Dynamic prediction method of rate of penetration （ROP） in deep geological drilling process based on regional multi-well data optimization and model pre-training
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摘要: 深部地质钻探过程钻速精准预测有助于提升钻探效率、降低钻探成本，可为安全高效的深部地质钻探施工提供关键技术支撑。本文提出了一种基于区域多井数据优选与模型预训练的深部地质钻探过程钻速动态预测方法。首先，选取岩性识别软件、钻进过程智能监控云平台、地质云系统等作为数据源，在此基础上设计深部地质钻探数据仓库。其次，运用区域多井数据优选技术在数据仓库中选择与目标井较匹配的数据，并开展钻速模型预训练。最后，结合深部地质钻探过程实钻数据，引入小波滤波、超限学习机、增量学习等技术，实现钻速预测模型动态更新。实验对比结果验证了所提方法具有很强的钻速预测性能与泛化能力。Abstract: Accurate prediction of rate of penetration （ROP） in deep geological drilling process can help to improve drilling efficiency and reduce drilling costs， which can provide key technical support for safety and efficient deep geological drilling construction. In this paper， a dynamic prediction method of ROP in deep geological drilling process based on regional multi-well data optimization and model pre-training is proposed. First， the deep geological drilling data warehouse is designed by selecting lithology identification software， drilling process intelligent monitoring cloud platform， and geological cloud system as data sources. Secondly， the regional multi-well data optimization technique is used to select the matching data with the target well in the data warehouse， and the ROP model is pre-trained on this basis. Finally， the ROP prediction model is dynamically updated through combining the actual drilling data of deep geological drilling process， and introducing techniques such as the wavelet filtering， extreme learning machine， and incremental learning strategy. The experimental comparison results verify that the proposed method has strong ROP prediction performance and generalization capability.
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[1] 甘超.复杂地层可钻性场智能建模与钻速优化［D］.武汉：中国地质大学（武汉），2019.GAN Chao. Intelligent modeling of formation drillability field and drilling rate of penetration optimization in complex conditions［D］. Wuhan： China University of Geosciences， 2019.
[2] [2] 张正，赖旭芝，陆承达，等.基于贝叶斯网络的钻进过程井漏井涌事故预警［J］.探矿工程（岩土钻掘工程），2020，47（4）：114-121，144.
ZHANG Zheng， LAI Xuzhi， LU Chengda， et al. Lost circulation and kick accidents warning based on Bayesian network for the drilling process［J］. Exploration Engineering （Rock & Soil Drilling and Tunneling）， 2020，47（4）：114-121，144.
[3] [3] 吴敏，曹卫华，陈鑫，等.复杂地质钻进过程智能控制［M］.北京：科学出版社，2022.WU Min， CAO Weihua， CHEN Xin， et al. Complex Geological Drilling Process Intelligent Control［M］. Beijing： Science Press， 2022.
[4] [4] VINíCIUS Veloso De Melo， WOLFGANG Banzhaf. Automatic feature engineering for regression models with machine learning： An evolutionary computation and statistics hybrid［J］. Information Sciences， 2018，430：287-313.
[5] [5] HUANG Junping， YAN Xuefeng. Quality-driven principal component analysis combined with kernel least squares for multivariate statistical process monitoring［J］. IEEE Transactions on Control Systems Technology， 2019，27（6）：2688-2695.
[6] [6] ZOU Lei， WANG Zidong， HU Jun， et al. Moving horizon estimation with unknown inputs under dynamic quantization effects［J］. IEEE Transactions on Automatic Control， 2020，65（12）：5368-5375.
[7] [7] DENG Yong， CHEN Mian， JIN Yan， et al. Theoretical and experimental study on the penetration rate for roller cone bits based on the rock dynamic strength and drilling parameters［J］. Journal of Natural Gas Science and Engineering， 2016，36：117-123.
[8] [8] 李谦，曹彦伟，朱海燕.基于人工智能的钻速预测模型数据有效性下限分析［J］.钻探工程，2021，48（3）：21-30.
