Abstract
Natural gas is one of the important energy sources. However, during the production process of natural gas, abnormal events often occurred due to various factors so that the pumping equipment could not work. At present, the detection of abnormal production of gas wells mainly relies on the personal experience of engineers. The continuous production of gas well data puts huge pressure on limited manpower. Moreover, the results of manual judgment are often unreliable due to personal subjectivity, and problems such as failure to find abnormalities in time. The objective of this paper is to establish a fast and reliable data-driven anomaly detection framework. Its focus is managing and processing a high volume of data to improve operational efficiency, enhance decision making and mitigate risks in the workplace. The proposed framework employs a state-of-the-art algorithm, called boosting tree, which can not only identify point anomalies but also find context anomalies based on historical data. Comparing the test results with the manual annotation results on several real gas production datasets, the results show that the proposed framework is proficient at detecting anomalies.
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References
Agyemang, M.: Algorithm for mining local outliers. In: Innovations Through Information Technology: 2004 Information Resources Management Association International Conference, New Orleans, Louisiana, USA, May 23–26, 2004. vol. 1, p. 5. IGI Global (2004)
Alallaq, N., Al-khiza’ay, M., Dohan, M.I., Han, X.: Sentiment analysis to enhance detection of latent astroturfing groups in online social networks. In: Chen, Q., Wu, J., Zhang, S., Yuan, C., Batten, L., Li, G. (eds.) ATIS 2018. CCIS, vol. 950, pp. 79–91. Springer, Singapore (2018). https://doi.org/10.1007/978-981-13-2907-4_7
Albanese, D., Riccadonna, S., Donati, C., Franceschi, P.: A practical tool for maximal information coefficient analysis. Oxford Open 7(4), giy032 (2018)
Lin, B., Wesseh, P.K., Jr.: Estimates of inter-fuel substitution possibilities in Chinese chemical industry. Energy Econ. 40(2), 560–568 (2013)
Bhati, B.S., Chugh, G., Al-Turjman, F., Bhati, N.S.: An improved ensemble based intrusion detection technique using xgboost. Trans. Emerg. Telecommun. Technol. 32(6), e4076 (2021)
Bhattacharya, S., Maddikunta, P.K.R., Kaluri, R., Singh, S., Gadekallu, T.R., Alazab, M., Tariq, U., et al.: A novel PCA-firefly based XGBoost classification model for intrusion detection in networks using GPU. Electronics 9(2), 219 (2020)
Bolton, R.J., Hand, D.J.: Statistical fraud detection: a review. Stat. Sci. 17(3), 235–249 (2002)
Breunig, M.M., Kriegel, H.P., NG, R.T., Sander, J.: Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 93–104 (2000)
Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2016)
Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: techniques, systems and challenges. Comput. Secur. 28(1–2), 18–28 (2009)
Hanley, J.A., Mcneil, B.J.: The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology 143(1), 29 (1982)
Knorr, E.M., Ng, R.T.: A unified notion of outliers: properties and computation. In: KDD, vol. 97, pp. 219–222 (1997)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: International Joint Conference on Artificial Intelligence (1995)
Kuang, L., Zulkernine, M.: An anomaly intrusion detection method using the CSI-KNN algorithm. In: Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 921–926 (2008)
Li, G., Tan, J., Chaudhry, S.S.: Industry 4.0 and big data innovations. Enterp. Inf. Syst. 13(2), 145–147 (2019). https://doi.org/10.1080/17517575.2018.1554190
Lu, H., Guo, L., Azimi, M., Huang, K.: Oil and gas 4.0 era: a systematic review and outlook. Comput. Ind. 111, 68–90 (2019)
Milojevic, S.: Sustainable application of natural gas as engine fuel in city buses: benefit and restrictions. Istrazivanja i Projektovanja za Privredu 15(1), 81–88 (2017)
Naseer, S., Faizan Ali, R., Dominic, P., Saleem, Y.: Learning representations of network traffic using deep neural networks for network anomaly detection: a perspective towards oil and gas it infrastructures. Symmetry 12(11), 1882 (2020)
Qiu, Z., Zhao, W., Hu, S., Zhang, G., Hui, F.: The natural gas resource potential and its important status in the coming low-carbon economy. Eng. Sci. 13(6), 81–87 (2011)
Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 427–438 (2000)
Shaikh, F., Ji, Q.: Forecasting natural gas demand in china: logistic modelling analysis. Int. J. Electr. Power Energy Syst. 77, 25–32 (2016)
Vu, H.Q., Luo, J.M., Ye, B.H., Li, G., Law, R.: Evaluating museum visitor experiences based on user-generated travel photos. J. Travel Tourism Market. 35(4), 493–506 (2018). https://doi.org/10.1080/10548408.2017.1363684
Xia, H., Vu, H.Q., Law, R., Li, G.: Evaluation of hotel brand competitiveness based on hotel features ratings. Int. J. Hospitality Manage. 86, 102366 (2020). https://doi.org/10.1016/j.ijhm.2019.102366, https://www.sciencedirect.com/science/article/pii/S0278431919303019
Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. CRC Press, Boca Raton (2012)
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Yang, S., Wang, Z., Liu, L., Liu, Y., Chen, H., Tang, X. (2022). Detecting Anomalies in Natural Gas Production: A Boosting Tree Based Model. In: Pokhrel, S.R., Yu, M., Li, G. (eds) Applications and Techniques in Information Security. ATIS 2021. Communications in Computer and Information Science, vol 1554. Springer, Singapore. https://doi.org/10.1007/978-981-19-1166-8_7
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