December 2018

Industrial Internet of Things (IIoT)

Industry 4.0 in the oil and gas business

The operational cost structure of oil and gas companies is very capital- and labor-intensive.

Lahti, T., Saurus, L., Hellgren, N., Neste Engineering Solutions

The operational cost structure of oil and gas companies is very capital- and labor-intensive. The long-term peak of oilfield production capacity may be reached when the world is actively chasing alternative solutions for fossil fuels. Everyday operations are constantly challenged by the scattered and continuously evolving supply and demand of raw materials and end products.

To ensure profitable and sustainable business, the efficient delivery of on-specification products to the right location at the right time is key. Therefore, flexibility and real-time visibility of every operational function is a must. As legislations tighten, environmental awareness rises and customer requirements evolve, oil and gas companies must store and ship more raw materials and end products while simultaneously doing it faster, with minimal energy consumption and in a safer manner.

Inventory control and capacity optimization play essential roles in the global competition between service providers. Companies must invest in production automation and optimization to respond to increased demands from both legislation and customers.

Evolving technology and digitalization for changing needs

Digitalization, evolving technology and global mega-trends are generating new business areas and reshaping existing businesses and operational methods. Many governments are mandating reductions in crude oil consumption. Digitalization has enabled new solutions and opportunities to utilize materials previously seen as waste. Residues and waste form a continuously expanding raw materials source for recycling industries. Including wind, biofuels and geothermal energy, new power-generating capacity from renewable energy dwarfed the 70 GW of net new capacity from fossil fuels in 2017.1

FIG. 1. To make decisions efficiently, logistics operators must be aware of the state of the entire supply chain and the anticipated discharge time, and communicate that information accordingly, so that logistics can be cost-efficiently and optimally arranged.
FIG. 1. To make decisions efficiently, logistics operators must be aware of the state of the entire supply chain and the anticipated discharge time, and communicate that information accordingly, so that logistics can be cost-efficiently and optimally arranged.

However, the circular economy requires traceability for transparency of raw materials throughout the supply chain. Inconsistent and distributed material sources will introduce new challenges to the entire production chain. Information on the quality and quantity of batches must be available, starting from upstream, to enable the efficient and appropriate collecting and further processing of waste. To manage these demands, modern data acquisition and refining capabilities in the form of intelligent digital solutions on top of traditional operational automation are required.

Biofuels for traffic is one application area for the circular economy. This objective is driven by the EU, which aims to reduce transport emissions and dependence on oil. The EU is targeting an increase in the use of renewable energy in transport by 2020, so that its share will amount to 10% of energy content of fuels. Legislation for 2030 is still underway.2 However, collecting scattered raw materials in a timely manner generates additional challenges for logistics and storage systems. Combining numerous small resource flows cost-effectively requires efficient logistics. To make decisions efficiently, logistics operators must be aware of the state of the entire supply chain and the anticipated discharge time, and communicate that information accordingly, so that logistics can be cost-efficiently and optimally arranged (Fig. 1).

To achieve these goals, robust autonomous systems for remote monitoring of material collection and transport are needed. The used material tracking and sensor systems must be easily installable and work accurately and reliably in any location and environment. Storage facilities must be able to easily communicate with their centralized management systems, which, in turn, must be able to easily—and in an interoperable way—communicate with enterprise operational systems.

As in other industries, the Industrial Internet of Things (IIoT) has a fundamental role in the digitalization of the physical world and the creation of data flows for more sophisticated operational intelligence systems. That generates a foundation for new artificial intelligence solutions to advise human operators in the field and control rooms, and to collect feedback for operators and process development purposes.

Evolving technology introduces new challenges

Recent technological advances in the IIoT and data analytics are moving quickly. As new possibilities are introduced, business requirements are also changing rapidly. This drives oil and gas companies to hesitate and delay their investment decisions, even if the need for operational predictive maintenance and intelligent real-time analytics, decision-making support and machine learning applications already exist.

FIG. 2. Flow of a common process industry information highway.
FIG. 2. Flow of a common process industry information highway.

Despite the rapid technology development, it is still possible to secure these investments by considering a few fundamental factors: digitalization supplier independence, industrial solutions movement toward Industry 4.0 and compatibility with relevant technologies, such as OPC Unified Architecture (UA) and IIoT system management (authentication, authorization, scalability and upgradability). These factors are essential for meeting Industry 4.0 specifications and reference architecture model (RAMI4.0) requirements in the future.

Many companies have already established plant information management system (PIMS) software platforms, often enterprise-wide, to collect information from multiple plants (Fig. 2). Operating companies cannot afford to rip and replace these systems. Therefore, it is essential that new operational intelligence solutions that introduce additional data layers are vendor-independent and technologically in line with existing and upcoming IT and OT infrastructure.

Edge computing

Data analytics is increasingly moving from cloud environments to the edge of the source network or to the outer boundary of the cloud. In many cases, this paradigm shift is driven by an amount of data becoming too large for transmission, or requirements for response timeliness growing too strict for traditional cloud-based solutions. Confidentiality of information may also compel customers to request the processing of information to be near the production network (i.e., on the edge). Cloud and edge solutions boost platform independency and set strict requirements for information security.

