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Every industry has problems, and the Downstream segment is no exception. The biggest problem with downstream asset performance management and reliability today? Operators are not leveraging the full potential of digital twins to maximise the efficiency, safety, and intelligence of their operations.
Join our Customer Acquisition Manager Susana Hite as she dives into the specific problems that downstream operators encounter – and stay tuned for the Part II blog and our upcoming webinar series for Downstream where you can learn more about how digital twins address these operational challenges.
While collaborating in a reliability initiative at a major refinery, I came to understand the many systems, applications, workflows, and human behaviours that affect Reliability. These included Distributed Control Systems [DCS], Emergency Shutdown systems [ESD], Alarm Management Systems [AMS], Operator Training Simulators [OTS], data insights from previous unplanned events [Historians], manual logging tasks/workflows [Operator Rounds], a plethora of dashboards from one-off applications - and the list goes on.
Based on this experience, I learned that it is safe to say refineries experience 2 -3 unscheduled shutdowns in a year, and these typically last somewhere between 2-5 days on average. One major cause for this was the customer’s inability to access actionable data or insights from the many siloed systems in use, and too many manual workflows impacted their ability to act in a timely manner.
What if a digital twin could access contextualized and actionable data so the operators can make sense of it, and make informed decisions to avert an unscheduled shutdown?
I became aware of a major international oil company’s (IOC) effort to optimise oil production rates at one of their major oil fields, requiring a secondary recovery process to meet production goals and quotas. In their zest to improve their weekly production plans and schedule, they built a structure and allocated costly and experienced resources to basically aggregate and contextualise field production data, using their expertise to analyse and determine the best course of action based on equipment and operational gaps.
The problem here? To build a credible and actionable weekly production plan/schedule, they needed to access operational and maintenance data that was not readily accessible and distributed throughout different systems for a very scattered production field. In addition, the data needed to be analysed and contextualized by experienced operators and maintenance managers. This company’s solution of building a new structure and relocating the experienced resources to that building was nothing more than a costly exercise with few benefits to operations.
What if a digital twin could remove large parts of this manual work by facilitating more autonomous aggregation and contextualisation of field production data – in a single, centralised location?
During the covid-19 pandemic, I received a call from a Technical Manager at a major refinery. He wanted to know if there was a solution to his very real problem: ‘I can’t access the data I need from home. How can I fix this?’
This is a common challenge in downstream, upstream, and actually all of the segments across the energy value chain. Data is distributed across multiple systems, and many of these systems are local to the refinery, along with the processes that govern how this data is accessed. It’s difficult to work from home when you can’t access the information you need to do your job.
What if a digital twin could make this data available in the cloud, allowing anyone to access asset information securely – from anywhere in the world?
Paper-based documentation, coordination challenges, and a lack of real-time collaboration. When working with one customer, it became clear that manual inspection planning and work package development using clipboards and isometrics are cumbersome processes involving a series of manual and time-consuming steps. The effects are just as you would expect: major delays, inefficient inspections, an increase in operational risk and sub-optimal decision-making.
Critical inspection needs are often overlooked, and all these manual processes make it difficult to derive meaningful insights from inspection-related data. It also means that teams were working reactively, dealing with asset integrity issues as they arose.
What if a digital twin could introduce proactive and even predictive ways of working to optimize inspection routes, improve resource usage and streamline the development of inspection work packages for maximum efficiency?
