Interview: Leveraging the vast potential of data analytics

The digital transformation of oil, gas and chemical enterprises is radically changing the way professionals are thinking and working, comments Dan Overly, vice president and global head of products, oil, gas and chemical, ABB.

Dan Overly, vice president and global head of products, oil, gas and chemical, ABB.
Dan Overly, vice president and global head of products, oil, gas and chemical, ABB.

The digital transformation of oil, gas and chemical enterprises is radically changing the way professionals are thinking and working, comments Dan Overly, vice president and global head of products, oil, gas and chemical, ABB.

What are the major current technology trends in the oil, gas and chemical businesses?

Big data, Internet of Things and digitalisation are a normal part of our industry today. What we see as well is the need for adding data analytics to all these elements. Optimal use of data analytics can take the oil, gas and chemical businesses to the next level of technological advancement.

It is a fact that oil, gas and chemical projects have been executed the same way for decades. With the recent re-set in cost for this sector, there is a need for a major change – efficiencies need to be uncovered in order to drive significant project and life-cycle savings. This change has already started with ABB’s Intelligent ProjectsTM.

By integrating intelligent infrastructure and intelligent engineering, we can create a truly integrated plant that reduces overall project risk while increasing competitiveness by lowering capital and operational costs. Intelligent engineering embraces ABB’s expertise as a main automation contractor, main electrical contractor and main instrumentation contractor.

The digital transformation of oil, gas and chemical enterprises is radically changing the way professionals are thinking and working. While this journey is improving how we build relationships with customers, partners and each other, the truth is that value creation happens at every level – device, process, plant, people and enterprise.

Where will data analytics be applied as far as the oil, gas and chemical industries are concerned?

The vast potential of data analytics could be leveraged at every level of the oil, gas and chemical businesses. For example, take the case of plant operations. The field sensors that are used to collect data from critical process equipment, such as motors or compressors, are embedded with data analytics so that they can diagnose equipment health issues before any negative incident happens.

On the other hand, the data collected from multiple plants could be used to feed powerful algorithms on private clouds that can understand how to improve the efficiency and the reliability of the process equipment by tailoring specific solutions for the specific process needs.

When we talk about project execution, as the main contractor, ABB provides project management together with design and engineering expertise. We also oversee interface management of the engineering suppliers and takes responsibility for the data transfer between parties.

Our proactive approach during project execution entails close cooperation with the client, other contractors and suppliers to implement best-in-class solutions, and makes ourselves accountable for the interfacing with other participants. This ensures effective integration of all components and systems. We achieve streamlined project execution using innovative, digital technologies such as cloud computing, virtualisation, emulation and simulation. Often, key added value is overlooked by not using such technologies. Virtualisation, emulation and simulation help us to remove the need for hardware testing in the factory, making project execution faster, as well as more reliable and cost effective.

Which sectors are more active in the implementation of data analytics?

World over, the chemical sector is the one that is experimenting the most. Large chemical companies, especially in mature markets like Europe, are testing and starting to use data analytics to improve profitability of their plants.

The upstream offshore and subsea sectors have always been the technology front-runners in the oil and gas business. Are these sectors active now in data analytics?

These sectors have been impacted heavily by the low oil price. However, we see a lot of research activities to improve profitability of the offshore and subsea plants, and to be competitive in a $50-60 per barrel market.

Our digitally enabled process performance services aim to improve operation and production quality by collecting data from customers’ existing assets, identifying areas where process improvement is needed, suggesting approaches to address challenges and deficits, and then implementing the services necessary to strengthen performance.

With lower oil prices, upstream operators are increasingly focusing on improving production. With the current price level, process performance improvement can significantly improve the bottom line of the oil companies. Typically, 3-5% increased production is achievable, translating into about $35mn extra earnings per annum for a platform producing 50,000bpd at an oil price of $50 per barrel.

It is worth mentioning here about one of our digital tools for the upstream oil and gas industry – the ABB AbilityTM Process Performance Dashboard. Through this digital platform, following some 15 years of identifying poor process system performance in upstream oil and gas facilities, we have developed six key performance indicators for the process performance of a plant. These include three symptoms and three results of bad process behaviour.

Is data analytics used by the midstream oil and gas sector?

The midstream sector has always been challenged to be the most ‘cost effective’ segment of the oil and gas production chain. Gas and oil transportation via pipeline, especially, had the issue of reducing the operative costs to not impact significantly the cost of the delivered product.

The use of data analytics and digital technologies can help the midstream sector to overcome this challenge. Performance of the large machines used in the pipelines (gas compressors and pumps driven by gas turbines and electrical motors) can be monitored in a centralised way and this information is injected into machine learning algorithms that optimise the load profile of each station across the pipelines.

The performance of the pipeline compression and pumping station can improve by several percentage points, without a single mechanical change to the machines. This improvement leads to significant reduction in the CO2 impact of these plants and up to double digit savings in the energy bill of the pipeline operators.

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