Connected Barrels: Transforming Oil and Gas with IoT
By Andrew Slaughter, Executive Director, Deloitte Center for Energy Solutions And Anshu Mittal, Energy & Resources, Deloitte
The Internet of Things (IoT), combining affordable sensors, new communications tools, and sophisticated data aggregation and analytics software and decision algorithms is opening up a new era in business effectiveness and value creation. Yet, a recent study by Deloitte and MIT Sloan Management Review puts the digital maturity of the crude oil and natural gas (O&G) industry among the lowest— at 4.68 on a scale of 1 to 10, with 1 being least mature and 10 being most mature. In comparison, the IT industry scored 6.3 and even industries such as transport and tourism and professional services scored well above 5.
The industry’s low digital maturity is surprising on two accounts:
• First, the industry has been using advanced technologies and automated control systems such as remote operated vehicles, geophones, supervisory control and data acquisition (SCADA) for decades.
•Second, in general, there has been an increased availability of cheaper, smarter, and smaller sensors; advanced wireless networks and data storage solutions; and more powerful and ubiquitous computing power over the past few years.
So why the low maturity, and why the discussion now? The industry has mostly valued these technologies to locate and exploit complex resources and it has primarily used them at the asset/field/plant level only. What has been missing is an integrated analysis of information across technologies (also referred to as IoT) to make hydrocarbon extraction and every successive stage before sale more reliable, efficient, and even revenue generating—the three business priorities which are critical in today’s landscape of much lower oil prices.
In an industry as diverse as O&G, it is no surprise that there is no one-size-fits-all IoT solution. But each O&G segment (upstream, midstream, and downstream) can find the greatest benefit from its initial IoT efforts in one of the above three categories, which are enabled by new sources and free flow of information—in other words, completing the Information Value Loop.
Standardizing diverse upstream data sets
The fall in crude prices and the need to become efficient come at a time when upstream players (or, explorers and producers of O&G) are facing rising technical and operational complexity. This increased complexity, when captured with the tens of thousands of new sensors, has driven a data explosion in the segment, which is overloading existing infrastructure or not creating the hoped-for economic benefits for companies.
Lack of standards to aggregate the growing
When data is standardized and integrated across disciplines, companies can all together gain new insights. For example, analytics applied to a variety of data across disciplines—seismic, drilling, and production data—could help reservoir engineers map changes in reservoirs over time and provide insights for production engineers making changes in lifting methods. This “compounding effect” of analysis would most benefit production, followed by development and exploration.
Transporting volumes of information
With annual losses of $10 billion from fuel leaks and thefts, and increased business complexity due to the coming of shales, U.S. midstream companies (or, companies into pipeline transportation and storage of liquids and natural gas) face considerable upside in improving pipeline reliability and optimizing operations. Installing more operational hardware and software with pre-defined tags, however, would do little in addressing these challenges. What is needed is a shift toward data-enabled infrastructure that essentially sees, feels, smells, and hears various aspects of their pipelines.
Safe and reliable operations are table stakes, but to differentiate and seize new commercial opportunities, a midstream company must go further. A company can accrue higher commercial gains by analyzing product and flow data more comprehensively all along its network— similar to the way U.S. utilities are analyzing energy data using smart devices. The pipeline data, when correlated with emerging types of data such as geolocation, weather, shale plays, product grades, and export terminals in a timely manner can give rise to a forward-thinking and data-equipped midstream enterprise.
Going beyond the refinery gate
As crude-oil refining is a mature and commoditized business, running refineries as efficiently as possible is a routine task for downstream executives. Although a lot is yet to be achieved on this front through condition-based predictive maintenance strategies, more ambitious deployment could target expanded visibility into the complete hydrocarbon supply chain to enhance core refining economics and target new-age digital consumers.
This, in particular, makes sense for U.S. refiners, which are fast changing their crude sourcing from mostly buying heavy crude under long-term contracts to buying a greater range of light and heavy blends in the spot market. By installing more sensors on refining equipment, to gather data on the impact of processing various crude, and integrating this data with variations in oil delivery times, dock availability, and so on the refiner could evaluate multiple what-if scenarios, making its crude sourcing more dynamic and competitive.
Integrating data doesn’t stop at inbound logistics—there’s the outbound logistics of distribution to consider as well. The proliferation of personal technologies has led to the emergence of connected consumers who are demanding a connected fueling experience from retailers. Forward-thinking retailers will correlate consumer profiles with fuel purchases and in-store purchases, mashing up existing petro-cards data with that collected by cloud-enabled emerging telematics solutions, and combining it with data from social-media networks to facilitate behavioral marketing and predictive analytics.
The way forward
Investing in IoT applications is just one aspect. Companies without short-term milestones and a long-term IoT vision risk getting caught in endless choices and duplicating efforts.
• Ensure that IoT is creating the necessary momentum and learning across businesses and employees
• Ascertain future costs and complexities associated with retrofitting and interoperability of applications
• Assess security shortcomings in light of new developments
• Take the intelligence from the fuel to the “molecule” level
• Extend IoT’s reach from cost optimization to capital efficiency and mega-project management
• Embrace business models that enable new information value chains and promote information convergence across the enterprise