Modern logical information and control models are the brains that run, monitor, maintain and secure operational facilities. The design objective of these logical systems is to optimize production and performance while minimizing supply chain problems. To achieve this critical objective, information flow, critical data, operational control points, as well as risk points are identified while fitting together the different compartments of these artificial models.
This cyber and logical representation of the physical asset environments, such as drilling and workover rigs, is displacing the traditional physical operational models in several domains of the oil and gas industry, including upstream, midstream and downstream. With the extended, geographically dispersed infrastructures of the oil and gas industry, the real-time communication and remote control capabilities are providing privileges to make more robust decisions that optimize deliverables. Additionally, as the added technologies, such as surveillance, are replacing the human element in tough locations, safety records are being boosted by reducing exposure to combustible, harming chemicals and off-road traffic.
Automation is often more efficient and safer than human intervention because it offers new operational capabilities, such as forward prediction, swift detection and reaction to events, and shuts down immediately if anomalous activities are indicated in data flow patterns or if signals are lost. For instance, onshore and offshore drilling operations in real-time monitoring and control centers that run land and subsea operations apart from the control room rely on analytics-driven strategies provided by the adopted intelligent systems to harness the full value of operational excellence.
This paper explores the design and function of the logical cyber representation of the physical asset environments, whether for drilling wells, producing wells, pipelines, or treatment facilities, to list a few components of the oil and gas supply chain. Physical assets and their controls are different for each compartment, and so are the communication networks and accompanying proprietary software. There are distinctive characteristics for each logical information and control network deployment architecture, depending on the operational requirements and levels of tolerance. This paper also highlights examples where such models have promoted solutions to mitigate uncertainty. For example, forward pore pressure prediction was applied while drilling along the minimum in-situ horizontal stress plane to predict what is ahead of the bit, improve wellbore stability and lateral trajectories, validate data, and prevent human error.
The analysis conducted showed that operational efficiency and cybersecurity compromise is essential for business success while constructing the information and control models. The paper discusses three useful tools that assist in promoting integrated cybersecurity for artificial models. The three tools are safety instrumented systems, decision tree, and information risk management.
