Unlocking Advanced E&P Applications for Offshore Wind using Python

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.