How Digital Twins in the Oil and Gas Industry Optimize Your Operations With Predictive Analytics 

Let’s say you’re an engineer overseeing operations in a network of gas pipelines. The day has been uneventful and calm… until an alarm disrupts it. There’s a sudden drop of pressure in one of the pipelines.  

Is it a leak? 

A malfunction? 

A surge in demand? 

Whatever it is, now it’s up to you to manage the logistics, send a team (or yourself) to check, and safety protocols, because they always come first, all while you diagnose the problem.  

Just another day in the life, right? 

Except that, this isn’t your typical troubleshooting scenario anymore. 

Nowadays thanks to digital twins in the oil and gas industry, you can monitor, analyze, and come up with a solution remotely and from the comfort of the control room. And sure, you can’t physically patch the leak, but now you know how to fix it without rushing there.  

In this article, you’ll learn the basics of digital twins and examples of how they’re used in the oil and gas industry not just by fixing issues, but by predicting them before they even happen. 

What Is a Digital Twin?

A digital twin is a digital version of a physical object, system, or model that represents it in near real-time and creates predictions of how that object or model will be affected by certain conditions.  

A digital twin takes data from multiple sources to keep its real-time status on the real-world object. Think: a computer program that takes real-world data about the object (in real-time) and creates predictions for different scenarios. 

Why would you need digital twins in oil and gas?

Digital twins in oil and gas allow you to test and predict outcomes in a new process or asset. Imagine you have an idea to optimize a process or get better results that, in theory, would work. But if it doesn’t, it could lead to money and time lost.  

Naturally, you don’t want to fail, so the best approach is testing. Something you can do with digital twins without the commitment of losing resources—other than a couple of hours. 

Are digital twins different from simulations?

Yes, digital twins are real-time copies of physical things in the real world. They’re updated with data from sensors at all times. Simulations are more visual. They mimic how things could behave in different situations but don’t always use data in real-time. 

Types of Digital Twins

Digital twins range from simple components of an asset to models of an entire system. For example, there’s a project for a digital twin of the Earth from the European Union called Destination Earth to predict weather conditions. 

You can distinguish the types of digital twins based on their scale of application, complexity, and use cases.  

Here are each of these aspects for digital twin types. 

Component Twins

• Scale of application: Individual components or parts of the machine.
• Complexity: Granular as you would collect data from specific behaviors in the components.
• Use cases: You can use component twins for the design or maintenance of a specific part.

Asset Twins

• Scale of application: The entire machine or equipment—which consists of multiple components.
• Complexity: Intermediate granularity by collecting data on the performance and health of the asset.
• Use cases: You can use asset twins for predictive maintenance, downtime reduction, and the lifecycle management of the equipment.

Unit Twins

• Scale of application: A unit or section of a facility. For example, a manufacturing line.
• Complexity: A broader scope as it integrates multiple assets and their interactions within the unit.
• Use cases: You can use unit twins to optimize the throughput of an entire section, train personnel, or make other unit-specific operations more effective.

Process Twins

• Scale of application: An entire process that could cover multiple units or facilities.
• Complexity: Extensive as it focuses on end-to-end process optimization.
• Use cases: You can use process twins to get insights on strategic planning, optimization of a process, and the overall efficiency of a process.

Advantages and Disadvantages of Digital Twins in Oil and Gas

Advantages of digital twins:

• Predictive maintenance: Maintenance efforts have evolved from reactive to preventive and now to predictive. Rather than waiting for an anomaly to happen or doing scheduled maintenance and hoping for the best, with digital twins, you can predict when a malfunction will happen based on data. For a real-world application of predictive maintenance, read our case study on how CSE ICON helped a midstream oil and gas company reduce costs and increase operational awareness through event prevention.
• Real-time monitoring: Over 19% of the market share of digital twins is focused on asset monitoring and maintenance. By analyzing real-time and historical data, operators and engineers gain insights into operational trends, performance metrics, and potential opportunities for optimization.
• Remote monitoring and control: The digital nature of a twin means engineers and operators don’t need to be onsite to solve most of the operational problems that occur. This provides more flexibility as in some cases you can intervene, remotely, to fix the problem.
• Cost reduction: Adopting preventive maintenance using digital twins can reduce maintenance costs by 15%. By simulating different operational scenarios with digital twins, personnel can identify opportunities to save on costs. Moreover, digital twins reduce unnecessary expenditures on maintenance.
• Increased safety and risk mitigation: Testing multiple scenarios means learning about potential hazards and ways to avoid them—without the real-world consequences. Digital twins allow optimization of safety protocols in instances such as equipment failures or emergency shutdowns.

