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June 28, 2018

To a large extent, the viability of oil and gas (O&G) companies depends on their ability to optimize complex, capital-intensive processes. McKinsey confirms this and goes on to say that digitalization in the global O&G sector has the power to unlock $50 billion in savings. Taking note, established players, such as Exxon Mobil and Chevron, are already well on their way, implementing strategies on similar lines.

Until recently, these companies did not have the tools required to optimize downstream asset functionality. The good news is they no longer need to overhaul their existing architectures. Advancement in analytics is paving the way for enhancing the production potential of complex process facilities, while improving ROI.

Yet, some enterprises are still hesitant to take this transformative leap owing to higher investment requirements at the initial stage and the unfamiliarity associated with the implementation process. Those who have an eye on the bigger picture should not shy away from making bigger bets. After all, machine learning (ML), sensor technology, robotics, artificial intelligence (AI), and edge computing can bring about a 20% reduction in capital expenditure and increase revenue by 3%.

Setting up Intelligent Refineries

Interconnected, agile, automated, and on-the-cloud solutions are all set to rewrite the playbook for managing existing downstream processes. In the present scenario, critical equipment often tends to malfunction, and sometimes issues escalate beyond control when field assets are assembled incorrectly. Apart from negatively affecting the operating budget, rectifying this without compromising uptime puts extreme pressure on the operations and maintenance (O&M) team.

The possible solution is a step-by-step journey up the implementation curve, which involves connecting physical assets with IIoT sensors. Companies can then remotely capture real-time operational data and monitor asset health round-the-clock through a centralized control hub. Analysing this data on the cloud and gleaning insights using machine learning algorithms becomes a simple process, particularly when these enterprises collaborate with technology partners. This will eventually help build virtual models or digital twins of downstream assets. Such simulations will enable O&G operators to test and validate the integrity of pipeline assets as well as subsea components, without having to put workers in hazardous situations.

At the Edge of Downstream Processes

In the end, the sustainability of digitalization initiatives depends on the ROI these can deliver. Before a fully functional smart asset management operation can be established, O&G enterprises will need to have the requisite smart assets in place. As more companies realize it’s not just about collecting performance data in a central control room, they are working towards creating equipment that can self-diagnose, and in certain cases, self-heal. In this regard, a combination of intelligent supervisory control and data acquisition (SCADA) and enterprise resource planning (ERP) systems can be systematically used to optimize assets, and improve worker productivity and safety.

A SCADA-enabled occupational health and safety management system will ensure security across operational sites by correctly mapping business processes, risk factors, and control mechanisms in the downstream. Augmenting this architecture with an ERP will further streamline resource utilization, maintenance, repair, and overhaul (MRO) capabilities.

As for leveraging cognitive and augmented reality (AR) technologies, the latest Google Glass Enterprise Edition is already being deployed as a standard safety equipment across workplaces. Early adopters such as GE, Boeing, and Volkswagen have experienced huge productivity gains and overall quality improvement.  The downstream O&G sector is expected to catch up and make these wearables mandatory for workers in order to meet OHSAS 180001 regulatory requirements.

In another move to make downstream processes more intelligent, O&G operators are partnering with leading technology conglomerates to develop edge analytics and broker-less data streaming platforms for connecting field assets to control systems in real time.

Looking Beyond the Cloud

In this context, edge analytics and fog computing is spearheading the downstream transformation revolution – eliminating the need to send data for central processing. Retrofitted edge sensors will enable supervisors at the helm of integrated SCADA-ERP systems to derive downstream insights, process them in the field, and generate billion dollars through smart supply chain management, engineering, and improved production.

Low-powered edge sensors embedded along pipelines will eventually be able to take automated decisions and act as the cornerstone for the next generation of self-healing assets. In order for the O&G industry to remain competitive, its future squarely rests on the shoulders of IIoT solutions and how well companies are able to assimilate them into their downstream processes.

What other technologies do you think will similarly impact O&G companies? Tell us in the comments section below.

Murli Agarwal is currently the Advisor and Director, Downstream COE, at TCS' Global Oil & Gas Practice.  Formerly, he worked as General Manager, IT and Chief Information Officer of Downstream Manufacturing with India’s Second Largest Oil Major Bharat Petroleum Corporation Limited. He is also the former President and fellow of Computer society of India and South East Asia Regional Computer Confederation (SEARCC). Agarwal carries over 37 years of experience in planning, designing and architecting of business transformation projects. In his current consulting and advisory role for Global customers , he conducts workshops and mentors teams on projects and front ended proposals for automation.


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