Knowledge-based Digital Twin for Oil and Gas 4.0 Upstream Process: A System Prototype

Abstract

The Industry 4.0 initiatives have triggered the concept of Digital Twin (DT). A DT is a virtual replica of any physical object like a machinery, an equipment or a manufacturing process, that accurately reflects the state of the object under observation. In an asset intensive industry like Oil and Gas (O&G), DT provides significant value addition. DT, being a digital representation in the cyber space of the Internet of Things (IoT) ecosystem, enables simulation, experimentation, and personnel training in a safe environment, without disrupting the actual physical process. In this paper, a knowledge based digital twin prototype for the O&G upstream, using generalized IoT stack & schema-based ontologies has been proposed and built. In comparison with the existing systems, the proposed prototype has the advantages of being open sourced, microservice based, context aware, and it supports ontology. The architecture and implementation details, along with the sample test results with real data, showing the working and efficacy of the system are presented. A use case of proactive site visit scheduling, resulting in operational improvement is detailed.