LI Qian， CAO Yanwei， ZHU Haiyan. Discussion on the lower limit of data validity for ROP prediction［J］. Drilling Engineering， 2021，48（3）：21-30.
[9] [9] 熊虎林，李谦.基于地层成分和钻进参数的钻速预测模型［J］.探矿工程（岩土钻掘工程），2018，45（10）：195-201.
XIONG Hulin， LI Qian. ROP prediction model based on formation composition and drilling parameters［J］. Exploration Engineering （Rock & Soil Drilling and Tunneling）， 2018，45（10）： 195-201.
[10] [10] 周长春，姜杰，李谦，等.基于融合特征选择算法的钻速预测模型研究［J］.钻探工程，2022， 49（4）：31-40.
ZHOU Changchun， JIANG Jie， LI Qian， et al. Research on drilling rate prediction model based on fusion feature selection algorithm［J］. Drilling Engineering， 2022，49（4）：31-40.
[11] [11] GAN Chao， CAO Weihua， WU Min， et al. Prediction of drilling rate of penetration （ROP） using hybrid support vector regression： A case study on the Shennongjia area， Central China［J］. Journal of Petroleum Science and Engineering， 2019，181：106200.
[12] [12] ETESAMI Danial， ZHANG W J， HADIAN Mohsen. A formation-based approach for modeling of rate of penetration for an offshore gas field using artificial neural networks［J］. Journal of Natural Gas Science and Engineering， 2021，95：104104.
[13] [13] YOUCEFI Mohamed Riad， HADJADJ Ahmed， BENTRIOU Abdelhak， et al. Rate of penetration modeling using hybridization extreme learning machine and whale optimization algorithm［J］. Earth Science Informatics， 2020，13（4）：1351-1368.
[14] [14] SOARES Cesar， GRAY Kenneth. Real-time predictive capabilities of analytical and machine learning rate of penetration （ROP） models［J］. Journal of Petroleum Science and Engineering， 2019，172：934-959.
[15] [15] SUI Dan， AADNøY Bernt Sigve. Rate of penetration optimization using moving horizon estimation［J］. Modeling， Identification and Control： A Norwegian Research Bulletin， 2016，37（3）： 149-158.
[16] [16] ZHOU Yang， CHEN Xin， ZHAO Haibin， et al. A novel rate of penetration prediction model with identified condition for the complex geological drilling process［J］. Journal of Process Control， 2021，100：30-40.
[17] [17] GAN Chao， CAO Weihua， LIU Kangzhi， et al. A new hybrid bat algorithm and its application to the ROP optimization in drilling processes［J］. IEEE Transactions on Industrial Informatics， 2020，16（12）：7338-7348.
[18] [18] GAN Chao， CAO Weihua， LIU Kangzhi， et al. A novel dynamic model for the online prediction of rate of penetration and its industrial application to a drilling process［J］. Journal of Process Control， 2022，109：83-92.
[19] [19] 甘超，曹卫华，王鲁朝，等.深部地质钻探过程流式大数据分析与动态处理——以辽宁丹东3000 m科学钻探工程为例［J］.钻探工程，2022，49（4）：1-7.
GAN Chao， CAO Weihua， WANG Luchao， et al. Streaming big data analysis and dynamic pre-processing in deep geological drilling process： A case study on the 3000m scientific drilling project in Dandong， Liaoning province［J］. Drilling Engineering， 2022，49（4）：1-7.
[20] [20] FAN Haipeng， WU Min， CAO Weihua， et al. An operating performance assessment strategy with multiple modes based on least squares support vector machines for drilling process［J］. Computers & Industrial Engineering， 2021，159：107492.
[21] [21] 中国地质调查局.地质云3.0［DB/OL］. https：//geocloud.cgs.gov.cn/， 2021.China Geological Survey. Geological cloud 3.0［DB/OL］. https：//geocloud.cgs.gov.cn/， 2021.
[22] [22] 刘添翼.基于模糊神经网络的机械钻速预测方法［D］.大庆：东北石油大学，2022.LIU Tianyi. Prediction method of rate of penetration based on fuzzy neural network［D］. Daqing： Northeast Petroleum University， 2022.
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