Empowering Subsurface Experts: Seamless Integration of Research and Data into Petrel Workflows with Advanced Python Tools

Energy companies are increasingly reliant on their ability to integrate diverse datasets, apply advanced technologies, and incorporate new research findings into their subsurface workflows to maintain a competitive edge. However, integrating machine learning (ML) models, new research, and external data into widely used platforms like Petrel* presents significant challenges for geoscientists, particularly due to the technical complexity and coding expertise required. This complexity slows the adoption of innovative tools and workflows and can quickly become a barrier to optimisation. A key hurdle lies in enabling geoscientists to leverage ML models and integrate new research directly within Petrel without needing Python coding skills. Many workflows are hindered by the technical expertise needed to develop custom solutions, which can impede the full adoption of advanced workflows or Pythonbased solutions for automation. This article explores how Python APIs facilitate the connection to external data sources, deploying ML models and customised solutions with minimal programming expertise. Additionally, we examine how the gap between data scientists and geoscientists can be bridged, enabling geoscientists to leverage these customised solutions without any programming expertise. Through practical examples, we demonstrate how these tools can optimise daily operations, automate processes, and allow for better decision-making in subsurface projects. This approach ensures that geoscientists can focus on the data, not the technical complexities, driving innovation and efficiency.