Abstract:

To address the challenge of real-time weight transfer recognition in directional well sliding drilling, an intelligent multi-level identification method based on measurement-while-drilling (MWD) real-time data is proposed. A four-level weight transfer evaluation system based on weight-on-bit (WOB) transfer ratio is established. By analyzing the response characteristics of downhole WOB and vibration signals, a multi-dimensional feature space is constructed from statistical domain, frequency domain, and temporal evolution perspectives. A comprehensive importance evaluation strategy is adopted to select 10 core features. To meet the strict requirements of real-time performance and lightweight design for downhole closed-loop control, a shallow random forest recognition model is designed, utilizing class weight methods to handle sample imbalance and well-based data partitioning strategy to ensure model generalization capability. Based on measured data from 5 directional wells in a western oilfield, the model achieves 90.2% accuracy and 0.900 Macro-F1 score on the independent well test set, with a recall rate of 87.6% for complete weight transfer. The model is successfully deployed on an ARM Cortex-M4 processor with 52 KB storage space and 355 milliseconds inference time, meeting all downhole hardware constraints. The consistency between the model decision logic and weight transfer physical mechanism is verified through interpretability analysis. The research results can be directly applied to intelligent on-off control of downhole active control devices such as hydraulic oscillators, reducing response time from minute-level of traditional surface control to within 5 seconds, which has significant engineering value for improving drilling efficiency and reducing downhole risks.

Key words: weight transfer recognition, measurement while drilling, lightweight model, random forest, embedded deployment

摘要：

针对定向井滑动钻进工况下托压实时识别的难题，提出了一种基于随钻实时数据的托压多级智能识别方法。建立了基于钻压传递率的四级托压评价体系，通过分析井下钻压与振动信号的响应特征，从统计域、频域和时序演化三个维度构建多维特征空间，采用综合重要性评价策略筛选出10个核心特征。针对井下闭环控制对实时性和轻量化的严格要求，设计了浅层随机森林识别模型，通过类别权重方法处理样本不平衡问题，采用基于井号的数据划分策略确保模型泛化能力。基于西部某油田5口定向井的实测数据，模型在独立井测试集上达到90.2%的准确率和0.900的Macro-F1分数，完全托压召回率为87.6%。实现了模型在ARM Cortex-M4处理器上的实际部署，模型存储空间52 KB、推理时间355 ms，满足井下硬件的全部约束条件。通过可解释性分析验证了模型判别依据与托压物理机理的一致性。研究成果可直接应用于水力振荡器等井下主动控制装置的智能启停，将响应时间从传统地面控制的分钟级缩短至5 s以内，对提高钻井效率、降低井下风险具有重要工程价值。

关键词: 托压识别, 随钻测量, 轻量化模型, 随机森林, 嵌入式部署

CLC Number: 

• TE271