FracLogGPT: A Multimodal Large Language Model for Fracture Interpretation in Imaging Logging

Imaging logging serves as a critical technology for identifying and characterizing fractures in unconventional oil and gas reservoirs. Despite significant progress in deep learning for automated fracture recognition in this field, the integration of fracture interpretation with large language models remains insufficient. To address this, this paper constructs a Chinese fracture image–text pair dataset covering multiple scenarios and proposes “FracLogGPT”, a three-stage multimodal large language model with a parameter scale of approximately 7 billion. Using Qwen2.5-VL-7B as the baseline model, this study employs Domain-Adaptive pre-training (DAPT) to tailor the model to geological and logging contexts. Efficient Supervised Fine-Tuning (SFT) is achieved via the LoRA method, while output style alignment is accomplished through Direct Preference Optimization (DPO) combined with expert preference data. Experimental results on an independent test set show that FracLogGPT achieves a Count-F1 of 0.70 for fracture-count classification, with location and morphology consistency accuracies of 0.49 and 0.43, respectively, and higher text-level BLEU and ROUGE-L scores than larger, non-domain-adapted external models evaluated under the same conditions. Comparative experiments across stages validate the effectiveness of the proposed workflow. In summary, “FracLogGPT” achieves automated identification and expert-like description of imaging logging fractures with approximately 7 billion parameters, providing a reusable training pathway and evaluation workflow for intelligent imaging logging interpretation.