Rate of Penetration Optimization and Prediction Based on a Geological Label-Parameter Spectrum Framework
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摘要: 针对钻井过程中钻速预测与优化面临的参数耦合复杂、地层描述粗糙、模型可解释性差等挑战，旨在实现地质驱动的钻进参数智能优化。本文提出一种融合地质特征量化与多参数可视化分析的“地质标签-参数图谱”体系。基于南海某区块实际数据，引入因子分析将该区域的深度、压力、地震波速、岩性等多源地质参数降维为三个公共因子，据此构建12类具有明确物理意义的地质标签。基于地质标签与目标钻速区间的钻进参数图谱，直观展示不同地质条件下十余项工程参数的合理取值范围与最佳值。基于图谱，可在实钻中通过比对参数曲线与图谱偏离，制定定量的参数优化策略；在设计阶段通过计算参数与多套图谱的匹配概率，实现钻速区间定量预测。Abstract: In response to the challenges of complex parameter coupling, rough formation description, and poor model interpretability in predicting and optimizing the rate of penetration (ROP) during drilling, this study aims to achieve geology-driven intelligent optimization of drilling parameters. A "Geological Label- Parameter Atlas" system integrating geological feature quantification and multi-parameter visualization analysis is proposed. Based on actual data from a block in the South China Sea, factor analysis is introduced to reduce multiple geological parameters, such as depth, pressure, seismic wave velocity, and lithology, into three common factors. Accordingly, 12 types of geological labels with clear physical meanings are constructed. The drilling parameter atlas, developed based on these geological labels and target ROP intervals, visually displays the reasonable value ranges and optimal values for over ten engineering parameters under different geological conditions. Based on this atlas, quantitative parameter optimization strategies can be formulated during actual drilling by comparing the deviation between current parameter curves and the atlas, while in the design stage, quantitative prediction of ROP intervals can be achieved by calculating the matching probability between design parameters and multiple sets of atlas.
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Key words:
- Geological label /
- Drilling parameter /
- ROP optimization and adjustment /
- ROP prediction
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[1] 刘书杰, 吴怡, 谢仁军, 等, 2021. 深水深层井钻井关键技术发展与展望. 石油钻采工艺,43(2):139-145. doi: /10.13639/j.odpt.2021.02.002. [2] 宋先知, 裴志君, 王潘涛, 等, 2022. 基于支持向量机回归的机械钻速智能预测. 新疆石油天然气, 18(1):14-20. [3] Young, J.F.S., 1969. Computerized drilling control. Journal of Petroleum Technology, 21(4):483-496. doi: / 10.2118/2241-PA. [4] Bourgoyne, J.A.T., Young, J.F.S.,1974. A Multiple Regression Approach to Optimal Drilling and Abnormal Pressure Detection.Society of Petroleum Engineers Journal, 14(04):371-384. doi: /10.2118/4238-PA. [5] ROP Optimization and Modeling in Directional Drilling Process.SPE, 2017. [6] Etesami, D. G., Shirangi, M., Zhang, W.J., 2021. A Semiempirical Model for Rate of Penetration with Application to An Offshore Gas Field.SPE Drilling & Completion, 36(01):29-46. doi: /10.2118/202481-PA. [7] Ren, C., Huang, W., Gao, D., 2023. Predicting Rate of Penetration of Horizontal Drilling by Combining Physical Model with Machine Learning Method in the China Jimusar oil field.SPE Journal, 28(06):2713-2736. doi: /10. 2118/212294-PA. [8] 闫炎, 韩礼红, 刘永红, 等, 2023. 全尺寸PDC钻头旋转冲击破岩过程数值模拟. 石油机械, 51(6):36-42. doi: /10. 16082/j.cnki.issn.1001-4578.2023.06.005. [9] 李根生, 穆总结, 田守嶒, 等, 2024. 冲击破岩钻井提速技术研究现状与发展建议. 新疆石油天然气, 2024,20(1):1-12. doi: /10.12388/j.issn.1673-2677.2024.01.001. [10] 吴泽兵, 袁若飞, 张文溪, 等, 2024. PDC混合布齿钻头破碎非均质花岗岩数值模拟. 天然气工业, 44(5):105-117. doi: /10.3787/j.issn.1000-0976.2024.05.009. [11] 曹继飞, 邹德永, 李成, 等, 2024. 基于仿真的复合冲击破岩流固热耦合场分析. 石油机械, 52(8):61-69. doi: /10.16082/j.cnki.issn.1001-4578.2024.08.008. [12] 鞠玮, 肖宇航, 田永净, 等, 2025. 深部煤层气储层地质力学研究与进展. 地球科学, doi: /10.3799/dqkx.2025.294. [13] 刘泽栋, 吴孔友, 汪必峰, 等.2026. 富满油田FI17走滑断裂带碳酸盐岩储层天然裂缝地质力学特征及开发意义.地球科学. doi: /10.3799/dqkx.2026. 016. -
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