Well logging data processing and interpretation are critical to reservoir evaluation and usually involve multi-source data integration, feature construction, and parameter inversion. These procedures are often complex and strongly dependent on expert experience. In conventional interpretation workflows, the individual processing steps are commonly performed separately, which leads to low overall efficiency. Moreover, the limited integration of data from different modalities further constrains the automation and effectiveness of integrated interpretation. To address these issues, a Logging Agent Collaborative Framework (LogACF) is proposed. The framework is built on a collaborative role-memory-planning-execution architecture, through which large language models are enabled to support task-oriented problem solving beyond conventional conversational generation. By incorporating predefined workflows into the reasoning process, automated execution is supported for data preprocessing, feature engineering, model construction, reservoir identification, and report generation. The framework is evaluated using both public datasets and field-measured data from a tight sandstone reservoir. In reservoir parameter prediction, the agent-optimized models outperform baseline models across evaluation metrics and show stable generalization performance. In addition, the automatically generated interpretation reports reasonably characterize high quality reservoir intervals in terms of reservoir identification and oil-bearing potential. The results demonstrate that LogACF can stably process multi-source well logging data, support data preprocessing and feature construction, and maintain data integrity and consistency with petrophysical interpretation. Overall, this study provides a practical framework for automated well logging data processing and interpretation, and offers technical support for the further application of agent-based methods in intelligent oil and gas exploration.
Luo et al. (2026) studied this question.