First Break - Volume 43, Issue 2, 2025

Geoscientists and engineers often need quick, reliable answers from confidential or internal documents. Generic cloud-based chatbots struggle to provide accurate, industry-specific information. Moreover, they are not allowed to access internal knowledge bases. To solve this, we developed a local, self-hosted chatbot that uses a local Large Language Model (LLM) combined with an AI-based search system fine-tuned to offshore drilling data. Our setup ensures reliable domain-relevant responses without sending information to external servers and limiting false information generation called ‘hallucination’. By keeping all data in-house and enhancing retrieval accuracy, this methodology offers a practical way to build secure, specialised chatbots for other subsurface applications. We provide open-source code and a setup guide to facilitate reproducibility and adoption.

The effective exploration and development of subsurface resources require detailed, continuous rock characterisation to understand reservoir heterogeneity and optimise resource recovery. Traditional core analysis techniques often rely on destructive, fragmented testing, which leaves significant gaps in the spatial resolution of rock properties and can lead to biased interpretations of subsurface conditions. This article introduces CoreDNA™, an innovative non-destructive core digitalisation platform that transforms conventional core analysis by creating high-resolution, multidisciplinary digital logs along the entire length of a core.

By integrating advanced imaging, geochemical, petrophysical, and geomechanical data, CoreDNA™ generates a ‘digital twin’ of the core. This approach bridges the gap between broadscale wireline logging and detailed core subsample analyses, enabling precise lithofacies identification, optimised subsample selection, and robust data upscaling. A case study from Well 15/12-20 S in the Norwegian Central North Sea demonstrates how the solution accelerates rock property characterisation, identifies reservoir heterogeneity, and enhances the resolution and reliability of reservoir quality assessments.

This cutting-edge workflow reduces uncertainties in reservoir modelling by providing a scalable, objective, and cost-effective methodology for subsurface evaluation. By complementing existing analytical techniques, the tool establishes a new paradigm in core analysis, paving the way for safer and more efficient resource exploration.

Here, we present a new workflow for creating 3D volumes from 2D seismic data. In essence, our method is a post-stack, interpretation-guided, data-driven process. We combine conventional techniques with modern deep learning algorithms to create a so-called Pseudo3D volume. We describe the workflow based on an example from offshore Indonesia.

Evaluating the effectiveness of hole cleaning and ensuring wellbore stability is crucial for preventing unwanted events such as kicks or stuck pipes, with consequent minor or major non-productive time (NPT) that, in severe cases, may lead to well abandonment. Beyond the economic implications, these scenarios pose risks of environmental damage and jeopardise the safety of rig personnel. Shale shakers are the first indicator of emerging borehole cleaning and wellbore stability problems and as such, they are a fundamental component of the drilling rig.

Monitoring the shakers and periodically collecting samples are tasks typically assigned to humans. These processes lack continuity in monitoring and rely on subjective interpretation of observed samples, often requiring humans to spend significant time in hazardous zones. Real-time machine learning-based automated detection and interpretation of the shaker screens can substantially improve rig safety by reducing the need for humans to be present in hazardous conditions with fumes and noises when their direct intervention is unnecessary.

A novel Computer Vision System has been implemented for automated and uncrewed shale shaker visual monitoring, coupled with Deep Learning (DL) Artificial Intelligence (AI) models. The system produces high-frequency objectively interpreted real-time data that can be recorded and plotted along drilling parameters. It aims to replace the traditional human-based monitoring approach by giving a continuous objective detection of shaker performance and events and enabling safer and more effective drilling operations.

Traditional workflows using machine learning interpretation of seismic data rely on iterative training and inference on single datasets, producing models that fail to generalise beyond their training domain. Self-supervised training and scaling of 3D vision transformer (ViT) architectures enables seismic interpretation with improved generalisation across diverse datasets. We address the complexities of large-scale training on a global dataset of 63 seismic volumes using the masked autoencoder (MAE) architecture with the ViT-H model consisting of 660 million parameters. We leverage a cloud-native, digitalised seismic data infrastructure to address the data engineering challenges, avoiding duplication. For a downstream task, a salt segmentation model trained using interpretation labels from the Gulf of Mexico and Brazil demonstrated zero-shot generalisation on a West African survey. These findings underscore the potential of pre-trained foundation models to overcome the limitations of iterative approaches and extend seismic interpretation across diverse basins, marking a significant advancement in scalable machine learning for subsurface challenges.

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.