In the future, only processed knowledge based on raw data will be transferred to the cloud. This will be more valuable for business operations, but it may expose sensitive details of business operations if not properly protected. One of the best approaches for ensuring secure data transfer between plants, edge solutions, and the cloud environment is the utilization of established standards, such as the Industry 4.0-endorsed OPC OPC UA. IoT and edge device remote management and control also play a key role in ensuring system integrity and business continuity.

ML and AI drive intelligent solutions

The biggest drivers behind the IoT in many industries are the vast possibilities of artificial intelligence (AI) and other advanced analysis solutions applications, as they need big data for models and related estimates. In many future applications, the IIoT will ultimately be a tool for gathering the required data for AI solutions.

To guarantee strong customer experience for these AI applications, wide know-how of applied industries and specialized verticals are needed from developers. McKinsey3 states that, in the final game, industrial customers want comprehensive solutions that do not need to be combined from partial solutions. That encourages businesses to build operational intelligence layers in collaboration with turnkey solutions providers having IIoT, analytics and machine learning capabilities as integral parts of the overall solution. Customers requesting shorter time to market do not want to start several projects to build semi-finished technologies or SW libraries, coordinate integration of multiple solutions with varying needs and purposes, or think about how to acquire data for the business case.

Industry 4.0

Industry 4.0 provides a vision of how different players in the field of process industry will work in a more customer-driven, more automated, more flexible and more efficient way. The intelligence of systems drives automation, people and organizations digitally. Industry 4.0 and digitality enable the setup of new, more productive processes with enhanced visibility and management capabilities. To ensure these comprehensive benefits, Industry 4.0 is concerned with all processes of the company: production, procurement, sales and logistics, to mention a few.

Parts of Industry 4.0 already exist, while others are still in the hype phase and will be realized over the longer term. This causes uncertainty around what is already available for investment, and what is not yet worthy of attention. Existing technologies can already enable the digital control of small delivery lots with transparency and excellent response capability.

Instead of relying on ad hoc solutions focused on transmitting information despite technology limitations (e.g., e-mailing spreadsheet documents to all related parties, or designing custom interfaces to related databases), modern, Industry 4.0-enabled applications embrace the possibilities of uniform communications and the minimized overhead of well-designed protocols and interfaces.

Ease of analysis, experimentation and synthetization of collected data presents previously undiscovered knowledge and possibilities. As an example, equipping all related systems with standardized interfaces enables data analysts to easily connect their tools to multiple sources of data without the additional technical and cognitive overhead of dealing with a wide variety of protocols, data formats and service interfaces. To answer these demands, comprehensive advanced analytics and intelligence solutions emerge, forming an intelligent operational layer between ISA-95 layers three (manufacturing operations management) and four (business planning and logistics). These solutions also serve a multitude of needs, from predictive maintenance to AI advisor systems to lifelong learning for operators.

Distributed industrial data-gathering systems comprising multiple parties and vast quantities of data face security-related challenges common to distributed big data systems: data integrity, availability and confidentiality. Finding a balance between communication confidentiality and system availability for all applicable parties is critical to successful integration. Industry 4.0 seeks to mitigate collaboration-related issues by recommending that all systems communicate in a standardized fashion, specifically OPC UA.

OPC UA

The chosen communication and information model for Industry 4.0 is OPC UA. While it is not a new protocol, it utilizes established best practices of the industry. OPC UA can be briefly described as “a platform-independent, service-oriented architecture that integrates all the functionality of the individual OPC Classic specifications into one extensible framework.”4 It emphasizes open transports, high security and a more complete information model than the original OPC. OPC UA provides flexible and adaptable mechanisms for moving data between enterprise-type systems and controls, monitoring devices and sensors that interact with real-world data.

Since its introduction in 2008, OPC UA has gained a large adaptation rate among industry solution providers and businesses. The protocol has received multiple revisions, reflecting fast-paced changes on the technical front and customer needs. As stated by Craig Resnick, Vice President of ARC Advisory Group, “OPC technology has become a de facto global standard for moving data from industrial controls to visualization up to MES/ERP and IT cloud levels.”5

OPC UA specifies a service-oriented communication protocol and an extensive information modeling system that are platform- and programming language-independent. This ensures that all data can be seamlessly transferred between different systems, from small embedded devices to large mainframe servers, while simultaneously maintaining the consistency of interpretation. In addition, OPC UA comes with built-in security features, including data encryption and signing based on proven public-key cryptography, user authentication and authorization to provide data integrity and access management, and audit logging to ensure traceability of operations.

OPC data connectivity standards play an important role in control automation today, and will play an important role in future IIoT- and Industry 4.0-based solutions. This is acknowledged by an in-depth ARC Advisory Group report from 2018, which stated: “IIoT-enabled edge devices embedded with OPC UA are being leveraged as an ‘asset gateway.’ This can help organizations maximize their return on assets (ROA) by helping ensure that their automation investments are scalable, future-proof, adhere to open standards and integrate with existing assets to avoid having to ‘rip-and-replace’ current automation infrastructure.”5 By combining OPC UA with time-sensitive networking (TSN) technologies specified by the IEEE, any cyclical and non-temporal data transmissions can use the same physical network.

