From Expert Review to AI Self-Optimization: Practices and Exploration of Intelligent Scheme Drafting in Petroleum Engineering

In recent years, the petroleum industry has been undergoing a significant transformation toward digitalization and intelligent solutions. Large language models (LLMs) have demonstrated remarkable capabilities in natural language processing, but their application in petroleum engineering plan formulation remains in its early stages. The domestic petroleum industry faces challenges such as reliance on multidisciplinary expertise and field experience in plan formulation, over-dependence on foreign specialized software, and insufficient experience of young technical talents. This study proposes a framework integrating large language models (such as the LLaMA3 series), parameter-efficient fine-tuning technologies (such as LoRA), and expert feedback mechanisms to break the dependence on experience and software and enable rapid generation and iteration of solutions. The framework consists of three stages: plan generation, expert review, and model optimization. It uses LLMs to quickly generate initial plans and incorporates expert feedback for LoRA parameter updates to enhance the model’s adaptability to domain knowledge. A virtual case study shows that after three rounds of LoRA fine-tuning, the error rate of the model-generated plans decreased from 32% to 21.5%, verifying the potential of the framework to improve the efficiency and accuracy of petroleum engineering plan design. This research provides a theoretical foundation and methodological innovation for the development of intelligent solutions in the petroleum industry and contributes to the effective application of LLMs in complex domain-specific tasks.