Oil and gas companies in 2019 face multiple challenges. With pricing and demand uncertainty, many producers are trying to get the utmost out of their sunk CAPEX. This requires maximizing asset uptime and maximizing utilization. Data, analytics, and digital twin modeling help them look at what-if scenarios and optimize the use of assets.
Refiners seek flexibility to react to future volatility in markets and business models. This requires a digitally-enabled asset and workforce. Above all, most oil and gas companies have seen significant exits of experts from the industry during the price downturn. There is an opportunity to substitute AI-assisted solutions for staff with years of experience.
“The oil and gas sector is familiar with digitalization, going back to the first automation of exploration data, processing plants, and refineries 35 years ago,” Ron Beck, strategy director, at AspenTech says. “The area that is giving traditional players in Europe and North America pause is how digital strategies will force ‘democratization’ of data and decision making. We find some Asian energy players more eager to embrace flexible organizational structures to gain competitive advantage from digitalization.”
Learning a new language
There has been a good deal of talk within the oil and gas sector of a digital transformation with buzzwords such as Artificial Intelligence, Machine Learning, Big Data and Analytics, Edge and Cloud Computing and IIoT thrown into the mix. However, sometimes speaking to those involved in the sector it appears that the industry is struggling to understand these technologies and lacks the know how to implement them to gain actionable insights.
“My feeling is that there is understanding of the base technologies, but not of how technology has rapidly evolved into easy-to-adopt solutions,” Beck explains. “Some of the technologically strongly-heritage energy companies have made, in my opinion, the mistake of very heavily investing in staffs of data scientists with little domain knowledge.
“We’ve been through this cycle before. This is not the energy industry’s core competency. They should be selecting the best of the new solutions based on advanced technologies, rather than trying to customize their own. There is a steep learning curve for them, and they will be behind.”
Foundation technologies
AspenTech provides five of the foundation technologies that energy companies depend on: namely simulation modeling for design and optimization of assets, refinery planning and scheduling, dynamic optimization, adaptive multivariate process control, and prescriptive maintenance (based on machine learning). AspenTech is embedding advanced AI, machine learning, edge computing, and cloud technologies into each of these critical business solution sets, so that core energy industry systems evolve into powerful knowledge-based and AI-based systems, married with technology containing extensive domain expertise built in.
“This technology fundamentally will enable a step-change in asset operating margin, by 30-50% over the next five to ten years, and in sustainability by wringing energy and water use out of the processes while accelerating innovation of new recyclable and degradable plastics,” Beck says.
Looking for low hanging fruit
According to Beck, two significant areas present a low hanging fruit opportunity. One is prescriptive maintenance, based on machine learning, to reduce downtime in upstream and downstream. “We have worked with several major refiners to identify millions of dollars in value available for knowing about refinery unit downtime 60 days in advance, which will change oil trader decisions, buying the right crudes for actual refining processing availability,” Beck says.
The second area is autonomous dynamic optimization, finally after years of promise, tying together refinery economic planning with advanced process control in a closed loop process. Two published case studies in significant refineries have identified over $10 million benefits each per year from modest implementations already. This is based on patented methods for reconciling real-time data streams with optimization models and based on the breakneck computational speeds now available.
Upgrading the Saras refinery
As part of an effort to drive reliability in its refinery operations, Saras, a leading European crude oil refiner, turned to Aspen Mtell prescriptive maintenance from AspenTech to improve equipment uptime and decrease maintenance costs.
“Saras executed a project within weeks that predicted equipment failures up to 45 days in advance using prescriptive analytics and enabled the company to increase revenue and cut operating expenses,” Beck says. “These capabilities are expected to reduce unplanned shutdowns by up to 10 days, increase revenue by 1 to 3 percent, reduce refinery maintenance costs and cut operating expenses by 1 to 5 percent.
“We have implemented a similar solution to the Saras solution at a Borealis polymer plant in North Europe. Other similarly successful implementations are underway in several refineries in North America, a cardboard plant in Asia, and several other situations. Now that companies can see and talk to implementors of successful applications of prescriptive machine learning, momentum is building in this area.”
The numbers projected by several refiners range from two to seven additional days uptime per year. Depending on the size of the refinery, that incremental revenue is economically significant. “The example I mentioned earlier about making better crude purchase decisions is worth even more to a refiner,” Beck adds. “Cases that we have validated with European refiners indicate that the value will be significantly over $1Million per month.”
Start with focussed applications
The clear message is to start with focused applications targeting well-defined business value cases. “This will create wins within a company that will generate their own momentum in the organization,” Beck concludes. “The other seemingly-paradoxical idea is to aim high. This area will enable business agility in a quickly-changing global energy world.
“Currently, there is a wide range of innovation happening with start-ups, established leaders like ourselves, and within energy companies. This will help the industry to attract the new, enthusiastic talent that it needs. Moreover, it will enable the industry to break barriers in supplying energy sustainably.”
