energy, refining, chemicals, oil and gas, digital transformation

Which Technologies Will Help Determine the Winners in the Energy Transition?

November 10, 2020

As energy companies navigate short-term profitability and long-term investment in energy transition, digital technology has become perhaps the key swing player. For many of the global refining, midstream and upstream companies, AspenTech’s V12 software is coming at a crucial time. As energy executives are looking for ways to apply the artificial intelligence (AI) technologies that have dramatically impacted other industries, AspenTech’s V12 portfolio, three years in the making, brings AI into a practical realm that immediately helps the operating units across the energy value chain. 

Embedding AI in a manner called Industrial AI in our software has created unprecedented excitement among our global energy customers. It has been designed to get directly at key levers which can release uncaptured value across the energy value chain. Companies who have taken an early look at the software report this is true. Over 80 companies engaged in early testing of several innovative new capabilities and identified highly practical and immediately implementable use cases.

One of our largest energy customers had over 50 employees take part in one of our innovation calls, in which we provide early looks at software and solicit feedback. They wanted to understand what we had discovered in our R&D process as to how to merge first principles with machine learning. Their own internal programs involving over 100 data scientists were reaching roadblocks. The customer’s team was excited and impressed with what they saw. Another company gathered over 70 technical experts to listen to an introduction to our new innovations.

What are some of the big impact areas that have energy companies so interested? Here are some of the powerful ways these solutions hit on the industry’s strategic change levers:


Energy Efficiency: A Key Carbon Reduction Lever

Energy efficiency progress, in refining, midstream processing and upstream production, offers a significant opportunity to capture margin but more importantly, will reduce a company’s carbon emissions. A CONCAWE study estimates that energy efficiency can make up more than 20% of the total carbon reduction the European refining industry needs to meet its Paris Accord goals. 

In AspenTech V12, AI is being embedded in Aspen DMC3™ multivariate control software in two important ways to increase DMC3’s ability to be deployed quickly and capitalize on its ability to reduce energy use significantly. Aspen Maestro™ for DMC3 reduces the time and expertise needed to build advanced process control models, massively lessening dependence on APC experts who are in short supply. And Aspen Deep-Learning IQ™ increases Aspen DMC3’s ability to accurately set and reach its targets. This will allow a company to broadly roll out an APC project 50% to 80% faster than possible before, enabling the scaling needed to significantly impact carbon use within the timeframes required.

Also in Aspen V12, Aspen Hybrid Models™ take advantage of AI combined with first principles asset models, to deploy fast-running and rapidly-calibrated reduced-order models. These can be run online, monitoring and optimizing major refining, petrochemical, LNG and gas processing units and mineral processing reactors, driving the process operating changes which further increase efficiency for the major energy consuming units in these plants.

These two breakthrough advances, combined with existing digital tools, such as the Aspen Utility online digital twin for energy optimization, can create the digital backbone for driving very significant carbon reduction for asset owners.


Navigating Shrinking Refining Margins

The rapid collapse of refined products demand, followed by market volatility over time and by region throughout 2020, has underlined the crucial importance of flexible, accurate and powerful refinery and multi-site planning technology. Planners have been laboring non-stop for months to respond to market conditions, pricing and executive scenario analysis. Enter into this fray several key breakthrough advances in aspenONE® V12. These include:

  • Aspen Unified™, the next generation planning, scheduling and optimization solutions, offers a major step forward in closed-loop operation, therefore allowing planners to adjust quickly. Introduced in September, in October we added two innovations which embed AI into the planning workflow and immediately create tens of millions of dollars of value.   
  • Aspen Verify™ for Planning provides an AI advisor for the planner to compare proposed plans with previously executed plans at that asset, helping the planner avoid repeating past mistakes. Also, Aspen Hybrid Models for PIMS brings together non-linear first principle reactor models with AI machine learning, to effortlessly embed non-linear accuracy into the widely used Aspen PIMS-AO™ planning technology. This boost in model accuracy alone will be worth on average over $0.05 cents per barrel in margin through the ability to set and achieve better monthly (or more frequent) planning targets.


Squeezing Cost out of the Upstream and Midstream

Most of the easy and even medium-hard ways to reduce costs in oil and gas production were done during the last oil price trough in the 2015 timeframe. Now, as in downstream, the pressure is higher, because in addition to cutting the production and processing costs, the upstream and midstream player must also show commitment to reducing carbon emissions, flaring and water use. 

Again, Aspen V12 comes to the technology rescue here. The newly released Maestro for Aspen Mtell® provides the ability to deploy specific machine learning analytics-based solutions for specific business problems rapidly, including upstream flow assurance. Two upstream operators have already proved the economic value of Maestro for Mtell in predicting and avoiding costly upstream problems such as wax and asphaltene formation. Here is another important use case for Aspen Hybrid Models. Aspen Hybrid Models have been applied to improving the ability to deploy digital twins for gas dehydration in particular and gas processing in general, unlocking the access of digital twin models to non-technical business users solving high-impact business problems, such as accurate production allocation as well as energy efficiency.

This is just a brief introduction to the innovations in V12 that quickly deliver substantial value for energy companies. Embedding Industrial AI in business-critical solutions is at the heart of AspenTech’s V12 portfolio. The pace of innovation has been breathtaking, leading to the most important product release in AspenTech’s history. One of the leading industry analyst groups told us that they were surprised by how much we achieved in such a short time after announcing our Industrial AI vision — and by how much it will impact industry.


Those energy companies who are able to embrace AI organizationally and align it with their business initiatives will gain important competitive advantage. We believe that our newly release hybrid modelling and Industrial AI capabilities are the first leg of the journey to the self-optimizing plant. I invite you to learn more about them through the videos, white papers and materials we have available, or by talking to our experts and to our partners.


There was a problem storing your subscription

Leave A Comment


  • 3 years ago

    Confiabilidad operativa de los sistemas.

  • 3 years ago

    Confiabilidad operativa de los sistemas.