not be available, sensors or meters may need to be installed, and infrastructure to transport or translate data may be required to perform effective analysis.
AI and automation also enable the simulation of event scenarios so mitigation efforts can react effectively. Leveraging real operational data to game out various situations enables effective strategic planning, efficiency, training, and response preparedness:
Simulate design concepts before deploying in the real world
Predict the impact of new construction or demolition projects on business operations
Model emergency events such as containment breaches to gain an immediate understanding of flow can prevent a broader impact on water, ground, wildlife, or airborne hazards
Simulate hot, cold, or severe weather events that pose a risk to existing infrastructure, such as hazardous releases, explosions, or interruptions to normal operations
Prepare for disruptions to supply chain logistics that may also have cascading impacts during events
Modeling these predictable outcomes supports operational planning capabilities to react to downstream effects. This can be very valuable to real-time decision processes, ongoing maintenance initiatives, emergency response teams, enterprise planning projects, and construction divisions.
Conclusion
Digital twins represent a paradigm shift for the oil and gas industry. They provide a strategic framework that integrates data, intelligence, and business processes to tackle complex challenges. By adopting digital twins powered by BIM, reality capture data, AI, ML, automation, and real-time IIoT, industry leaders can transform operational hurdles into competitive advantages which can be realized with high-level coordination with business goals, strategic thinking, change management, and effective technology selection.
About the Author
Kelly Watt is an accomplished Digital Twin Consultant and the pioneering co-founder of Digital Twin Consulting. He is known for developing the "Digital Twin Assessment Process (DTAP)" and providing reality capture and technology services. His work primarily concentrates on enhancing 3D visualization and advancing digital transformation, effectively bridging the divide between visionary leadership and technological firms. Through strategic planning, master planning, detailed technical project roadmaps, and collaborative program management, Kelly and his dedicated team facilitate the successful implementation of digital twin technologies.
Presently, Kelly oversees sophisticated digital twin initiatives at DFW Airport and AIFA Airport. He has vast expertise across various sectors such as oil and gas, energy, manufacturing, government, transportation, and aviation. Recognized as a thought leader and a regular contributor to the digital twin and 3D community, he often shares his insights as a speaker at industry events. His specializations include 3D lidar, photogrammetry, video imaging, and analysis technologies, and he is certified as a level 1 thermographer with skills in spectral imaging, condition-based asset monitoring, and reliability centered maintenance.
Kelly Watt serves as an Ambassador for the Digital Twin Consortium in the Mobility and Transportation sectors while supporting the oil and gas and resource sectors. This role further highlights his commitment to driving forward the application and development of digital twin technologies within these critical areas of global infrastructure.
Kelly is also deeply involved in developing digital twin programs using knowledge graphs and semantic models, providing a robust foundation for these initiatives. Committed to the dissemination of knowledge, he has created a comprehensive digital twin course aimed at educating the industry workforce on the core principles, components, and techniques of digital twin technology. This course is remotely offered at a leading Canadian engineering college, further extending his impact on the field.