Disadvantages of digital twins:

• Complex data integration: Up to 60% of oil and gas companies use legacy systems with data in different formats and with multiple protocols. This makes integrating data into a cohesive format, that can be used for digital twins, time-consuming and costly.
• Dependency on reliable data: Digital twins rely on quality data to come up with accurate simulations. Otherwise, results can be suboptimal or even wrong. The data source from where digital twins learn must be reliable.
• High (initial) investment: Digital twins aren’t just about simulations on software, they require a significant investment in your infrastructure adding hardware and software alike while thinking about your data integration capabilities. Not to mention software licenses, data storage, and cybersecurity best practices.
• Talent shortage: A talent with a combination of modeling, simulation, data science, software development, and domain-specific knowledge may be difficult to come by.
• Compliance and legal issues: Ensuring compliance with regulations and industry standards is always a consideration when implementing new technologies. For instance, when it comes to intellectual property, companies developing an algorithm to optimize their operations using digital twins may file for patents.

Real-World Digital Twin Examples in Oil and Gas

MathWorks, a company that specializes in mathematical software, uses MATLAB and Simulink for data analysis and simulation to create digital twins.

Here are some of their customers’ use cases using digital twins:

Magotteaux maximizes copper cell flotation recovery in real-time

Magotteaux is a global leader in process optimization solutions for high-abrasion extracting industries focusing on improving the efficiency of industrial processes. They specialize in the design and production of cast wear parts for industries like mining and cement.

Digital twin application: By creating a digital replica of the flotation cells and their operating conditions, Magotteaux predicted how changes would affect copper recovery. This process involved constant data feedback from the physical to the digital twin, enabling real-time optimizations and adjustments.

Tata Steel optimizes operations to save 40% of energy on cooling towers

Tata Steel, part of the global conglomerate Tata Group, is among the top steel-producing companies worldwide. They are known for their commitment to sustainability and efficiency in their operations.

Through the adoption of digital twins, Tata Steel achieved 40% energy savings on their cooling towers. The technology provided insights into operational efficiencies and identified areas for improvement.

Digital twin application: The digital twin for the cooling towers integrated real-time data on water temperature, flow rates, and energy consumption. This enabled predictive analytics for maintenance and operational optimization, leading to significant energy savings.

BuildingIQ provides predictive energy optimization for buildings

BuildingIQ is a tech company that offers solutions for predictive energy optimization in building management. Their services aim to reduce energy consumption and improve overall building operations.

With the use of digital twins, BuildingIQ does predictive control and optimization of heating, ventilation, and air conditioning (HVAC) systems in real-time. This results in significant energy and cost savings.

Digital twin application: The digital twin technology utilized by BuildingIQ incorporates real-time data from the building’s HVAC systems, external weather conditions, and occupancy patterns. This data is then analyzed to predict and adjust the building’s energy needs, ensuring optimal efficiency.

NIO improves battery state-of-health for electric vehicle fleet

NIO, an innovative automotive company specializing in electric vehicles (EVs), focuses on creating a more pleasant and environmentally friendly driving experience. Their focus is EV technology and infrastructure development.

NIO used digital twins to monitor and improve the state of health of batteries across their EV fleet.

Digital twin application: NIO’s digital twins collect and analyze data from each vehicle’s battery pack in real time, including temperature, voltage, and current. This data helps predict battery health and optimize charging strategies to extend battery life and improve efficiency.

Renoir Consulting predicts the real-time performance of drilling rigs

Renoir Consulting, a global management consultancy firm, focuses on operational improvements across various sectors, including oil and gas. They specialize in implementing strategic changes that drive performance and efficiency.

Employing digital twins, Renoir Consulting accurately predicts the real-time performance of drilling rigs. This facilitates preemptive adjustments to operations, enhancing safety and efficiency.

Digital twin application: The consultancy developed digital twins that simulate the drilling process, incorporating real-time data from the rigs. These simulations allow for the analysis of different operational scenarios, enabling the prediction and prevention of potential issues before they occur.

Following the Trend of Adopting Digital Twins

The rise of digital twins in the oil and gas industry has proven to be fruitful with many applications that go from simple components to a whole process.

Still, one of the biggest, and persisting challenges seems to be the use of reliable data. If you just started collecting data from a component, asset, unit, or process, you’ll have to give it some time before predicting and making decisions based on an early simulation. You don’t want to end up on a tight spot when things don’t turn out as expected.

This is one of the reasons collecting data has become imperative for any company that wants to stay relevant (we’ll keep repeating it). Once you have enough data, let’s just say that limitations depend on the complexity of the system and the accuracy of the models.

CSE ICON is a professional services company focused on the design, development, and implementation of Operational Technology used in the processing and manufacturing industries. Our mission is to bring people and data together, ‌helping our customers continuously improve and increase profitability.