Digital Twins for Oil and Gas Asset Decarbonization

Detailed overview of innovation with sample startups and prominent university research

What it is

Digital twins are virtual representations of physical oil & gas assets, encompassing wells, pipelines, refineries, and other infrastructure. These dynamic, data-driven models mirror the real-time behavior and performance of their physical counterparts, enabling engineers and operators to gain a deeper understanding of their assets, optimize operations, predict failures, and make better decisions to improve efficiency, safety, and sustainability.

Impact on climate action

Digital Twins for Oil and Gas Assets enhance climate action by optimizing operations, reducing emissions, and improving efficiency through real-time monitoring and predictive analytics. This innovation fosters smarter resource management, leading to lower carbon footprints, accelerated transition to renewable energy, and heightened environmental stewardship in the oil and gas sector.


Digital twins in the oil & gas sector are built upon a confluence of technologies:

  • Internet of Things (IoT): Sensors embedded in physical assets collect real-time data on various parameters, such as pressure, temperature, flow rates, vibrations, and emissions. This data is fed into the digital twin, providing a continuous stream of information about the asset’s condition and performance.
  • Cloud Computing: Cloud platforms provide the infrastructure and computing power needed to store, process, and analyze the vast amounts of data generated by IoT sensors and other sources.
  • Artificial Intelligence (AI) and Machine Learning (ML): AI and ML algorithms are used to analyze the data, identify patterns, predict failures, and optimize operational parameters.
  • 3D Visualization and Simulation: Digital twins are often visualized as 3D models, allowing engineers and operators to interact with the virtual representation of the asset and simulate different scenarios.
  • Data Integration and Interoperability: Integrating data from various sources, including sensors, historical records, and engineering models, creates a comprehensive and holistic digital twin that reflects the complexity of the physical asset.

TRL : 7-9 (depending on the specific application and complexity of the digital twin)

Prominent Innovation themes

  • Predictive Maintenance: Digital twins can predict equipment failures before they occur by analyzing sensor data and identifying patterns that indicate potential issues. This allows for proactive maintenance, reducing downtime and minimizing environmental impact from unplanned shutdowns.
  • Production Optimization: Digital twins can simulate different production scenarios and optimize operational parameters, such as flow rates, pressures, and temperatures, to maximize efficiency and reduce energy consumption.
  • Emissions Monitoring and Reduction: Digital twins can be used to monitor emissions from oil & gas operations, identify sources of emissions, and evaluate strategies for reducing environmental impact.
  • Safety Enhancement: Simulating different scenarios with the digital twin can help identify potential safety hazards and develop mitigation strategies, improving overall safety performance.
  • Remote Operations and Control: Digital twins enable remote monitoring and control of oil & gas assets, allowing operators to manage operations from centralized control centers, reducing the need for on-site personnel.

Sample Global Startups and Companies

  • Emerson Automation Solutions:
    • Technology Focus: Emerson Automation Solutions specializes in creating digital twin solutions tailored for the oil and gas industry. Their digital twins integrate real-time data from physical assets with advanced analytics and simulation models.
    • Uniqueness: Emerson’s digital twins are known for their accuracy and ability to provide predictive insights, helping oil and gas operators optimize operations, improve asset performance, and reduce downtime.
    • End-User Segments: Their solutions are primarily aimed at upstream, midstream, and downstream segments of the oil and gas industry, including exploration, production, refining, and distribution.
  • Aker Solutions:
    • Technology Focus: Aker Solutions focuses on developing digital twins that enhance the lifecycle management of oil and gas assets. Their solutions leverage data analytics, AI, and IoT to create virtual representations of physical assets.
    • Uniqueness: Aker Solutions emphasizes the integration of digital twins with their expertise in engineering and field operations, enabling comprehensive asset monitoring, predictive maintenance, and operational optimization.
    • End-User Segments: They cater to oil and gas companies across the globe, including operators and service providers involved in offshore and onshore operations, subsea systems, and energy transition projects.
  • Kongsberg Digital:
    • Technology Focus: Kongsberg Digital specializes in digital twin solutions that focus on operational efficiency and safety within the oil and gas sector. Their approach includes advanced simulation capabilities and real-time data integration.
    • Uniqueness: Kongsberg Digital stands out for its comprehensive platform that covers the entire asset lifecycle, from design and construction to operation and decommissioning, supported by digital twin technology.
    • End-User Segments: Their solutions are targeted at oil and gas operators, engineering firms, and asset owners looking to optimize production processes, improve decision-making, and ensure regulatory compliance.

Sample Research At Top-Tier Universities

  • Massachusetts Institute of Technology (MIT):
    • Technology Enhancements: MIT researchers are advancing digital twin technology specifically tailored for oil and gas assets. This includes integrating advanced sensors, IoT (Internet of Things) devices, and real-time data analytics to create highly accurate digital replicas of physical assets such as drilling rigs, pipelines, and refineries.
    • Uniqueness of Research: MIT’s approach focuses on not only creating digital twins for individual assets but also on developing interconnected digital ecosystems that simulate entire oil and gas operations. This holistic approach enables comprehensive monitoring, predictive maintenance, and optimization of energy efficiency and emissions reduction strategies.
    • End-use Applications: The digital twins developed at MIT have broad applications across the oil and gas industry, including optimizing production processes, improving safety protocols, and reducing carbon emissions. By simulating different scenarios and analyzing real-time data, operators can make informed decisions to enhance operational efficiency and sustainability.
  • Stanford University:
    • Technology Enhancements: Stanford researchers are pioneering digital twin technology to decarbonize the oil and gas sector by integrating advanced modeling techniques and machine learning algorithms. They focus on creating dynamic digital representations that evolve with real-time data inputs from multiple sources.
    • Uniqueness of Research: Stanford’s research emphasizes the integration of renewable energy sources and carbon capture technologies within digital twin simulations. This allows for the optimization of hybrid energy systems and the development of strategies to reduce greenhouse gas emissions throughout the oil and gas value chain.
    • End-use Applications: The digital twins developed at Stanford can be applied to optimize the operation of oil fields, enhance reservoir management, and facilitate the transition to low-carbon energy solutions. They enable operators to explore scenarios for integrating renewable energy, improving energy efficiency, and mitigating environmental impacts.
  • Imperial College London:
    • Technology Enhancements: Researchers at Imperial College London are advancing digital twin technology by incorporating advanced physics-based models and high-performance computing capabilities. They focus on creating virtual replicas of complex oil and gas assets that accurately simulate operational conditions and performance metrics.
    • Uniqueness of Research: Imperial’s approach includes the development of digital twins that support decision-making processes for decarbonization strategies in the oil and gas sector. This involves optimizing asset performance, reducing energy consumption, and integrating sustainable practices such as carbon capture and storage.
    • End-use Applications: The digital twins developed at Imperial College London are applicable to improving asset reliability, optimizing maintenance schedules, and reducing operational costs while minimizing environmental impact. They support the industry’s transition towards sustainable energy practices by providing insights into emissions reduction strategies and improving overall operational efficiency.

commercial_img Commercial Implementation

Digital twins are being implemented commercially in various applications within the oil & gas sector:

  • Predictive maintenance programs based on digital twins are being deployed to reduce downtime and improve asset reliability.
  • Digital twins are being used to optimize production processes, reduce energy consumption, and minimize environmental impact.
  • Companies are utilizing digital twins to enhance safety by simulating different scenarios and identifying potential hazards.