To address the issues of multi-source heterogeneity in production logging data, strong multi-solution interpretability, the inefficiency and expert-dependency of traditional methods, a question-answering system enhanced reasoning method based on CoT(Chain of Thought) is proposed. This method is built on the LLaMA2 large language model and the parameter-efficient fine-tuning LoRA ( Low-Rank Adaptation ) technique is introduced, which inserts low-rank matrices into the self-attention and feed-forward network layers to achieve a balance between model performance maintenance and computational resource optimization. A DCoT(Divergent Chain of Thought)reasoning framework is constructed, enabling the model to generate multiple reasoning paths and enhancing the system’s multi-step interpretation ability for complex production logging problems. Experimental results show that this method improves F1 score, precision, and recall by 4. 6% , 7. 0% , and 2. 1% , respectively, compared to the baseline model, demonstrating strong reasoning performance and application potential. The research proves that the LoRA+DCoT method has good practicality and scalability in improving the intelligent interpretation effect of logging data.

large language model, chain of thought, production logging, enhanced reasoning, low-rank adaptation(LoRA), divergent chain of thought(DCoT)