This blog was originally posted by ARC Advisory Group.

Predicting how machines and processes will behave in the oil and gas industry is key to proactive maintenance, future productivity, and driving new value. To succeed, it’s important to understand all aspects of an oil and gas process and bring together the physical and virtual assets in a continuous loop of knowledge and improvement, in effect, giving machines a “mind.”

The focus today is on the next generation of smart, connected machines and assets that will support the requirements for connected plants and IoT ecosystems. These so-called edge devices will function as the source of data and information that will also power the Industrial Internet of Things (IIoT). Next-generation edge devices with embedded intelligence and advanced analytics are being designed to support the IIoT. With intelligent, connected machines, production lines, and equipment, IIoT promises to connect things, people, and systems, in effect, bringing minds to machines to deliver organizational value. This is only possible by seamlessly unifying all these pieces, and it’s no easy task to break down the siloes.

Emerging technologies and digital transformation are rapidly changing the face of the oil and gas industries production systems, processes, supply chains, and even the work force. This affects all stages of the lifecycle, from upstream, midstream, and downstream to service in the field. A key benefit of digital transformation is operational intelligence. A vast quantity of historical data is available from most processes, but the challenge is to transform this huge repository of data into actionable intelligence to gain value from it.

Data generated by the oil and gas industry includes operational and historical work records, such as quality reports, process control history, operational deviations and variations, product blends and formulas, and many other records related to the production process. According to the US Bureau of Labor Statistics, the entire manufacturing and processing sector (including the oil & gas industry), has the most stored data of any industrial or business sector. This data, representing a virtual “digital brain trust,” comes in a wide variety of formats, both structured and unstructured, and needs to be aggregated, analyzed, and converted into actionable information.

Machines and Digital Twins

digital twin, as the name implies, is the virtual representation of a physical asset. It is an archive of historical information and real-time data. Historical information such as drawings, models, bills of material, engineering analysis, dimensional analysis, manufacturing data, and operational history can be used as a baseline when benchmarking machine performance. Similarly, real-time data acquired via integrated sensors or external sources can be used for condition monitoring, failure diagnostics, prescriptive analytics, predictive analytics, and so on.

The relative benefit gained from the digital twin will depend on the nature of the asset or machine and volume and quality of information retained throughout its lifecycle. As assets increase in complexity, demand for digital twins will grow rapidly. A unique aspect of the digital twin is its ubiquity across the product lifecycle. A genuine digital twin will contain information about its design, manufacturing, and service life. This raises questions about who best understands the digital twin and the data it makes available.

Digital transformation in the oil and gas industry would not be possible without the convergence of IT and OT to connect operational data with business processes for an end-to-end lifecycle asset or machine view. OT brings real-time connectivity and analytics, including applications for asset performance management, predictive analytics, asset integrity, and inspection-based risk scoring. IT brings business integration solutions that preserve existing IT and ERP investments. This includes applications to streamline logistics services; record historical and future planned maintenance; and generate reports for production rates and volume, inventory. IT/OT convergence supports end-to-end process excellence, with enterprise integration and visibility that leverages existing systems and the strengths of industrial products. These IT/OT-converged solutions should be built on a scalable enterprise-wide, platform that enhances operational uptime and accelerates digital transformation.

Digital transformation in the oil and gas industry and digital twin technology would also not be possible without an effective asset performance management (APM) solution. APM can save companies a significant amount of money by increasing maintenance efficiency and effectiveness, avoiding costly unplanned downtime, minimizing the need for scheduled downtime, and maximizing equipment availability, all while increasing safety. APM also provides a mechanism to reduce regulatory compliance cost and effort and minimize the risk of non-compliance.


In today’s increasingly complex global competitive environment, real-time information is vital to help the oil and gas industry at both the plant and enterprise levels make decisions that improve efficiency and effectiveness, and bring intelligence to their assets and machines, in effect, giving those machines a mind. This means being prepared to embrace digital transformation to acquire the needed information, even if it can potentially alter current processes.

Technologies, such as digital twin, IT/OT convergence and APM can provide the competitive edge that will determine the winners and losers in today’s volatile oil & gas market. Only operating companies that can manage their cost structures effectively will survive another price downturn in the future. Effective digital transformation can mitigate this risk to a significant degree.

Giving machines a mind

Digital Twin | Predicting asset performance | GE Digital

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In the Oil and Gas industry, predicting how assets and processes will behave is key to proactive maintenance and future productivity, and in driving new value. To succeed, one must understand all aspects of an Oil and Gas network and bring together the physical and virtual assets in a continuous loop of knowledge and improvement. Hear from ARC Advisory Group’s Analyst, Craig Resnick, as he shares his insights on why asset knowledge matters in IoT and steps Oil and Gas companies can take to bring together IT/OT. Learn what's at risk.


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About the author

Craig Resnick

ARC Advisory Group

Craig Resnick covers the PLC, PAC, HMI, OIT and Industrial PC markets as well as the Packaging, Plastics and Rubber Industries for ARC.  He is the primary analyst for many of ARC’s Automation Supplier and Financial Service clients.  Craig’s focus areas also include Production Management, OEE, HMI Software, Automation Platforms, and Embedded Systems.  Craig has 30 years’ experience in sales, marketing, product development, and project management in the industrial market, gained with major suppliers of PLCs, process control systems, power transmission equipment, and field devices.  Craig is a graduate of Northeastern University with an MBA and BS in Electrical Engineering.

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