Field-Scale Digital Twins for Offshore Wind Energy

For years, the oil and gas industry has benefited from transforming their engineering and design practices to digital, cloud-based solutions to enhance productivity, reduce costs and possess the ability to make better business decisions faster.

FutureOn’s powerful FieldTwin technology allows users an unparalleled design and collaborative experience to create digital twins, geospatially and technically accurate models of assets virtually anywhere in the world. This same proven technology is now available to other energy sectors, including the offshore wind industry.

Why Use a Digital Twin?

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.

The Anholt Windfarm Study

Using FieldTwin, FutureOn conducted an experiment demonstratingestablished digital workflows in the oil and gas sector can be successfullyapplied to relevant use cases for the offshore wind sector

Anholt Wind Farm

Location: Offshore Kattegat, Denmark
Type: Fixed Steel Jacket
Substation: Offshore
Operator: Orsted
Operational: 2013 – Present
Capacity:400MW

An API connection into an external map server – Helcom.fi/MADS –enabled fast integration of large volumes of spatial datasets used tobuild and contextualize the project. Geospatial data such as bathymetry,maritime activity, MetOcean, subsea infrastructure, flora and fauna wasimported into FieldTwin.

With all the data in place, FutureOn then created the digital model ofthe Anholt windfarm, including the turbine, substation and inter-arraycabling geolocated in-line with published field data from the operator(Orsted). Turbines were modeled to match OEM specifications (SiemensGamesa) and modeling in the 2D and 3D viewer, enabled spatial contextwith coastline and GIS data.

FieldTwin also allowed the visualization of windfarm wake effect datawithin the 3D model. This workflow reviewed multiple study data inparallel within the same model. This process can be explored with anAPI link into simulation tools.

With faster integration from multiple sources, FieldTwin quickly auto-generated a generic windfarm layout as proof of concept. This explorationinto technology transfer of geospatial data models and digital twins high-lights the availability of existing datasets and toolkits from the offshoreoil and gas industry which can readily be updated and augmented tosupport workflows in the offshore wind sector

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