We believe data, algorithms, and software should power the industry and humans, to use their creativity to shape a profitable, safe, and sustainable present & future. Today, heavy-asset industries like oil and gas, renewable industry, and energy have reached a digitalization tipping point. Increasing access to data has made data handling a key changer, even in industries that have historically been considered far from high-tech.

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We believe machine learning is not a magic wand though it is an entirely new technology that is just at the beginning of the usage. Only 4% of companies in the whole world are in the phase – early practice. But, humans will still be the ones to drive the change.

Companies need to consider investing in the technology of digital twins (LENAᵀᴹ) that will amplify the experience and skills of their own people and assets. Digital twins technology is there to inform users about their operations and suggest measures to avoid any downtime. Once again, humans bring their own expertise to the table, supplemented by a data-driven decision and the ability to examine the data more deeply before taking any action. A creative mind is something that can not be automated and humans are essential in the artificial intelligence reality that is coming.

Online Digital Twins

As the operational life continues, the digital copy is updated automatically, in real-time, with current data, work records, and engineering information to optimize maintenance and operational activities. Using this information, engineers, managers, and operators can easily search the asset tags to access critical up-to-date engineering and work information and find the health of a particular asset. Previously, such tasks would take considerable time and effort, and would often lead to issues being missed, leading to failures or production outages. With Online LENAᵀᴹ, operational and asset issues are flagged and addressed early on, and the workflow becomes preventative, instead of reactive.

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The reliable, real-time process data from the digital twin can be fed into simulation and analytics to optimize overall production, process conditions, and even predict failures ahead of time. A digital twin, when combined with powerful analytics and machine learning, enables predictive maintenance and optimized processes. Analytics leverage advanced pattern recognition, statistical models, mathematical models and machine learning algorithms to model an asset’s operating profile and processes and predict future performance. Appropriate, timely actions are then recommended to reduce unplanned downtime and to optimize operating conditions. With the digital twin, process simulation can also be performed to optimize the operating models based on their physical properties and thermodynamic laws.

The following three steps approach enabled by Digital Twin to optimize oil and gas production -from gathering systems to gas processing plants – is fundamental to improving performance and boosting profitability:

1. Steady State

Engineering and Design (FEED) stage, steady-state simulation models of gas processing and others, can be created to optimize the design. During operations, engineers and operators can perform engineering studies via Offline to identify design changes that will significantly increase throughput and the reliability and safety of plant operation.

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Data analytics can be used to model fluid flow behaviors in pipeline multiphase or single-phase flow to predict pipeline holdup and potential slugging in the network. Understanding flow performance is key to optimizing the gathering network design, reducing CAPEX, and optimizing pipeline. With a unified simulation platform, the evolution from a steady-state to dynamic simulation can be achieved effortlessly.

2. Dynamic Modeling

Dynamic modeling based on ordinary differential equations and partial differential equations can be performed on these models to validate process design such as relief and flare systems, changes in feedstock, production capacity adjustment, and controls, enabling engineers to optimize the design and reduce CAPEX and OPEX. In addition, the dynamic simulation allows effective troubleshooting, control system checkouts, and comprehensive evaluations of standard and emergency operating procedures to shorten time requirements for safe plant start-up and shutdown.

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3. Predictive Analytics to Monitor Equipment Health

Minimizing plant downtime is therefore key to improve production. This is where predictive analytics comes in. Predictive analytics enables modeling of rotating equipment performance – such as pumps, compressors, and turbines – using advanced pattern recognition and machine learning algorithms to identify and diagnose any potential operating issues, days, or weeks before failures occur. Minimizing plant downtime is therefore key to improving production. Operating models including past loading, ambient, and operational conditions are used to create a unique asset signature for each type of equipment. Real-time operating data is then compared against these models to detect any subtle deviations from expected equipment behavior, allowing reliable and effective monitoring of different types of equipment with no programming required during setup. The early-warning notification allows reliability and maintenance teams to assess, identify and resolve problems, preventing major breakdowns that can cost companies millions of dollars in production slowdowns or stoppages.

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Brief Summary overview of Digital Twins

Digital Twins works as a back-end rigorous model and running to report key data. Such as production throughput, product purity, but it also includes extra features and tools for optimization Oil & Gas production. Digital Twins can work either offline (case studies and What-If) or online (data acquisition and optimization) based on the requirements of business values.

The digital twins enables operational excellence by helping oil and gas engineers, managers operators and owners take a model-focused approach that quickly turns massive amounts of data into business value.

These powerful data insights mean

  1. Asset failure can be predicted.
  2. Hidden revenue opportunities can be uncovered and realized.
  3. Businesses can continuously improve in the ever-changing, competitive marketplace.

In a nutshell, Digital twins is the foundation of a digital transformation that optimize production, detects equipment problems before failure occurs, uncovers new opportunities for process improvement, all while reducing unplanned downtime. Depending on the facility itself you can combine data analytics, machine learning, Big Data, and software applications in order to utilize the power of the data and turn it into business value for every Oil and Gas company.

Until next time,

Manja Bogicevic