Observational analysis shows enhanced operational performance in subsea pipeline networks using digital twin, indicating better design choices and condition monitoring.
A subsea pipeline network is part of the complex field development infrastructure, which includes different sub-systems, from surface to seabed. The subsea pipeline network is a pivotal component, from well-head to manifold, then to export line or riser base. The satisfactory performance in operation, in both shallow and deep waters, depends on flow assurance and pipeline integrity. High pressure high temperature (HPHT) aggressive fluids from wells, often multi-phase, are challenging. At design stage, this is commonly tackled by using engineering models to predict performance in operation. These are inter-linked, increasingly high-fidelity, possibly allowing for multi-physics simulation. Nowadays, field development is based on a combination of engineering models and data from experience through installation to field monitoring, with the aim of building a digital replica or twin of the system on a software platform, which use is assisted by data analytics. The scope of the paper is to show how a digital replica (digital twin, DT) of the inter-field pipeline network, likely part of the digital replica of the full system, can positively impact both design choices and operational strategy. DT are guided by safety targets and operational performance to be met, the latter often input/output to/from other sub-systems. The paper deals with a new approach to subsea pipeline networks, belonging to complex field development infrastructures, which is based on extensive condition monitoring concept. There is a digital replica of the infrastructure, both functional and structural, conceived at the early design, improved during installation and pre-commissioning, continuously updated in operation. DT is unique to deal with complexity, allowing for testing flexibility and reliability of adopted solutions at design stage, with the aim to timely and rationally tackle those variations that might deeply affect life-cycle performance.
No takes yet. Share an insight, caveat, or question.
Bruschi et al. (2025) studied this question.
Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context: