Spatial Digital Twin Architecture for the Field Design Process of Oil and Gas Projects in Australia

Sijan Bhandari (), Dev Raj Paudyal and Sreeni Chadalavada
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Sijan Bhandari: School of Surveying and Built Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Dev Raj Paudyal: School of Surveying and Built Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia
Sreeni Chadalavada: School of Engineering, University of Southern Queensland, Springfield Lakes, QLD 4300, Australia

Land, 2024, vol. 13, issue 7, 1-32

Abstract: This paper aims to develop a conceptual system architecture utilising the land survey data model (LSDM) to manage spatial information associated with field design for oil and gas projects. A widely accepted design science research approach was used, which includes five phases as the foundation phase, development phase, implementation phase, demonstration phase, and validation phase. The developed system architecture consists of five major components: data classes and categories, a data management system, map servers, a visualisation system, and users. This study utilised Unified Modelling Language (UML) diagrams, such as use case, and sequence diagrams to illustrate the relationship between the developed architecture and the field design process and demonstrate workflow and its interaction with stakeholders. A prototype was developed to demonstrate the viability of developed conceptual system architecture. Furthermore, a study area situated near a mining town Chinchilla at the Western Down Regional Council, Queensland, Australia, was selected to validate the prototype in the real world. This study provides a theoretical contribution towards Industrial Revolution 4.0, aiding enhanced decision-making processes within the oil and gas industry and the more effective management of energy resources.

Keywords: digital twin; oil and gas; system architecture; land survey data model; mining geomatics (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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