陶杉,余星,宋海,廖亚民,常启帆,樊晶晶. 大数据方法寻找顺北碳酸盐岩储层开采过程中井壁坍塌主控因素[J]. 石油钻采工艺,2020,42(5):627-631. DOI: 10.13639/j.odpt.2020.05.017
引用本文: 陶杉,余星,宋海,廖亚民,常启帆,樊晶晶. 大数据方法寻找顺北碳酸盐岩储层开采过程中井壁坍塌主控因素[J]. 石油钻采工艺,2020,42(5):627-631. DOI: 10.13639/j.odpt.2020.05.017
TAO Shan, YU Xing, SONG Hai, LIAO Yamin, CHANG Qifan, FAN Jingjing. Application of the big data method to search for the main factors controlling the hole collapse in the production process of Shunbei carbonate reservoir[J]. Oil Drilling & Production Technology, 2020, 42(5): 627-631. DOI: 10.13639/j.odpt.2020.05.017
Citation: TAO Shan, YU Xing, SONG Hai, LIAO Yamin, CHANG Qifan, FAN Jingjing. Application of the big data method to search for the main factors controlling the hole collapse in the production process of Shunbei carbonate reservoir[J]. Oil Drilling & Production Technology, 2020, 42(5): 627-631. DOI: 10.13639/j.odpt.2020.05.017

大数据方法寻找顺北碳酸盐岩储层开采过程中井壁坍塌主控因素

Application of the big data method to search for the main factors controlling the hole collapse in the production process of Shunbei carbonate reservoir

  • 摘要: 塔里木顺北油田是深层碳酸盐岩油田,储层取心困难,钻取岩心因非均质性强代表性差。现有方法研究井壁稳定是单一因素或者几个确定因素通过室内岩样或者数模研究,研究成果控制井壁坍塌成功率不高。引入大数据思想利用现场数据寻找主控因素,以解决无岩心或者少岩心问题以及尽可能多的考虑影响因素。整理顺北1条带和5条带17口井的储层钻开、洗井、测试、酸化压裂、试油生产等作业环节的85万条原始数据,以井径扩大率作目标参数,钻开储层的地质、工程和流体等实际发生数据作自变量,为保证数据的物理意义采用独立自变量的多元回归拟合。再利用削元法去掉非主控因素,获得可以人为控制的因素从大到小的排序为Φ600、钻进目的层钻时、Φ300、钻进目的层钻压、Φ200Y坐标、Φ100、漏斗黏度、X坐标、Φ6、钻井泵压、井斜角、固相含量。用钻井过程的模型推测了生产过程中因井壁坍塌造成油管堵塞的SHB1-4H井的井径扩大率为3.22%,并解释了开采过程中出现堵塞的原因。引入大数据思想寻找深层无实验样品预测井壁稳定主控因素,为深层油气开发过程中控制井壁稳定提供了一种方法。

     

    Abstract: Shunbei Oilfield is a deep carbonate oilfield in the Tarim Basin, whose reservoir coring is difficult and the cores are poorly representative due to their strong heterogeneity. At present, the borehole stability is studied by means of laboratory rock samples or numerical simulation based on a single factor or several confirmed factors. However, the success rate of controlling the hole collapse according to the research results is not high. The idea of big data was introduced to search for the main control factors based on field data, so as to solve the problems of no or few cores and take into consideration the influential factors as much as possible. 850 thousand original data in the operation links (e.g. reservoir drilling, well flushing, testing, acid fracturing and production test) of 17 wells in No. 1 and No. 5 Shunbei belt were sorted out. And in order to ensure the physical significance of the data, the multiple regression fitting of independent variable was carried out by taking the hole enlargement rate as the target parameter and the actual data of drilled reservoirs (including geological, engineering and fluid data) as the independent variables. Then, non-main control factors were removed by means of element reduction method, and the artificially controllable factors were ranked from the top to the bottom, i.e., Φ600, drilling time of target layer, Φ300, weight on bit while drilling the target layer, Φ200, Y-axis, Φ100, funnel viscosity, X-axis, Φ6, drill pump pressure, hole deviation angle and solid content. Finally, it was inferred by using the model of drilling process that Well SHB1-4H whose tubing was blocked due to hole collapse in the process of production had the hole enlargement rate of 3.22%. In addition, the reasons for the blockage in the process of production were interpreted. In conclusion, introducing the idea of big data to search for the main factors controlling the borehole stability in deep layers while no test samples is available for prediction provides a new method for controlling the borehole stability in the development process of deep oil and gas.

     

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