Oil and gas industry operators benefit from digital twin through a quick response when something goes wrong. With digital twin technology, operators can use data they collect from sensors on their equipment to create accurate models that replicate how these machines operate in real-time.
It means that if an anomaly occurs, there's no need for expensive trial-and-error or lengthy troubleshooting procedures - open up your model, and you'll know what went wrong. In addition to saving money by avoiding costly repairs, this will save time which means more production time!
This article describes how digital twins are helping organizations make sense of large volumes of diverse data sources—whether internally generated or provided by third parties—and use them effectively for making.
Emerging Technologies and Digital Transformation:
The new face of the oil and gas industry is quickly becoming digitized. Emerging technologies allow for production to become a cycle, automated, efficient, and streamlined - but this also means that you get to deal with operational intelligence.
Digital transformation will affect every stage of a company's lifecycle- from upstream operations to midstream labor-management down into downstream sales efforts. Even services in oil fields can be managed more efficiently digitally through Emerging Technologies. It will challenge operators to transform substantial data sets acquired in various processes into actionable intelligence.
Oil and gas industry operators benefit from digital twin through advanced analytics in their plant operations to improve the performance of assets, reduce unplanned downtime, and extend equipment life. In addition to these things, it also allows for a greater return on investment by identifying complex problems.
Digital transformation provides opportunities for improved return on investment by identifying quick fixes upstream, midstream, and downstream processes.
Read more: How Digital Twins Can Help In Saving The Environment
In addition, with the digital twin, a machine's maintenance and operational intelligence are never compromised.
With predictive analytics for maintenance and prescriptive analytics for operations intelligence, your business will always have the edge over any of its competitors by being able to fix problems before they even happen! In addition, the augmented reality provides tools that improve both productivity time and the effectiveness of the repair.
Digital Twin Mirrors Manufacturing Big Data:
The oil & gas industry is a massive business that generates an incredible amount of data. The oil & industry data will typically have quality reports, process control history, operational deviations and variations, product blends and formulas, etc., related to the production process.
The Bureau of Labor Statistics found that this sector had more stored data than any other business or industrial sector in a recent survey among US manufacturers.
The data generated by today's connected world comes in a wide variety of formats and needs to be aggregated, analyzed, and converted into actionable information.
The digital twin is a virtual representation of your production plant that can provide personnel with operational intelligence. This process starts by combining Big Data, statistical sciences, rules-based logic, and artificial intelligence into one easy-to-use package called predictive analytics.
Read more: Simulation in Digital Twin for Aerospace, Manufacturing, and Robotics
Advanced machine learning allows the company to discover complex problems shaping up in their manufacturing processes and then determine ways to resolve them before they become costly.
The move from predictive analytic models will eventually lead manufacturers out on top because it utilizes big data effectively without adding too much cost or complexity along the way.
Digital Twin and Machine OEMs:
The relative benefits of the digital twin will depend on many factors, not limited to complexity and quality. As assets increase in sophistication, demand for a digital representation is bound to overgrow, too - with one difference: ubiquity across its lifecycle. The genuine virtual version will contain information about design as well as manufacturing and service life.
There has been some debate over who should be overseeing them: those with knowledge or experts in data science? Without answers, we won't know how best to utilize their potential capabilities
The oil and gas equipment OEMs (Original equipment manufacturers) are traditionally the best informed about information, such as engineering analysis data. However, end-users of these assets require this operational performance data to be successful in their jobs.
For a digital twin to work effectively, the manufacturer should share the information or offer an online service-based business to monitor and optimize digital and physical asset performances.
It includes servicing, optimizing operations with real-time data analytics, improving safety in complex environments like offshore drilling rigs, or carrying out hazardous tasks like handling chemicals at a refinery.
Implementing this type of initiative could be done through partnerships between IIoT software vendors that develop solutions to support these new approaches. In addition, there are emerging opportunities within large organizations that have been adopting advanced techniques across their business units.
Manufacturers of long-lifecycle products such as gas turbines and pumps are coming to understand that after-sale service is a significant differentiator for them. Implementing digital twin services will improve efficiency in the field, which can be very helpful when considering how many people it takes on average to change out oil filters at most factories worldwide.
By connecting remote sensors with real-time data analytics, companies have new opportunities not only have they have never seen before but also ones that were previously unaffordable due to cost considerations or complex engineering problems involved.
Manufacturers who implement this intelligent technology into their manufacturing process stand poised to provide better customer satisfaction rates and reduced downtime through continuous monitoring, thereby increasing profitability by improving quality control metrics.
