Industrial Automation Magazine India

AR and VR technologies are helping users move to a more visual way of working

Published on : Monday 03-07-2023

Peter Reynolds, Contributing Analyst, ARC Advisory Group

What are the latest technology trends dominating the process industries in general, oil & gas in particular?
Industrial process manufacturers and especially oil & gas around the world are entering a period in which new digital technologies augment people and processes to an unprecedented degree. New, commoditised computing resources in the cloud (and at the edge) and artificial intelligence (AI) are changing how people work. Approaches such as the Industrial Internet of Things (IIoT) and Industry 4.0 have helped pave the way for digital transformation across a broad swath of industrial sectors.

Digital transformation spans industrial products, operations, value chains, and aftermarket services. It augments people and knowledge through expanded use of sensors, data, and analytics. ARC Advisory Group believes that most industrial process companies globally will undergo a digital transformation to some degree or other, with many already actively piloting advanced technologies.

How are robotic applications enabled by AI helping oil exploration and extraction with processes like well imaging, etc., offering visibility and transparency across the entire oil & gas value chain?
New technology can provide detailed logging-while-drilling using high-definition (HD) images for reservoir description and completion optimisation. Oil field services companies can develop detailed fracture characterisation and completion optimisation in conductive drilling fluids for all well types, including horizontal and highly deviated wells. In unconventional and carbonate reservoirs it is critical for geologists to fully understand the fracture networks that may challenge drilling operations and those that will contribute to production, thus helping to prevent drilling risks, optimise completion design and potentially increase production. The HD service has been field tested extensively in reservoirs in the Middle East, Europe and Africa, as well as unconventional reservoirs in North America. More than 45 job runs have been completed, confirming that high-definition images can be obtained reliably in conductive mud environments while drilling in oil and gas carbonate, sandstone and unconventional reservoirs.

Also, time-lapse techniques have enabled reservoir managers to deduce how hydrocarbons move through the reservoir. 4D seismic imaging has seen success, particularly where gas is involved. Cross-well tomography has enabled inter-well resistivity imaging with enough precision to allow the siting of infill wells or steering of side tracks to improve sweep efficiency. The biggest disadvantage to time-lapse techniques is that they are reactive processes; achieving maximum reservoir productivity requires predictive processes.

Can AI be implemented in the Oil & Gas Industry for optimum performance and safety?
Across the board, owner-operators are realizing that the largely reactive nature of conventional asset management strategies alone is inadequate for maintaining the equipment uptime necessary to maximise production and profitability. Increasingly, Artificial Intelligence (AI), or also known as predictive maintenance is recognised as a new solution that helps provide much earlier warning of impending breakdowns. Solutions can now utilise machine learning, a field of computer science that does not require explicit programming. Unlike traditional programs that use rules, statistical models, and engineering equations, machine-learning based systems learn patterns in data and use them to predict future outcomes. State-of-the-art machine learning systems automate these learning, adapting, and predicting activities.

While statistical techniques, engineered equations, or other model-based prediction approaches can sometimes detect impending asset failure, they do not provide as much forewarning of asset degradation and failure. Producing such methods is intensely difficult and the models can contain inaccuracies, deliver many false positives, and are extremely constrained. The methods also cannot capture the biggest root causes – upstream process distress that results in equipment operating outside safety and design limits. Without early heads up, continuing damage leads to costly breakdowns

Can AI, AR/VR technologies improve productivity, safety and quality for process plants located remotely?
Regarding Augmented Reality (AR) and Virtual Reality (VR), these technologies are helping users move to a more visual way of working. AR uses displays, cameras, various types of sensors, and software to augment the user’s real-world environment with artificial perceptual experience. Most AR applications use smart devices such as smart phones, tablets, and smart glasses to overlay digital information and graphics onto the user’s real-world view. Pokemon Go and Snapchat filters are some of the most popular AR applications in the consumer world. While AR is not a new concept, advancements in areas such as computer vision, sensing technologies, data storage, displays, and software technologies have now made AR a much more practical solution for consumer, commercial, and industrial adoption. Although still in the early stages of the AR revolution, we are seeing an explosion of AR startups, thanks to all the interest by the venture capital (VC) world. The oil & gas industry is faced with an impending crisis related to the rapidly approaching mass exodus of skills from the industry with few young people wanting to take up the gauntlet. AR is proving to be an effective tool to help the industrial workforce. With remote augmented communication, and augmented operations, AR can help the industry better address the impending skills gap challenge.

