How to Achieve Project and Operational Certainty Using a Digital Twin
- Alistair Douglas (Total E&P UK Limited) | Vicente Rios (Emerson Automation Solutions)
- Document ID
- Society of Petroleum Engineers
- SPE Offshore Europe Conference and Exhibition, 3-6 September, Aberdeen, UK
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Safety, Modelling, Certainty, Digital Twin
- 1 in the last 30 days
- 207 since 2007
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A Digital Twin is a software representation of a facility which can be used to understand, predict, and optimize performance to help to achieve top performance and recover future operational losses. The Digital twin consists of three components: a process model, a set of control algorithms, and knowledge.
Usually the time for commissioning a project exceeds the initial estimations, therefore delays in project completion are quite common. This is often because ICSS testing is done on a static system which does not account for how the system will react dynamically to certain scenarios such as start-ups and shutdowns. Issues such as configuration errors, loop behaviors, start-up over-rides, dead-lock inter-trips and sequence logic are difficult to predict and are impossible to anticipate during static testing. Such delays lead to higher costs and therefore reduced revenue.
This paper aims to describe the most innovative approach to Project & Operational Certainty, which addresses these issues by using a Digital Twin for commissioning support and training. One successful use of this approach was in the Culzean project, an ultra-high-pressure high temperature (UHP/HT) gas condensate development in the UK sector of the Central North Sea. A high-fidelity process model was built and fitted to the actual plant performance based on equipment data sheets. This was connected to ICSS database and graphics, offering a realistic environment, very close to the one offshore, which had the same look and feel for the operators.
Dynamic tests conducted on the Digital Twin predicted issues on the real system, which enabled potential solutions to be tested, leading to a significant decrease in the time spent and cost during commissioning. All the operating procedures were dynamically tested, which enabled us to correct errors, saving time before First Gas. Additionally, all CRO (Control Room Operators) and field technicians were trained and made familiar with the system months in advance, aiming to avoid future unnecessary trips during First Gas.
Finally, all the control loops were fine tuned in the Digital Twin and parameters were passed to off shore, to be used as first starting point. It is expected that these parameters will be very close to fine operational points, as the model used is high fidelity model and very close to real system offshore.
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A. Parrott, L. Warshaw, 2017, Industry 4.0 and the digital twin: Manufacturing meets its match, Deloitte University Press, https://www2.deloitte.com/insights/us/en/focus/industry-4-0/digital-twin-technology-smart-factory.html