Abstract
Rate of penetration (ROP) plays a key role in reducing drilling engineering cost. At present, low drilling rate and long drilling period have become major problems of the development of deep drilling. In response to these problems, the combination of machine learning technology and drilling engineering technology may provide new techniques for increasing the ROP. For this reason, it is necessary to introduce machine learning technology into drilling engineering, even if the work is only exploratory. In this paper, regression analysis of the ROP was conducted by the method of a variety of machine learning algorithms. In the example, a total of 15 tag data were collected, and the data volume was greater than 5,000 groups. It was saved as a CSV file and then read into a Python program. And the distribution of each tag data is analyzed, and the data distribution table of each tag data is calculated. Random forest method showed more accurate prediction result by reaching prediction accuracy of 76% in the data of whole well. In fact, if the outliers can be ignored, the accuracy will be higher.
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References
The national energy administration: Shale gas development plan (2016–2020) countries, no. 255 (2016). Accessed 14 Sept 2016
Chen, P.: Drilling and Completion Engineering, pp. 15–18. Petroleum Industry Press, Peking (2005)
Sun, J., Yang, Y., An, S., Yinao, S.: A study on the theory and technology of drilling fluids to increase ROP. Drill. Flud Complet. Fluid 26(2), 1–6 (2009)
Wang, H., Zheng, X.: Current situation and challenges of deep well drilling technology of CNPC. Oil Drill. Prod. Technol. (02), 4–8 (2005)
Li, Z., Xu, J., An, X.: Using the mathematical model to optimize the motor and drilling parameters, improving the rate of penetration. Comput. Appl. Pet. (04), 48–52 (2014)
Dye, W.M., Daugereau, K.: New water-based mud balances high-performance drilling and environmental compliance. Society of Petroleum Engineers (2006). https://doi.org/10.2118/92367-pa
Qu, Y., Sun, J., Su, Y.: Advances of faster penetration drilling fluids. Drill. Fluid Complet. Fluid 23(3), 68–70 (2006)
Xiong, J., Mang, W., Yang, L.: Mechanism of enhancing penetration rate by novel jet and its progress in research. Nat. Gas. Ind. 25(9), 51–53 (2005)
Li, X., Gao, D.: Analysis research on the maximum penetration rate for extended-reach horizontal well. In: Exploration Engineering: Rock & Soil Drilling and Tunneling, p. 5 (2017)
Lin, Y., Zong, Y., Liang, Z., et al.: The development of ROP prediction for oil drilling. Pet. Drill. Tech. 32(1), 10–13 (2004)
Liu, J., Wei, H., et al.: A new 3D ROP equation considering the rotary speed of bit. Pet. Drill. Tech. 43(01), 52–57 (2015)
Fan, X., Xia, H., Zheng, L.: New method of using seismic velocity to predict layer drilling rate. Drill. Prod. Technol. 30(1), 4–6 (2007)
Motahhari, H.R., Hareland, G., Nygaard, R., Bond, B.: Method of optimizing motor and bit performance for maximum ROP. Petroleum Society of Canada (2009). https://doi.org/10.2118/09-06-44-tb
Lin, Y., Shi, T., et al.: Simulation of impact force and penetration rate of air hammer bit drilling. Chin. J. Rock Mech. Eng. (18), 3337–3341 (2005)
Feng, F., Ai, C., Lv, Y., et al.: Penetration rate model of roller bit based on revolving indentation theory. Sci. Technol. Rev. 33(12), 29–32 (2015)
Li, C.: Study of method for predict rate of penetration based of multiple regression analysis. Sci. Technol. Eng. 13(07), 1740–1744 (2013)
Moran, D., Ibrahim, H., Purwanto, A.: Sophisticated ROP prediction technologies based on neural network delivers accurate drill time results. In: IADC/SPE 132010 (2010)
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This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA14040401), National Science and Technology Major Project (Grant No. 2016ZX05034-003), the National Key Research and Development Program of China (No. 2018YFC1504803) and Science and the Key Research and Development Program of Shanxi Provence (No. 2017ZDCXL-SF-03-01-01), the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (No. 2019QZKK0905), the Key Deployment Program of CAS (No. KFZD-SW-422).
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Li, S. et al. (2020). Prediction of Rate of Penetration Based on Random Forest in Deep Well. In: Correia, A., Tinoco, J., Cortez, P., Lamas, L. (eds) Information Technology in Geo-Engineering. ICITG 2019. Springer Series in Geomechanics and Geoengineering. Springer, Cham. https://doi.org/10.1007/978-3-030-32029-4_45
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DOI: https://doi.org/10.1007/978-3-030-32029-4_45
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