Abstract
The prolonged period of waterflooding development on the oilfield has considerably increased the risk of casing damage, which would affect the regular production of wells, decrease the oil production rate as well as increase the costing of productive maintenance. Considering the in-depth analysis of casing damage and the selection of main effecting factors, a targeted countermeasure is needed in order to prevent casing damage. The causing factors of well casing damage include three types: geological (i.e., fault, formation dip, and mudstone interlayer), developmental (i.e., water injection pressure, injection-producing intensity, and volume replacement ratio), and engineering (i.e., cementing quality, material of the casing pipe). Data of influencing factors as described various in type and are big in volume, especially the data of develo** dynamic history. The article focuses on the optimization of main-controlling develo** factors and risk prediction of casing damage based on big data analysis technology. To derive the pattern in which exploiting factors affect on casing damage from mass data using big data analysis technology, the first step is to establish the relations between individual influencing factors with their corresponding casing damage rate, therefore identify the main controlling factor; In the second step, casing damage forewarning parameters system is established while the hysteresis phenomenon of the casing damage time is eliminated both by the computation of the correlation coefficient between parameters and casing damage rate. The last step is to establish the casing damage risk prediction model achieved by the application of principal component analysis and support vector machine, which make prediction of casing damage risk in advance feasible and provide the technical support for the prevention and control of casing damage.
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Acknowledgements
This project was subordinate to China Petroleum and Natural gas co., LTD., a major science and technology projects “Daqing oil and gas sustainable development effective key technology research and application”(2016E-0205), and this work was support by exploration and development research institute of Daqing oilfield, we thank Yanming Pang, Qinglong Du, Qing Li, for their helpful comments, we are grateful to Chunyu Zhao, Xueyan Jiang for their significant technical contributions to this project.
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Zhang, S. et al. (2020). Analysis of the Influence Factors of Casing Damage Based on Data Mining. In: Lin, J. (eds) Proceedings of the International Field Exploration and Development Conference 2018. IFEDC 2018. Springer Series in Geomechanics and Geoengineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-7127-1_21
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DOI: https://doi.org/10.1007/978-981-13-7127-1_21
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