For example, an operator may want to analyse the data relating to a particular piece of machinery or measurement system and anticipate forward resource requirements. They can do this by feeding the time-series data into AI and analytics software and using it for real-time optimisation of the assets. That data can then be exchanged between operatives working in different teams or areas of the business as workflows to serve a variety of purposes and outcomes.
5 Steps Towards IT/OT Convergence
Fundamentally, Oil and Gas executives want to achieve two broad goals. On one hand, they want to improve time to value, cutting through the inefficiencies that slow down operations and produce inertia. On the other, they want to minimise risks so that outcomes can be achieved in a safe, secure and compliant way.
Closed or disparate systems lead to friction and disconnect, which slows operations and reduces the quality of decision making. The integration of systems, on the other hand, means you can get to the outcome far quicker and with far greater reliability.
There are five key steps that Oil and Gas executives can use to enable the integration process and ensure information can flow freely across IT and OT systems.
1. Identify and align critical data sets
One distinctive feature of a typical Oil and Gas production environment is that there are multiple systems working in tandem at different parts of the process. These may have been provided by a range of different OEMs and software vendors, each operating on different protocols and generating data in different formats. If you lack a consistent set of standards for integrating these different sources, then you end up with data in siloes which are disconnected and cannot communicate with one another. This inevitably leads to fragmented operations and less efficient processes.
Start by identifying the different forms of data available and develop ‘data-centric’ strategies – for example, how can data from upstream and midstream operations be collected, cleaned and utilised across multiple purposes or workflows? Then consider what barriers currently exist in getting greater utility from the different data sets, such as bottlenecks in the sharing of workflows between relevant parties. Remedying these frictions at a systems level helps us to find alignment between the different applications.