For vendors, a certification program by the OPC UA Foundation proves that certified products have been tested in an OPC Foundation-accredited certification lab and have met or exceeded requirements related to compliance, interoperability, robustness, usability and efficiency. The benefits of using OPC UA-certified products include faster mobilization and configuration, proven reliability and interoperability and minimal integration risks. Using a certified product is an investment in quality. Fulfilment of Industry 4.0 key requirements gives investors the possibility of focusing on the business possibilities presented by their investment.

Security

Especially when it comes to protocols designed for industrial communications, security has traditionally been an afterthought, at best. In the era of small networks enclosed in guarded industrial facilities, this was a viable approach. However, the distributed nature of modern industrial networks presents new risks and attack surfaces that must be considered in the development and management of solutions.

The seemingly trivial solution to mitigate these risks would be obfuscation by using proprietary or custom-made protocols, interfaces and security solutions. While the “security by obscurity” approach is viable against unskilled and opportunistic attackers relying on one-size-fits-all attacks, it is ineffective against targeted attacks by capable individuals and organizations. Well-equipped attackers are a serious threat, especially to organizations operating in fields critical to functional nation-states: energy, healthcare, water supply and food production, to name a few.

Following open standards and industry best practices, applying proven security solutions (e.g., proper encryption, strict access and identity management, defense in depth) and identifying the critical role of security as a process in connected systems, increase an organization’s chances of survival in the era of interconnected systems.

Industry 4.0-era production solutions

The easiest and most cost-effective way to implement and modernize operational process management is to transfer the data wirelessly and directly to a cloud service via Industry 4.0-standard OPC UA communications. Instead of using unprotected legacy communication protocols across a wide area network (WAN), such as internet, IIoT and edge solutions offer more modern communication techniques (such as MQTT, OPC UA, AMQP, CoAP and TSN), designed for secure and efficient network communications. These technologies constitute a solid foundation for ISA 95 Level 3 data communication.

One of the main business differentiators presented in the Industry 4.0 initiative is the horizontal integration across company and business area borders. To ensure collaborating parties’ trust in the distributed system, it is essential that the management and utilization processes of the system are transparent. This enables any stakeholder to verify both network behaviors and utilization compliance with given policies and contracts, and to audit the behavior and security of the network.

A major obstacle in legacy systems that prevents cross-organizational collaboration is the heterogeneity of devices, networks and communication protocols. Utilizing standardized data models and communication protocols enables faster and more predictable integration of different manufacturers’ devices. Open, clearly defined technologies also increase the transparency and verifiability of systems, and lower the threshold for starting new collaborations or exploring new business opportunities.

Modern communications, combined with appropriate management processes, enable the direct digitalization of operational management. From the edge and the cloud, the data can flow to plant automation systems, enterprise resource planning (ERP solutions, such as SAP), or other relevant management systems related to the use case. Central cloud connection allows smooth operational shifts between different plants, as operative focus can be moved where free capacity exists in the system when needed.

According to ARC, “It is crucial for the industry to build modern analytics tools and deploy data-enabled knowledge. Doing so will drive new levels of efficiency into upstream, midstream and downstream operations while addressing the human skills gap.”6

Seamless production chain

Accurate inventory in real time—i.e., exact tank levels and line fills, with predictions of future inventory—can be seen as part of a wider production optimization context. High-level production optimization can benefit from the information provided by plant-wide solutions, enabling the optimization of product specification, energy requirements or production costs.

Solutions packages from cutting-edge providers form a new basis for the intelligent operational level with existing historian data and IoT capabilities. In addition to data collection, storage and analysis capabilities, solutions are available for plant-wide production optimization, operator trainings with simulators and serious games, as well as advanced decision support and flow dynamics simulation, to name a few examples. HP

Literature cited

  1. Chestney, N. and A. Doyle, World Economic Forum, “Renewables grew twice as fast as fossil fuels in 2017,” April 6, 2018.
  2. “Biofuels for traffic,” Petroleum and Biofuels Association Finland, online: http://www.oil.fi/en/traffic/biofuels-traffic
  3. Batra, G., A. Queirolo and N. Santhanam, “Artificial intelligence: The time to act is now,” McKinsey & Co., Advanced Electronics, January 2018.
  4. OPC Foundation, “Unified Architecture,” online: https://opcfoundation.org/about/opc-technologies/opc-ua/
  5. Resnick, C. and D. Clayton, “OPC technology well-positioned for further growth in tomorrow’s connected world,” January 10, 2018, online: https://opcfoundation.org/wp-content/uploads/2018/02/ARC-Report-OPC-Installed-Base-Insights.pdf
  6. Guilfoyle, W., “Building effective analytics for oil and gas,” ARC Advisory Group, June 1, 2018.

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