Innovative Fine-Tuning Paradigms for Large-Scale Modelsin Oil and Gas Exploration and Development—DomainAdaptation Algorithms and Engineering Practices Based OnDeepSeek

To address the technological bottlenecks in industrial software adaptability and computational efficiency during the intelligent transformation of oil and gasexploration, this study proposes a domain-adaptive framework based on advanced computational models. The framework aims to establish an efficient and preciseintelligent exploration system by integrating domain-specific knowledge and optimizing computational performance. A multimodal domain knowledge base was constructed to fuse geological, drilling, and seismic data, while a dynamic hierarchical optimization strategy was implemented to enhance the adaptability of computational models. Combined with parallel computing acceleration and lightweight model compression, a high-efficiency architecture for 3D seismic data processing was developed. The validation system, grounded in drilling logs and seismic datasets from the Shengli Oilfield, encompasses algorithm training, performance optimization, and engineering deployment. Key experimental results include: A significant improvement in drilling log analysis accuracy ZUOKHF1-score: 0.68 → 0.91YOUKH. A 12-fold increase in 3D seismic data processing speed ZUOKHtask duration: 4.8s → 0.4sYOUKH. Engineering validations demonstrate that the proposed framework reduces exploration cycles from 120 days to 45 days and decreases per-project costs by approximately ￥ 820,000. The study highlights two critical advancements: Dynamic knowledge-driven optimization effectively mitigates domain drift and enhances model stability. The synergy of computational acceleration and model compression overcomes real-time processing limitations in industrial software, enabling reliable decision-making in complex geological environments. The primary innovations include: A domain knowledge-computational model fusion framework for adaptive learning. An industrial-grade acceleration-compression co-optimization methodology to balance efficiency and precision. These advancements have been successfully implemented in the Shengli Oilfield’s exploration system, providing a scalable technical pathway and practical benchmarks for the intelligent transformation of the oil and gas industry.