Oil and Gas Industry Operators Benefit from Digital Twin & Asset Performance Management:
With digital transformation, oil & gas companies are redefining their business models and operations, but these changes would not be possible without effective asset performance management (APM).
APM can help oil & gas firms to increase maintenance efficiency and effectiveness.
It helps to avoid costly unplanned downtime while minimizing the need for scheduled downtime. It also improves safety by cutting down on risks of accidents.
With this strategic approach to managing assets in place, the company's regulatory compliance costs will also decrease as well as minimizing the risk of non-compliance which is always a top concern when it comes to environmental protection regulations
Data is a valuable resource, but it cannot be easy to manage due to the sheer abundance and variety of sources. Modern APM can alleviate this by collecting all information into one system for ease-of-use and quicker analysis periods so that valuable insights are never lost again!
Imagine life without oil & gas. It would be much less convenient, not to mention plain dangerous. That's why you should invest in the industry today!
Operators collect data and analyze it. The approach enables companies to develop new techniques with better efficiency, safety, yield rates, etc., leading us towards a brighter future for all involved parties in your investments.
The technology around collecting and analyzing data has enabled many improvements for those invested in this sector. This work can lead industries into their "brightest" futures through increased production flexibility or more efficient operations...and it only gets easier when people are willing to dedicate themselves fully toward these goals.
APM is a new way to monitor and manage oil production from unconventional sources. APM integrates into the larger automation environment, enabling companies to take advantage of shale oil and gas opportunities, ultra-deepwater, or subsea applications.
Accurate and timely data is the lifeblood of a company's success. In today's business world, oil companies have to constantly adapt their operations to improve efficiency and safety standards for employees operating on site.
It becomes difficult to comply with regulations across different sectors without an efficient way of collecting accurate information about all aspects, from production levels and equipment status up through downstream applications like environmental impact reports or health & safety assessments.
Midstream operators can now benefit from improved visibility into what goes wrong when things go wrong to act quickly. It is possible because integrated APM solutions aggregate real-time operational event intelligence at every level - including plants, refineries, pipelines, and transportation networks.
Conclusion:
Fossil fuels have powered the world ever since the Industrial revolution. However, Digital technologies like artificial intelligence (AI) and Blockchain are making the process of extracting energy more accessible, cheaper, more efficient, less risky - and cleaner!
Digital twin technology is a new, innovative innovation that has the power to change the way we work. For example, we can use this new technology to create digital replicas of our environments and assets – also known as virtual simulations – and have them interact in real-time with their physical counterparts.
It means you could simulate making any significant changes or decisions which would otherwise be costly!
Digital twins are changing today's way we operate by providing information about our environment and previously unavailable assets.
Oil and gas industry operators benefit from the digital twin significantly. The benefits include increased safety, improved production rates, lower maintenance costs, and reduced downtime. With these advantages in mind, it's no wonder why more companies are jumping on board the digital twin train!
Simulation experts in industry 4.0 must have a passion for digital twin technology today for industry research. Digital Twin simulation is entering mainstream use as more industries are adopting this technology. According to Gartner's IoT implementation 2019 survey, 75% of organizations already use Digital Twin or plan to in a year. Notably, all the companies willing to adopt Digital Twin are implementing Internet-of-Things.
Digital Twinning is not a new technology. In 2002, Michael Grieves of the Florida Institute of Technology introduced the concept of Digital Twin publicly. In 2010, NASA showcased the first practical implementation of Digital Twinning to improve the physical model simulation of spacecraft.
We can see Digital Twin has been around for two decades now. Yet, many businesses are confused about the value of Digital Twin Technology. In addition, many companies don't know the use of Digital Twin in the modern energy, chemical, and process manufacturing industries.
Many companies still don't know that Digital Twin can enjoin the disconnected processes cutting out manual efforts that can be time-consuming.
For instance, interns or low-level employees will still follow the outdated method to gather engineering information. They would walk around one department to another to collect data required for engineering research.
Many engineers use CAD and PLM software and other sources to collect data to make informed design decisions. A few engineers are lucky enough to use enterprise search engines to pull information from various departments from hundreds of documents, folders, presentations, etc.
Read more: Simulation in Digital Twin for Aerospace, Manufacturing, and Robotics
Now that we are entering the age of automation, companies must adapt to cultural change and access the right technology. They need to integrate technologies like Digital Twin to help teams gain information without any hassle. They also need to save employees from the pain of surfing through numerous record systems.
There are other reasons to consider Digital Twin to accelerate business innovation. Here we've discussed 3 of them below:
1) The Rapid build-up and expansion of data:
The business environment of the energy and chemical industries is already volatile. On top of that, the decision-making cycle is in an array across these industries due to piled-up data sources.
