The oil and gas industry has always been volatile, but with the recent decline in oil prices and cuts in spending, companies are exploring new vistas to improve efficiencies, performance, safety and take risk mitigation measures, at the same time optimize production. In the Utilities Industry as well, dynamic fordecasting & load management, operational efficiency augmentation, increasing government initiatives for AMI accentuate demand for predictive analytics, with highly advanced data technologies focused around solutions for preventive maintenance and production-related analytics at the core.
MarketsandMarkets™ View Point:
Rajiv Roychaudhuri – Associate Director : E&P, at MarketsandMarkets™, shares his Point of View as mentioned below:
Making predictive analytics at the centre of all activities, is likely to take the industry to a different plane:
The energy and utility analytics market is segmented by software and service. The services segment is expected to grow at the highest CAGR during the forecast period, out of which energy and utility analytics services in the services segment is projected to witness the highest demand due to the growing need for energy and utility analytics software solutions across organizations.
Energy, oil, and gas holds the largest share of the energy and utility analytics market in 2016. The growth is fueled by the growing need of people to simplify their workload related to security and their increasing dependence on data generated from various devices used in the energy, oil, and gas vertical. Utilities is expected to grow at the highest CAGR during the forecast period due to the increasing need to store and manage data coming from various utilities used across organizations.
Analytical platforms, Big Data and new cognitive sciences are working in tandem to allow companies to analyze data from varied angles. Oil and gas companies produce multitude of of data in their day to day operations. Highly sophisticated data algorithms along with machine learning, predictive analytics and deep insights can help oil and gas businesses to increase operational efficiency by innovating the way in which they maintain and upgrade assets, identify the best areas and ways to drill and complete wells at lower costs, optimize production, decrease unplanned downtime and augment refinery and chemicals operations.
A paradigm shift from a reactive to proactive outlook to invest in predictive analytics
Several large oil and gas companies have put predictive analytics to good use upstream, earning returns from improved performance of unconventional wells. Few leading examples include Schlumberger, Chevron, Shell, GE Digital (Predix), Maana (Knowledge Platform), HortonWorks (Hybrid Data Platform) and SAS (Enterprise Miner). Maintenance, safety and production control are recent areas of adoption.Companies are exploring future uses of predictive tools in combination with emerging technology capabilities such as machine vision and behavioural analytics. Use cases in other industries may hold potential in oil and gas Industry firms like scanning the health, aerospace, financial services and other industries for examples of predictive analytics use in equipment diagnostics, prognostics and maintenance, pricing and risk management. Other relevant use cases include machine vision in the automotive, healthcare and sport industries; behavioural models in the financial services and retail industries; and enhanced route optimisation used by logistics and transport companies. Accessing the external data and expertise needed to make predictive analytics tick requires oil and gas firms to broaden the scope of their interaction. For example, many of the world’s technology giants support open platforms for developing algorithms, as well as cloud platforms and data lakes that provide humongous analytics power. Typically, the use of predictive tools is most advanced in companies earning annual revenues of $50 billion or more, as well as those with the highest market capitalisation levels. Interestingly, between 2012 and 2018, 425 patent applications relating to predictive analytics has been filed worldwide, out of which 5 were related to oil & gas with the first being filed in 2016.
Some of the Industry experts opine:
‘’We’re using predictive analytics in demand forecasting to manage our inputs and resources more efficiently. We need it, for example, to allocate tools and equipment to projects across 85 countries and to plan raw material inputs. We also use it to ensure people with the right proficiencies are available when projects get under way’’- Eric Abecassis, CIO, Schlumberger
‘’For us, the next big predictive analytics nut to crack is maintenance. We are making progress in preventive maintenance and in improving asset integrity. We will use it to predict events and better plan maintenance and inspection schedules. This can be applied in upstream, midstream and downstream’’- Margery Connor, leader, data science capability and technology, Chevron
‘’Advances in behavioural analytics and machine vision, the real-time capture and analysis of images, will enhance predictive analytics’ existing role in maintenance, safety and efficiency and also create new fields of application’’ – Daniel Jeavons, General Manager, Data Science, Shell
‘’ConocoPhillips is also experimenting with behavioural analytics to better predict the performance of its wells’’ – Richard Barclay, Manager, Advanced Analytics
The increasing volume of structured and unstructured data, changing client needs, and demand for efficient supply chain management has created a need for analytics software across energy and utilities verticals. The analytics software plays a pivotal role in collecting, analyzing, and transforming vast amount of data into valuable deliverable information. The oil vertical is a revenue contributor, as it has been an early adopter. As per the International Data Corporation (IDC), the global datasphere will grow from 33 Zettabytes (ZB) in 2018 to 175ZB by 2025, which is a staggering five-fold growth in seven years and definitely, the industry will be in a position to capitalize on this growth.
Conclusion:
Some of the major business applications for which the energy and utility analytics is deployed are load research & forecasting, meter operation & optimization, transmission & distribution management, predictive maintenance, workforce management, emergency response management, and others. The adoption of energy and utility analytics for predictive maintenance is expected to increase significantly in the coming years because of the increasing need to predict when the maintenance of assets needs to be performed to increase field crew efficiency, customer satisfaction, and reliability. Governments, regulators and industry leaders need to foster environments in which oil and gas companies can share data with confidence without any repercussions. An increasing number of the technology sector and other fringe players offer data and analytics experience that could be valuable to those operating in the oil and gas industry and the key lies in effective collaborations with such a rich diaspora with advanced skills, beyond the oil & gas majors. Interestingly, for oil and gas companies, making the most of data requires more than advanced analytics tools and skilled data scientists. It also demands a host of modern IoT sensors to collect and transmit the data, which maybe developed indigenously or selectively outsourced.