In the process of petroleum exploration and development, long-term accumulated documents contain a large amount of engineering knowledge and practical experience, and these materials are of great significance for the scientific development of oilfields and production decision-making. However, such information is mostly preserved in multimodal and unstructured forms such as textual descriptions, data tables, and illustrative figures, lacking a unified structured representation, which leads to low efficiency in retrieval and utilization, and makes the knowledge difficult to be systematically applied. Traditional information retrieval methods have limitations in dealing with complex cross-paragraph and multimodal corpora, and relying only on largescale language models for question answering is prone to hallucinations and context fragmentation, which cannot meet the requirements of professional fields for accuracy and interpretability. To solve this problem, this paper, based on Microsoft’s opensource graph retrieval-augmented generation framework, constructs a graph retrieval-augmented intelligent question answering system for reservoir geology. Aiming at the linguistic complexity, hierarchical diversity, and structural heterogeneity of oilfield documents, three optimization methods were applied: a logical structure-based segmentation method was used to identify heading hierarchies and numbering rules to achieve reasonable division of semantic units, thereby avoiding semantic fragmentation in entity and relation extraction; a prompt optimization mechanism combined with the terminology system of reservoir geology was applied to improve the accuracy and completeness of entity and relation recognition and extraction, and to reduce errors and omissions; and a multimodal output mechanism was employed to realize the linkage of textual answers with relevant figures and tables through embedding matching, so that the results not only have linguistic coherence but also obtain visual evidence support, enhancing the interpretability and credibility of the answers. In the experimental part, a comprehensive report of about ninety thousand characters from a typical offshore oilfield was used as the data source to construct a knowledge graph and carry out system evaluation. Compared with unoptimized methods and the original framework, the results show that the optimized system has achieved significant improvements in factuality, answer relevance, context precision, and context recall. The improvement in factuality and answer relevance indicates that the system can more accurately generate answers that conform to facts and question intent, while the improvement in context indicators shows that it has greater advantages in cross-paragraph integration and multimodal association. The research results show that this system exhibits higher accuracy and reliability in knowledge extraction, organization, and application, has good engineering adaptability and scalability, not only provides a feasible solution for the structured management and intelligent utilization of complex oilfield knowledge, but also offers references and practical experience for the application of large language models in petroleum engineering and other highly specialized fields.

Predicting the rate of penetration (ROP) plays a significant role in optimizing drilling parameters, improving drilling efficiency, and reducing costs. Although intelligent algorithms have achieved promising results in ROP prediction, existing methods generally ignore the impact of drill bit wear on ROP. To address this technical bottleneck, this study proposes a ROP prediction model incorporating drill bit wear, which establishes a dual neural network architecture for predicting drill bit wear coefficients and ideal ROP. This architecture enables modeling of the complex nonlinear relationships among drilling parameters, wear states, and ROP. Aiming at the scarcity of real-time drill bit wear labels, a pretraining mechanism is proposed to obtain wear coefficients through a two-step training process. Comparative experiments based on measured data from Blocks A and B in Bohai oilfield show that: (1) The prediction accuracy of the porposed model for ROP is improved by 100% and 27% in Blocks A and B, respectively, compared with traditional machine learning methods, and by 14% and 7.6% compared with BP neural network models, significantly surpassing the performance of traditional data-driven models. (2) The proposed model demonstrates improvements in prediction performance in the shallow strata with complex lithology (Block A) than in the deep strata with stable lithology (Block B). (3) The proposed pretraining mechanism enables the model to predict drill bit wear coefficients without real-time wear labels and simultaneously improves the prediction accuracy of mechanical ROP by 24% and 10% in the two blocks, respectively. The coupled model and pretraining mechanism developed in this study not only provide a more accurate method for mechanical ROP prediction but also offer an effective means for real-time monitoring of drill bit wear states, providing practical guidance for drilling operations.