System Digital Twins made for entire plants, or factory systems can rescue energy and chemical industries. A massive amount of operational data can be collected, organized, and analyzed from various devices and products.
Human decisions are not rational, even if we make sound judgments after weighing evidence and assessing probabilities. It happens because the human brain tends to simplify information processing. So, cognitive biases, including memory and attention biases, influence human decision-making.
System digital twins can eliminate human bias for critical decision makings. System digital twins can also provide a single logical view of the actual situation based on evidence, probabilities, and analytics.
It is also essential that you know your needs before adopting Digital Twin Technology. Therefore, you must ask these three questions to ensure your success with Digital Twin:
2) What type of analytics should Industry players seek?
The factory systems and manufacturing plants involve complex processes today. So, measuring KPIs isn't easy now.
The digital twin can resolve this problem by providing deeper analytics from factory systems and plants, taking multi-dimensional factors and non-linear trade-offs into account.
The digital twin can build an accurate understanding of the future based on historical and present performances data. The digital twin can recommend the best strategies that can maximize profitability for the industries. Next, the experts will need to assess each recommendation and its impact to make the best decision for the businesses.
Therefore, industries can use the digital twin technology as a supporting tool to aid decisions enabling improved safety, reliability, and profitability.
3) Digital Twin Model Utility across the entire lifecycle of the asset:
Manufacturers use digital twins differently at each stage of the product development cycle.
Initially, manufacturers start working with Digital Twin Prototype or DTP. Then, manufacturers use DTP to design, analyze, and plan out the process to predict the future shape of the actual product.
In the next phase, manufacturers use Digital Twin Instance or DTI. DTI is the virtual twin of a physical asset. Developers will use DTI to run multiple tests and determine how the product will behave in different scenarios.
The DTI stays connected with the physical asset throughout its lifecycle. As a result, developers will add more operational data to improve it over time.
In the final phase, manufacturers will use Digital Twin Aggregate or DTA. Manufacturers use DTA to cross-examine the physical product, predictions, and learning based on the collected data from the previous phase.
People from engineering, operations, supply chain, shop floor even board room can look inside the assets and processes of the Digital Twin technology at every stage.
Companies integrate AI, machine learning, predictive analytics, etc., into the system with high hopes. They do it because they believe that digital transformation will cut out all the manual workload. However, when they realize that a lot of the work still depends on the human end, they get shocked.
Industries may have entered the automated age and have innovative IoT solutions at their disposal. However, automated systems cannot replace the human touch in many critical areas of business. For example, humans still need to implement and monitor automated systems in manufacturing plants.
Read more: How Are Industries Creating New Opportunities By Combining Simulations and AI
Automation cannot replace other tasks like enhancing product design, building strategies, and growth roadmap, decision making, communicating with stakeholders, applying creativity to solve problems, etc. These areas will continue to need human intervention.
Companies need to set clear expectations when moving forward with the digital transformation of the assets.
The purpose of digital transformation and digital twin is to make the technical aspects of the job easier. In addition, Digital Twin technology will provide you the intelligence to help you focus your hard work on beneficial outputs.
Companies building Digital Twin Technology today are the pioneers of shaping the agile and intelligent industries of tomorrow. So, they need to develop the right digital twin platforms to leverage the full potential of digital transformation to create an exemplary model that others can follow.
The first steps will always be challenging. You can expect objections and hurdles to come your way. However, all these troubles are manageable if you know the proper ways to manage them.
Here is a brief guideline to follow for successful digital twin adoption in business:
To sum it up, digital twin adoption has the scope to attract more stakeholders' buy-in. Companies can show them the data-driven rewards based on concrete analysis instead of flawed predictions. So, the stakeholders will always have the know-how of the direction they are heeding with you.
"The true benefit of a digital twin: it gives you business intelligence to make better decisions in the future. It doesn't eliminate or minimize the work you're doing now, but it fundamentally changes what you're going to do next." - Former chief executive of Cambridge City Council, Andrew Grant
As we deduce the statement of Andrew Grant, it's a life lesson that industries have learned the most brutal way around the world after the Covid-19 shock. Thus, many enterprises are seriously considering the concept of Digital Twin and thinking big to expand it.
Companies are now interested in optimizing business operations based on the real-time insights gained from manufacturing plants and product use. As a result, they are more focused on satisfying orders, resolving root causes that are hindering growth, and maximizing factories' performance based on solid predictions.
We have already entered the age of automation as the 4.0 industrial revolution has begun. Today, companies maybe just interested in predictive maintenance. However, the use of Digital Twin Technology will expand where it will be integrated not for products but into manufacturing processes and entire factory systems.
