New Role of Technical Specialists to Enable Digital Transformation in the Petroleum Industry: A Petrophysicist-Based Proof of Concept

Standard petrophysical workflows are limited by centralized control of log data, restricting tool access to dedicated specialists. This structure often leads to delays, reduced reproducibility and limited accessibility for multidisciplinary teams. To address these challenges, this study proposes a methodological framework combining natural language interfaces with an GenAI-based orchestration layer. The approach allows non-petrophysical specialists to access key petrophysical data, perform standard analyses, reduce reliance on manual processing and ensure secure, reproducible operations. The system is built on a modular architecture to streamline and standardize petrophysical data analysis. The Natural Language Interface uses large language models to interpret user queries and translate them into structured tool commands. The MCP Server Execution layer orchestrates analytical workflows, manages access control, and coordinates the execution of core analytical tools. Library Operations provide the fundamental well log processing, quality control, statistical analysis, and data visualization. The containerized system supports local deployment, ensuring traceability and data confidentiality within secure corporate infrastructure. Results indicate that the proposed agent-based framework mitigates procedural constraints associated with centralized log interpretation, facilitating more autonomous access for subsurface teams. The natural language interface translates user queries into structured analytical sequences, supporting reproducible execution and preserving auditability. This outcome supports statistical summaries, anomaly indicators, and customized visualizations optimized for interpretation by multidisciplinary teams. The framework enables petrophysicists to transition from task execution to system-level oversight, focusing on the design, validation, and supervision of automated workflows. Data security is a critical concern, addressed through containerized local deployment and the use of private LLM instances, ensuring compliance with internal data governance policies and eliminating external risks. The system integrates internal corporate data with locally hosted open-source libraries deployed under strict network isolation to ensure that no data is transmitted outside the organizational environment. The results confirm the feasibility of the approach as a scalable solution for restructuring interpretation workflows and reducing barriers in geoscientific collaboration. This study introduces a secure framework for embedding natural language-driven AI agents into subsurface workflows. A key impact is the shift in the petrophysicist's role from repetitive execution to workflow supervision and agent training. Significant business impact by reducing workload and decisions acceleration while supporting full data control and business process modularity. The cognitive layer uses structured prompting, including chain-of-thought reasoning to enhance interpretability and robustness of responses.