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
-
摘要: 深部地质钻探钻进过程数据价值密度低，传统方法难以在钻中流式大数据条件下有效去除尖峰、毛刺等各类钻进过程数据噪声。本文提出一种深部地质钻探钻进过程流式大数据分析与动态预处理方法，并成功应用于辽宁丹东3000 m科学钻探工程。首先，深入分析过程工艺和数据处理需求，建立深部地质钻探钻进过程流式大数据分析与动态预处理框架结构；然后，运用限幅滤波结合过程数据分布特征、司钻/机长人工操作经验去除过程数据中的离群值；接着，引入滑动窗口策略对流式钻进大数据进行动态处理，在每个窗口中运用Savitzky Golay滤波进一步提升数据质量。仿真实验和工程应用结果验证了本文方法具有很好的工程适用性和有效性。Abstract: The data quality in deep geological drilling process is poor， and traditional methods are hard to effectively remove all kinds of data noise such as spikes and burrs. A streaming big data analysis and dynamic pre-processing method for deep geological drilling process was proposed and successfully applied to the 3000m scientific drilling project in Dandong， Liaoning province. Firstly， the process mechanism and requirements of data processing are deeply analyzed， and the framework of streaming big data analysis and dynamic pre-processing in deep geological drilling process is established. After that， the outliers in the process data are removed by limiting filtering combined with the distribution characteristics of the process data and the driller’s manual operation experience. Then， the moving window strategy is introduced to dynamically process the big data of convective drilling， and savitzky Golay filter is used in each window to further improve the data quality. Finally， results of simulations and engineering application show that the proposed method has good engineering applicability and effectiveness.
-
-
[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] 范海鹏，吴敏，曹卫华，等.基于钻进状态监测的智能工况识别［J］.探矿工程（岩土钻掘工程），2020，47（4）：106-113.
FAN Haipeng， WU Min， CAO Weihua， et al. Intelligent drilling mode identification based on drilling state monitoring while drilling［J］. Exploration Engineering （Rock & Soil Drilling and Tunneling）， 2020，47（4）：106-113.
[3] [3] 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.
[4] [4] 张正，朱恒银.深部钻探关键设备选择原则及配置优化［J］.探矿工程（岩土钻掘工程），2017，44（9）：17-20.
ZHANG Zheng， ZHU Hengyin. Selection principles and configuration optimization of the key equipments in deep drilling［J］. Exploration Engineering （Rock & Soil Drilling and Tunneling）， 2017，44（9）：17-20.
[5] [5] 陈师逊，翟育峰，王鲁朝，等.西藏罗布莎科学钻探施工对深部钻探技术的启示［J］.探矿工程（岩土钻掘工程），2012，39（11）： 1-3，9.
CHEN Shixun， ZHAI Yufeng， WANG Luzhao， et al. Enlightenment to deep drilling technology from scientific drilling in Luobusha of Tibet［J］. Exploration Engineering （Rock & Soil Drilling and Tunneling）， 2012，39（11）：1-3，9.
[6] [6] 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.
[7] [7] 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.
[8] [8] 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.
[9] [9] GAN Chao， CAO Weihua， WU Min， et al. Two-level intelligent modeling method for the rate of penetration in complex geological drilling process［J］. Applied Soft Computing， 2019，80： 592-602.
[10] [10] ASHRAFI Seyed Babak， ANEMANGELY Mohammad， SABAH Mohammad， et al. Application of hybrid artificial neural networks for predicting rate of penetration （ROP）： A case study from Marun Oil Field［J］. Journal of Petroleum Science and Engineering， 2019，175：604-623.
[11] [11] DIAZ Melvin B， KIM Kwang Yeom， SHIN Hyu Soung， et al. Predicting rate of penetration during drilling of deep geothermal well in Korea using artificial neural networks and real-time data collection［J］. Journal of Natural Gas Science and Engineering， 2019，67：225-232.
[12] [12] 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.
[13] [13] 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 based on artificial intelligence［J］. Drilling Engineering， 2021，48（3）：21-30.
[14] [14] DIAZ Melvin B， KIM Kwang Yeom， SHIN Hyu Soung. On-line prediction model for rate of penetration （ROP） with cumulating filed data in real time［C］//
YSRM & NDRMGE ，Jeyu， Korea ，May 10-13 ， 2017.[15] [15] SAVITZKY Abraham， Golay M J E. Smoothing and differentiation of data by simplified least squares procedures［J］. Analytical Chemistry， 1964，36（8）：1627-1639.
-
计量
- 文章访问数: 619
- PDF下载数: 274
- 施引文献: 0
下载: