First Break - Volume 43, Issue 10, 2025

As the offshore wind industry grows to meet demands for renewable energy, offshore wind farms are being built bigger, in deeper water, and in more challenging ground conditions. Increasingly complex and accurate ground models are needed to deal with these conditions, and the software traditionally used to build ground models does not always fulfil these advanced processing, analysis, and modelling needs.

The E&P industry has spent decades and millions of dollars innovating and refining subsurface interpretation and modelling software. We argue that the benefits of this research and development should be leveraged when building ground models for offshore wind.

A barrier to the use of popular E&P platforms is data ingestion. While the data used for building ground models is analogous to the data used in E&P there are important differences that can hinder integration. Cone penetration test (CPT) data used by the offshore wind industry are analogous to the well data used by the hydrocarbon exploration and production (E&P) industry. Despite their similarities, these data are typically stored in different file types, meaning that they cannot be loaded into software packages interchangeably. This limits the workflows and functionality available in ground modelling for offshore wind.

Using Python application programming interfaces (APIs), CPT data were successfully loaded into Petrel® E&P Platform* (Petrel), which does not natively support ingestion of these data. This enabled the advanced data visualisation, analysis, conditioning, interpretation and modelling functionalities of Petrel to be applied to CPT data. We demonstrate the value of this through a streamlined and partially-automated soil behaviour type classification workflow.

Across carbon capture and storage (CCS) conferences, cost reduction remains a central theme to enable deployment ‘at scale’. Monitoring, measurement, and verification (MMV) plans span the entire Life of Injection (LoI) and beyond, representing significant operational expenditure. Drawing from oil and gas heritage, pioneering CCS projects like Quest and Sleipner rely on image-based solutions (VSP, 4D seismic). While robust, these methods are costly and raise operational and societal concerns. Even if providing global insights about the subsurface and seismic images have proved efficient for oil and gas production optimisation, a question is raised regarding its adoption on CCS, where safety is the primary concern.

To address the surveillance need of CCS, it is important to mention that the million dollar question here isn’t whether to image, but when. In our industry, ‘the value of full 4D is in the surprise’, but surprises are precisely what CCS operators want to avoid. Instead, we propose a seismic-focused monitoring method called spot seismic, which is model and data-driven. Lightweight and repeatable, this active seismic method has been successfully implemented onshore and offshore. Its goal is to provide timely evidence that the CO2 plume behaves as predicted, and to trigger alarms when significant anomalies arise. The spot seismic method, has been recognised in the Global CCS Institute’s state of the art: CCS Technologies 2025 report as a TRL 8–9 scalable and innovative approach for CCS surveillance.

This predictive monitoring approach not only addresses CCS economics but also serves multiple stakeholders, aligning with regulatory frameworks and enabling transparent, risk-based decision-making.