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ARC View - Data Quality for Effective Digital Twins in Oil and Gas


Effective digital twins need high-quality data in real time for common applications like predictive maintenance and optimizing the operational performance of equipment. An intelligent asset inspection program becomes an imperative for success.

A digital twin, by definition, replicates attributes of a physical asset. Digital twins enable oil and gas companies to respond with fact-based decision support for the industry challenges, which can be particularly helpful considering the added adverse impact of the COVID-19 pandemic.

A digital twin needs data for analytics, prediction, and automation. For a useful twin, the data must be of high quality, verified, and referenced. To operate in real time it needs current data and models.

Predictive maintenance using data from a particular piece of equipment has been a key driver for adopting digital twins. Once a digital twin of a physical asset has been deployed, it is vital to keep it current to be effective for real-time decision support.