Rob Howard, vice president sales, MENA AspenTech talks to Pipeline Magazine about the growing push for operational excellence in the region and how AspenTech’s software can help companies
How do companies maintain asset integrity in today’s oil price environment?
With crude oil prices showing recent declines and little sign of any sustained upward trend, operators are looking for new ways to improve existing operational excellence initiatives. Asset reliability and integrity have risen to the top of C-level agendas as a result. With the onward march of digitalisation and the emergence of the latest AI and machine learning technologies, companies can increasingly solve these issues through predictive and prescriptive analytics.
And if they want to stay ahead of the pack it is increasingly urgent that they do. With predictive analytics, the focus is analysing issues known to cause a problem such as vibration in a pump or compressor. Sonic monitors can be added to the device and when vibration exceeds a certain level, alerts sent to advise operators that remedial action is needed.
Prescriptive analytics moves the methodology to a broader process-based approach, looking at data streams across interconnected systems in the plant to pinpoint sophisticated patterns of data happening in advance of an event, thereby helping to maintain asset integrity.
How much demand for operational excellence do you see coming from the Middle East region?
We’ve noticed a steady incline for increased operational excellence in the Middle East – much more than any other region. Because the fluctuations in the oil and gas prices over the past few years have had a major impact on profitability, companies have added pressure to find ways to remain sustainable. Operational excellence has the power to lower operational risk, lower operating costs, and increase revenues relative to its competitors, creating value for customers and shareholders.
How can AspenTech help firms achieve operational excellence?
AspenTech works with many large energy companies to improve aspects of operational excellence. These companies are increasingly leveraging our deep expertise in predictive and prescriptive maintenance. Embedded AI/machine learning provides real-time insight into plant assets, helping to determine weeks in advance if certain assets are likely to degrade or fail. Aspen Mtell software uses Autonomous Agents which run continuously, distinguishing between normal and abnormal equipment and process behaviour by recognising complex data patterns and uncovering the precise signatures of degradation and failure. Upon detecting unusual behaviour or failure patterns, agents alert operators and even prescribe solutions to avoid the failure, or at least mitigate the consequences.
What innovation are you bringing to the operational excellence sphere?
Most condition monitoring products only detect anomalies by trying to identify when actual behaviour differs from what’s expected. The most common approach is to use mathematical/statistical models based on engineering, thermodynamic and heat/mass balance equations.
Such models typically contain inaccuracies; they also fail to factor in process changes. Such products tend to rely on simulations rather than true patterns based on historical data. As sensors capture information about operating conditions, these products don’t analyse that data to change the patterns they seek – on their own, these tools do not correctly identify the behavioural signatures of normal equipment operation and exact failure.
What makes AspenTech’s Aspen Mtell solution different and more innovative is that agents are designed to do more than just anomaly detection; detecting, “Is this normal behaviour?” In addition, Failure Agents detect the actual behavioural patterns that begin early in root cause conditions that lead to very specific failures, e.g., a bearing failure. Such patterns are not unique to a single piece of equipment and Agents can learn on one and share with many in a pool of similar assets.
Aspen Mtell’s Autonomous Agents are software elements that automatically execute intense technical and analytical work in real-time, announcing issues with long lead times the second they are detected. They work continuously 24/7, constantly learning and adapting - and retain absorbed knowledge forever. However, when Anomaly Agents detect a previously unseen failure condition, a more detailed scrutiny determines the degradation pattern that created the anomaly and builds a specific Failure Agent that is able to detect the condition much earlier and with far more accuracy.
How can companies leverage their digital operations?
For organisations across the oil and gas sector today, the key is ensuring that they are taking advantage of the opportunity that digital transformation brings. Today, with the introduction of AI, machine learning and multivariate analytics, enabled by advances such as high-performance computing, the cloud and IoT connectivity, these companies can begin to address issues they’ve never previously been able to solve. They can really start leveraging their digital operations to derive business advantage and competitive edge.