Field-Scale Digital Twins for Offshore Wind Energy

FieldTwin offers three powerful benefits to accelerate windfarm development:

Digitization of data empowers engineers to make better decisions in real time. Information scattered across desktops, servers or laying around on the back of a cocktail napkin can now be collected in a single space, creating a rich library of searchable, instantly retrievable datasets to make more efficient workflows for teams across the room or across the globe.

It’s one thing to see data on a spread-sheet, but another thing entirely to be able to visualize data in real-time, 3D models improving efficiency and minimizing risks through greater comprehension. Because subsea bathymetry data, Met Ocean conditions, marine traffic routes and other asset information is readily available and displayed, multiple scenarios can be quickly explored, easily evaluated and allow design team to reach their goals faster.

Perhaps the most powerful benefit of working in the FieldTwin environment, is the power to collaborate. Breaking down silos to streamline design work-flows and create greater collaboration within organizations and their outside partners, accelerates the concept phase to deliver better, more timely outcomes at a lower cost.

Winds of Change

As with oil and gas uses, FieldTwin provides the same benefits when developing offshore windfarms. Like its onshore cousins, the offshore wind sector uses a range of point solutions within workflows, often dependent on spreadsheets or manual import/export processes into GIS tools for wider context and collaboration. Applying a digital twin solution in the concept and design phase initiates the data model early on and enables a consistent digital thread throughout the project lifecycle. This holistic approach supports integrations with high-fidelity simulation workflows in detailed design phases or even operational digital twins for commissioned projects.

Empowering
Sustainable Decisions

FieldTwin boasts a more efficient user interface with streamlined controls to create a more productive digital working experience, giving users the ability to achieve meaningful results in a fraction of the time. Multiple scenarios can be modelled and iterated using the latest data sets and the best-case solution can be realized prior to major capital investment. The digital twin can further evolve to support smart operations, real-time measurement and serve as a basis for applying machine learning algorithms for predictive maintenance support. The ultimate impact of digitalization can be realized to reduce project costs, uncertainty, and the time to first energy.