What are the benefits edge computing brings to process industries vis-à-vis the traditional model?
When digital transformation first emerged as a trend, various suppliers positioned their offerings as “ready for Industrial IoT,” or “ready for Industry 4.0.” Initially, this was largely marketing speak, with few if any sustainable strategies to back it up.  However, over time, industrial automation suppliers, equipment manufacturers, and end users have developed and started to implement effective digital transformation strategies.

The term “Industrial IoT edge” has also emerged. This recognises that cloud-based digital transformation strategies require data from and access to the physical devices, assets, machines, processes, and applications that reside on the factory or plant floor.  The “edge” was initially viewed as the place where industrial network infrastructure devices like switches, gateways or routers, as well as endpoint devices, connected to the Internet. Internet connectivity and automation protocol conversion were the main tasks.

Since then, the role of the Industrial IoT edge has developed rapidly. Today, edge functionality ranging from data preprocessing to artificial intelligence (AI) is being integrated into a broadening variety of systems and intelligent devices. These include traditional automation devices like PLCs as well as edge servers hosting a local cloud.

At the same time, new methods for configuring and programming Industrial IoT edge devices have emerged. Modern smartphones provide a virtual blueprint for how the firmware and application software in future automation systems can be deployed efficiently to devices and updated from a central server. High-level programming languages and configuration approaches from the IT world are also descending to the edge in the form of container support and hardware and software virtualisation.

What levels of precautions are required to prevent Cybersecurity attacks on sensitive installations?
Today the whole problem is getting bigger and harder to resolve and the threat landscape is getting tougher and more sophisticated. While the outside threat environment is challenging, there are also a lot of changes happening within plants and within the industrial infrastructure that are creating more vulnerabilities. It's not just insecure devices; it's more connectivity and things like that. Also, there’s a lack of human resources. Although there are more protections, the risks for sophisticated attacks are also increasing. Due to global political unrest, cyber warfare is a reality. This goes beyond attacks on plants – it attacks IT systems and entire supply chains. Changes are happening with end points. The number and diversity of end points is just exploding and there is open connectivity.   

So, now there are different levels and new devices to manage. More technology is being infused into solutions and the suppliers reassure the end users that they will manage the equipment and the security aspect. But this might seem “unmanageable” for the end user as the device was simpler to handle before and he knew whom to approach for patches. Previously, trouble spots could be isolated and fixed. But with integration of IT, OT, IoT, mobile devices, etc., you have one device that can talk to 10 different sources of information at one time and any of those could be at risk, creating pathways for attacks. The value of digital transformation efforts is to change the status quo and have one consistent security policy across all those domains and across those applications.  

(The views expressed in interviews are personal, not necessarily of the organisations represented)

Peter Reynolds is an Analyst and Consultant for the Energy and Chemicals sector, ARC Advisory Group. He brings 30 years of professional experience in manufacturing engineering automation, optimisation, and technology strategy. Peter publishes market studies and papers covering industrial software and consults with oil & gas and chemicals companies to advance digitalisation initiatives on diverse topics such as sustainability, process optimisation, and asset performance management. He frequently speaks at conferences and events throughout the Americas, Europe, and the Middle East. Before joining ARC, Peter was Refinery automation & engineering manager and Director responsible for the Enterprise IT, Supply Chain strategy and multi-year plan for one of Canada's largest refining and marketing companies.