In the oil & gas industry, predicting how machines and processes will behave is key to proactive maintenance, future productivity, and driving new value. To succeed, it’s important to understand all aspects of an oil & 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 Internet of Things (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. But it’s no easy task to break down the existing silos.
Emerging Technologies and Digital Transformation
Emerging technologies and digital transformation are rapidly changing the face of the oil & gas industry’s production systems, processes, supply chains, and even the work force. This affects all stages of the lifecycle - upstream, midstream, and downstream, and extends to services in the field.
A key benefit of digital transformation is operational intelligence. A vast quantity of historical data is available for most processes, but the challenge is to transform this huge repository of data into actionable intelligence to gain value from it.
The oil & gas industry is leveraging advanced embedded analytics to improve the operational state of plant assets and machines (such as rotating equipment) and improve maintenance to reduce unplanned downtime and extend service life. Digital transformation also enables a greater return on investment by providing opportunities to discover and understand the source of complex problems and identify options for improving upstream, midstream, and downstream processes. Concepts such as the digital twin enable analysis of real-time empirical data from the physical asset or machine, product, equipment, or system connected to the virtual design model. This supports predictive analytics for maintenance and prescriptive analytics for operational intelligence. Other technologies, such as augmented reality, provide tools to improve maintenance time and effectiveness.
What Is the Digital Twin?
A 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.
Knowledge gained by employing these methods can add value to the service life of the asset or machine in many ways: improving efficiency, reducing downtime, anticipating failures, etc., and provide valuable insight for continuous improvement. With the emergence of the digital twin, the notion of closed-loop design extends beyond the asset or machine and through the entire production lifecycle.
Digital Twin Mirrors Manufacturing Big Data
Data generated by the oil & 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.
The digital twin can also be employed to provide plant personnel with operational intelligence. By bringing together Big Data, statistical sciences, rules-based logic, artificial intelligence, machine learning, and the digital twin, manufacturers can discover and reveal the origins of the complex problems and determine options for resolving these. This move from predictive to prescriptive analytics is where the industry will realize the real payback from Big Data and advanced analytics.
Digital Twin and Machine OEMs
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.
Oil & gas equipment OEMs best understand information, such as engineering analysis, performance data, and the end-of-line performance of their assets. End users of these assets require this operational performance data. For the digital twin to be effective, either the information must be shared, or the oil & gas equipment OEM must offer a service-based business to monitor and optimize the performance of both digital and physical assets.
Implementing an enterprise asset management and lifecycle services strategy around the digital twin and within the context of an overall IIoT ecosystem can provide significant benefits for both the oil & gas equipment OEM and the owner-operator. For manufacturers of long-lifecycle products, ranging from gas turbines to pumps, after-sale service typically represents a significant differentiator. Implementing a digital twin service model for smart, connected products will improve service and support efficiency and enable a shift from reactive service to preventive, proactive, and remote service.
Digital Transformation Requires IT/OT Convergence
Digital transformation in the oil & 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 and 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 converged IT/OT solutions should be built on a scalable, enterprise-wide, platform that enhances operational uptime and accelerates digital transformation.
IT/OT convergence has led to a rapid learning curve for both IT and OT groups throughout the oil & gas industry. IT personnel often should learn what terms such as “real time,” “non-stop,” and “deterministic” mean in the operations context. OT personnel, in turn, are rapidly discovering the advantages of leveraging the latest IT-based approaches. This convergence is also helping plants address unplanned downtime, as 30-year-old control systems (DCS and PLCs) need to be upgraded. This convergence trend increases the demand for tighter integration and more information and advanced analytics. It also contributes to the adoption of IIoT, digital twin, edge, advanced analytics, artificial intelligence, machine learning, and augmented reality applications. These, in turn drive the need for high-availability systems to help eliminate unplanned downtime.
Asset Performance Management Key Digital Enabler
Digital transformation in the oil & gas industry and the digital twin would not be possible without effective asset performance management (APM). 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 the past, much time and effort was required to collect, aggregate, condition, and analyze the abundance of available data, much of which often got lost, rather than converted to meaningful information to manage the business. This is due in part to too many different software solutions, poor integration between them, a lack of openness and standardization, and difficulty creating and maintaining these integrations. Modern APM solutions can alleviate this. An approach built on data collection and analysis enables oil & gas companies to develop new techniques that result in greater efficiencies, improved safety, less unplanned downtime, better yields, less operational risk, and increased production flexibility.
These solutions are based largely on increased connectivity, use of open standards, and increasingly more capable platforms for predictive and prescriptive analytics. They enable oil & gas companies to move from largely reactive, conventional approaches for managing their critical production and automation assets to more effective proactive and predictive approaches. When well-integrated into the larger automation and information environment with reliable, secure communications, today’s APM solutions can help companies take advantage of opportunities in shale oil and gas, ultra-deep water, subsea, and other “unconventional” upstream applications. In addition, a wide variety of midstream and downstream applications can also benefit from securely integrating APM data with selected plant and enterprise applications designed to maximize asset availability, performance, and utilization to help improve overall business, environmental, and safety performance across the oil & gas industry.
In today’s increasingly complex global competitive environment, real-time information is vital to help the oil & 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.
Recent examples of digital transformation don’t seem so “new” anymore. The oil & gas industry is already using IIoT, cloud computing, mobile devices, social networks, advanced search engines, and Big Data analytics to create the information-driven enterprise. However, many companies still struggle to manage and fully leverage this abundance of data and information.
To see the desired shift and bring about concrete, quantifiable change, barriers and siloes must go away. IT providers understand the systems and many OT providers have the deep subject matter experience and access to physical assets. These separate worlds must now converge.
Technologies such as digital twins provide a window into both the problems and potential solutions. However, these technologies also require oil & gas companies to select partners that have expertise in oil & gas industry applications and the related physical assets and understand the importance of effective asset modeling. Data alone is not the solution. It’s critical to also know how you use the data for intelligence, decision making, and modeling to affect the needed business change.
Technologies such as digital twin, IT/OT convergence, and asset performance management, 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.
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Keywords: Digital Transformation, Artificial Intelligence, Digital Twin, IIoT, Machine Learning, Analytics, Augmented Reality, Edge, Big Data, APM, ARC Advisory Group.