Estimation of Oil Flow Production of Well Employing Machine Learning Algorithms Using Electrical Submersible Pump (ESP)

Document Type : Research Paper

Authors

1 School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran

2 Faculty of Chemical, Oil and Gas Engineering, Iran University of Science and Technology, Tehran, Iran

10.22078/pr.2024.5417.3415

Abstract

Estimating the flow rate in oil wells of a field is a vital and practical process. However, the flows extracted from oil wells are multiphase, and their accurate estimation is highly challenging and costly. Virtual flow meters, compared to multiphase flow meters and well-testing methods, are an economically viable option that can accurately predict future flow rates by leveraging existing data and artificial intelligence algorithms. Therefore, data-driven virtual flow meters have recently received significant attention. This paper estimates the production flow rate of a well using three machine learning algorithms: 1- k-nearest neighbors (k-NN); 2- gradient boosting (GBR); and 3- decision tree (DT), using pump data. Pearson and Spearman statistical analyses were used to select appropriate features as the algorithm inputs. Moreover, the dataset under investigation pertains to one of the wells of a southern oil field in Iran. The available dataset has a small volume and insufficient diversity, but despite this, the results show that the proposed algorithms perform well. The k-NN method, with an accuracy of 0.9494, performed better than the other two methods in estimating oil flow rate. Ultimatly, to examine the performance of the algorithms against noisy data, one percent of standard deviation noise was added to the input data. Moreover, the investigations showed that the k-NN model, with an accuracy of 0.9257, performed better than the other two methods and was least affected by the noise.

Keywords

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References

[1]. Dayev, Z. A. (2020). Application of artificial neural networks instead of the orifice plate discharge coefficient. Flow Measurement and Instrumentation, 71, 101674. doi.org/10.1016/j.flowmeasinst.2019.101674.##
[2]. Bikmukhametov, T., & Jäschke, J. (2020). First principles and machine learning virtual flow metering: a literature review. Journal of Petroleum Science and Engineering, 184, 106487. doi.org/10.1016/j.petrol.2019.106487. ##
[3]. Mercante, R., & Netto, T. A. (2022). Virtual flow predictor using deep neural networks. Journal of Petroleum Science and Engineering, 213, 110338. doi.org/10.1016/j.petrol.2022.110338.##
[4]. AL-Qutami, T. A., Ibrahim, R., Ismail, I., & Ishak, M. A. (2018). Virtual multiphase flow metering using diverse neural network ensemble and adaptive simulated annealing. Expert Systems with Applications, 93, 72-85. doi.org/10.1016/j.eswa.2017.10.014.  ##
[5]. Al-Qutami, T. A., Ibrahim, R., & Ismail, I. (2017, September). Hybrid neural network and regression tree ensemble pruned by simulated annealing for virtual flow metering application. In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 304-309). IEEE. ##
[6]. Ahmadi, M. A., Ebadi, M., Shokrollahi, A., & Majidi, S. M. J. (2013). Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Applied Soft Computing, 13(2), 1085-1098. doi.org/10.1016/j.asoc.2012.10.009 ##
[7]. Bikmukhametov, T., & Jäschke, J. (2019). Oil production monitoring using gradient boosting machine learning algorithm. Ifac-Papersonline, 52(1), 514-519. doi.org/10.1016/j.ifacol.2019.06.114.##
[8]. Góes, M. R. R., Guedes, T. A., d’Avila, T. C., Vieira, B. F., Ribeiro, L. D., de Campos, M. C., & Secchi, A. R. (2021). Virtual flow metering of oil wells for a pre-salt field. Journal of Petroleum Science and Engineering, 203, 108586. doi.org/10.1016/j.petrol.2021.108586.‏##
[9]. Hotvedt, M., Grimstad, B., Ljungquist, D., & Imsland, L. (2022). On gray-box modeling for virtual flow metering. Control Engineering Practice, 118, 104974. doi.org/10.1016/j.conengprac.2021.104974. ##
[10]. AlAjmi, M. D., Alarifi, S. A., & Mahsoon, A. H. (2015, March). Improving multiphase choke performance prediction and well production test validation using artificial intelligence: a new milestone. In SPE Digital Energy Conference and Exhibition (p. D031S022R003). SPE. doi.org/SPE-173394-MS. ##
[11]. Sandnes, A. T., Grimstad, B., & Kolbjørnsen, O. (2021). Multi-task learning for virtual flow metering. Knowledge-Based Systems, 232, 107458. doi.org/10.1016/j.knosys.2021.107458.‏##
[12]. Al-Jasmi, A., Goel, H.K., Nasr, H., Querales, M., Rebeschini, J., Villamizar, M.A., Carvajal, G.A., Knabe, S., Rivas, F. and Saputelli, L., (2013), June. Short-term production prediction in real time using intelligent techniques. In SPE Europec featured at EAGE Conference and Exhibition? (pp. SPE-164813). SPE. doi.org/10.2118/164813-MS.‏##
[13]. Denney, T., Wolfe, B., & Zhu, D. (2013, March). Benefit evaluation of keeping an integrated model during real-time ESP operations. In SPE Digital Energy Conference and Exhibition (pp. SPE-163704). SPE. doi.org/10.2118/163704-MS.##
[14]. Camilleri, L. A., Banciu, T., Ditoiu, G., & Petrom, S. A. (2010, March). First Installation of 5 ESPs Offshore Romania-A Case Study and Lessons Learned. In SPE Intelligent Energy International Conference and Exhibition (pp. SPE-127593). SPE. doi: 10.2118/127593-MS.##
[15]. Rao, H., Shi, X., Rodrigue, A.K., Feng, J., Xia, Y., Elhoseny, M., Yuan, X. and Gu, L., (2019). Feature selection based on artificial bee colony and gradient boosting decision tree. Applied Soft Computing, 74, 634-642.doi.org/10.1016/j.asoc.2018.10.036.##
[16]. Dudani, S. A. (1978). The distance-weighted k-nearest neighbor rule. IEEE trans. on systems, man and cybernetics, 8(4), 311-313.‏##
[17]. Jóźwik, A. (1983). A learning scheme for a fuzzy k-NN rule. Pattern Recognition Letters, 1(5-6), 287-289.‏ ##
[18]. Song, Y., Liang, J., Lu, J., & Zhao, X. (2017). An efficient instance selection algorithm for k nearest neighbor regression. Neurocomputing, 251, 26-34.‏ doi.org/10.1016/0167-8655(83)90064-8.##
[19]. Devroye, L., Gyorfi, L., Krzyzak, A., & Lugosi, G. (1994). On the strong universal consistency of nearest neighbor regression function estimates. The Annals of Statistics, 22(3), 1371-1385. doi.org/10.1214/aos/1176325633.‏##
[20]. Xu, M., Watanachaturaporn, P., Varshney, P. K., & Arora, M. K. (2005). Decision tree regression for soft classification of remote sensing data. Remote Sensing of Environment, 97(3), 322-336. doi.org/10.1016/j.rse.2005.05.008.‏##
[21]. Breiman, L. (2017). Classification and regression trees. Routledge. 1st Edition. 25 October 2017. New York. Chapman and Hall/CRC. doi.org/10.1201/9781315139470.##
Journal of Petroleum Research
Volume 34, 1403-4 - Serial Number 137
November and December 2024
Pages 156-171

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