Previously, refinery operations were left to chance and malfunctions were only identified when something had already gone wrong. The IIoT has allowed operators, engineers and plant managers to attain operational value through capturing and analysing data which allows them to identify potential risks and act accordingly before it becomes a problem.

Leading technology licensor Honeywell developed a cloud-based service which allows oil and gas companies to monitor, predict and improve their plants performance. The service is meant to help refineries reduce downtime and improve underperformance. 

By implementing the IIoT, refineries can improve their ability to predict when something is about to go wrong, they can gain access to operations in real time, and they can reduce maintenance cost. However, one of the limitations of IIoT is exposure to security risk. Therefore, a company that adopts the IIoT should make sure all those devices connected are protected from inter-based software, network-based, and hardware-based attacks.

The opportunity presented by the IIoT as mentioned above, also presents a cybersecurity threat for oil and gas companies. Because of the critical nature of the oil and gas industry, it is more prone to cyber threats. Earlier this year Oiltanking Deutschland GmbH & Co. KG had to operate at limited capacity after their IT system was attacked. The implementation of the right cybersecurity can help refineries limit or avoid internal and external threats to their information technology and operational technology. By protecting their systems and operations, these companies are protecting their ability to function and deliver, their integrity, confidentiality, availability.

Artificial Intelligence can be defined as a machine or system that is programmed to perform tasks that are usually performed by humans.  These machines or systems can improve themselves from the information they gather. AI can assist in driving analytical decision making for the refinery engineers and operators. Considering that data is a critical driver in the oil and gas industry, the data obtained in real time will assist in the creation of ideas that align with the needs of the business. Furthermore, the predictive analysis encoded in AI can help reduce well/equipment downtime, which would normally cost refineries millions. One of the many oil and gas companies that has taken a step to invest in AI is Shell. In 2018, Shell started using machine learning at their Shell Energy and Chemicals Park Rotterdam (formerly Pernis refinery) to improve analysis and data processing. The model was produced to forecast failures in control valves and give workers the green light to execute maintenance or adjust operating conditions when necessary.

Like AI, Robotics is the design, manufacturing and use of machines to perform tasks that would conventionally be performed by humans[1]. The aim of these robots is to help humans achieve their objectives, as per their sector needs. In the downstream sector, they can be adopted to improve safety, enhance efficiency, and improve environmental performance.

In the long-term robots can go into more dangerous and difficult to reach territories that humans cannot reach. This can reduce the costs to the company by saving on safety measures that would have been taken if human capital was involved.

The implementation of robotics can also help oil and gas companies increase their efficiency and improve productivity[2]. Robotics can identify inconsistencies and potential issues which allows the workforce to be proactive in fixing the problem. Robots can be seen as an assistant as opposed to a replacement, as their presence will allow the workforce to continue to be productive in their roles whilst the robots’ play their role in other parts of the operations.

Mitsubishi Heavy Industries, Ltd. (MHI) in an agreement with ENEOS Corporation jointly developed a plant inspection robot that would be used to collect data in a refinery. The robot has been configured with explosion proof functions to minimise the risk causing an explosion or fire caused by electric sparks or heat. At the time of testing, the second – generation EX ROVR prototype moved through the ENEOS oil refinery collecting data via various sensors, confirming its ability and its adherence as a plant inspection robot[3].

Refineries need to continuously ensure that their operations are efficient so they can produce the right amount of output. By digitising their operations, they will be able to enhance their efficiency through improved data analytics and increased productivity. This is not to say that robots and systems should replace the workforce, but it should rather be seen as a helping hand that will contribute to the skills of the workforce. Most refineries can do better than what they are doing now and this will be explored at future WRA events on how the right investments can be made.