Artificial lift system optimization using machine learning applications

Abstract

Currently, in the oil industry, artificial lift optimization (ALO) systems are dealing with different applications including well monitor and control, reservoir management, production optimization, predictive maintenance, artificial lift, and flow assurance, multiphase pumping systems, etc. The scope of this article is to provide a detailed analysis of ALO and predictive pump maintenance applications using machine learning (ML) and artificial intelligence (AI). The oil and gas industry has experienced a lot of improvements that have impacted the businesses and economies associated with the market in recent times. Issues such as unplanned shutdown time and failure of equipment cause a huge impact on many corporations especially with the current fluctuations in hydrocarbon prices. Similarly, advanced modern technologies such as real-time analysis and predictive maintenance are designed to drive ALO systems. This paper covers several applications and techniques in which ML and AI have been applied to optimize hydrocarbon withdrawal from potentially depleted reservoirs that require some external supports to uplift the reservoir fluid from sub surface to surface using artificial lift system. In a nutshell, the applications of AI and ML for the artificial lift selection, their predictive maintenance, equipment malfunctioning detection, etc. using a self-trained system are the main topics of this paper. While reviewing each of these techniques, the workflow is also explained along with the effectiveness of utilizing each application to the current operations.

Keywords

Artificial lift optimization / Predictive maintenance

Conflict of interests

This is an original work that has not been published before. In addition, it has not been submitted for publication elsewhere. There is no conflict of interest among authors to submit this manuscript. All authors have approved the enclosed manuscript for publication.

References

[1] J. Valbuena, E. Pereyra, C. Sarica, Defining the Artificial Lift System Selection Guidelines for Horizontal Wells, Society of Petroleum Engineers, 2016, October 17, https://doi.org/10.2118/181229-MS.

[2] F. Clarke, L. Malone, Sucker Rod Pumping in the Eagle Ford Shale Field Study, Society of Petroleum Engineers, 2016, October 17, https://doi.org/10.2118/181214-MS.

[3] A.L. Kurkjian, Optimizing Jet-Pump Production in the Presence of Gas, Society of Petroleum Engineers, 2018, https://doi.org/10.2118/191382-PA.

[4] P. Toma, R. Coates, D. Nguyen, R. Ivey, W. Good, Improved design of rod pump valves for thermal and horizontal wells e laboratory and field results, J. Can. Petrol. Technol. 37 (5) (1998) 67-77.

[5] A. Monk, J. Rutledge, Enhancing well optimization through rod lift automation. Presented at the Southwestern Petroleum Short Course, April, Lubbock, Texas, 2018, pp. 16-19.

[6] E. Orji, J. Lissanon, O. Omole,Sucker rod lift system optimization of an unconventional well, in:Presented at the SPE North American Artificial Lift Conference and Exhibition, 2016. The Woodlands, TX, 25-27 October. SPE-181242-MS.

[7] D.W. Kimery, J.C. Saponja, R.C. Chachula, C. Jensen, Breaking the 800 Psi ESP PIP Barrier: How A Proven Flow-Conditioning Technology Can Dramatically Improve ESP Performance in Horizontal Wells, Society of Petroleum Engineers, 2017, https://doi.org/10.2118/185128-MS.

[8] D. Snyder, B. Ellithorp, J. Snyder, Casing gas separator e a simple method to double gas separation capacity in new wells. Presented at the Southwestern Petroleum Short Course, April, Lubbock, Texas, 2018, pp. 16-19.

[9] D. Snyder, B. Ellithorp, Casing Gas Separator e Initial Installation Learnings and Design Progressions, Presented at the Southwestern Petroleum Short Course, Lubbock, Texas, 2019, pp. 15-18.

[10] S.C. Kennedy, Z.T. Madrazo, C. Rhinehart, B. Hill, C.M. Grimm, C. Smith, New ESP Gas Separator for Slugging Horizontal Wells, Society of Petroleum Engineers, 2017, https://doi.org/10.2118/185147-MS.

[11] T.G. Freet, K.P. McCaslin, Successful Submersible Lift Operations in Gassy Horizontal Wells, Pearsall Field, Society of Petroleum Engineers, Texas, 1992, https://doi.org/10.2118/24763-MS.

[12] D. Sask, D. Kola, T. Tuftin, Plunger Lift Optimization in Horizontal Gas Wells: Case Studies and Challenges, Society of Petroleum Engineers, 2010, https://doi.org/10.2118/137860-MS.

[13] Poór Peter, Josef Basl,Predictive Maintenance as an Intelligent Service in Industry 4.0, 2019, https://doi.org/10.20470/jsi.v10i1.364.

[14] R. Bigliani, Reducing risk in oil and gas operations, White Paper, http://www.exterramonitoring.com/files/minimizing-operational-risk-in-oil-gas-industry. pdf, 2013.

[15] J.S. Akosa, Predictive accuracy: a misleading performance measure for highly imbalanced data, SAS Proceeding, Oklahoma, 2017.

[16] P. Bangert,Predicting and detecting equipment malfunctions using machine learning, Offshore Technology Conference (2017, October 24), https://doi.org/10.4043/28109-MS.

[17] C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.

[18] W.G. Elmer, Artificial Lift Applications for the Internet of Things, Society of Petroleum Engineers, 2017, October 9, https://doi.org/10.2118/187390-MS.

[19] R.v. Flatern, The Defining Series: Electrical Submersible Pumps, 2015. https://www.slb.com/resource-library/oilfield-review/defining-series/defining-esp.

[20] N. Jansen Van Rensburg, L. Kamin, S. Davis, Robert), Using Machine Learning-Based Predictive Models to Enable Preventative Maintenance and Prevent ESP Downtime, Society of Petroleum Engineers, 2019, November 11, https://doi.org/10.2118/197146-MS.

[21] Jarjis Muhammad, Sana Abdulwahab, Shwan Rajab, Pshtiwan Jaf, Production Optimization Using Gas Lift Technique, Koya University, 2019. MS Thesis.

[22] O. Kolawole, T. Gamadi, D. Bullard, October 15). Comprehensive Review of Artificial Lift System Applications in Tight Formations, Society of Petroleum Engineers, 2019, https://doi.org/10.2118/196592-MS.

[23] R. Lastra, Electrical Submersible Pump Digital Twin, the Missing Link for Successful Condition Monitoring and Failure Prediction, Society of Petroleum Engineers, 2019, November 11, https://doi.org/10.2118/197156-MS.

[24] M.G. Mayani, T. Baybolov, R. Rommetveit, S.I. Ødegaard, V. Koryabkin, S. Lakhtionov, Optimizing Drilling Wells and Increasing the Operation Efficiency Using Digital Twin Technology, Society of Petroleum Engineers, 2020, February 25, https://doi.org/10.2118/199566-MS.

[25] M.A. Naguib, A. El Battrawy, A. Bayoumi, N. El-Emam, Guideline of Artificial Lift Selection for Mature Field, Society of Petroleum Engineers, 2000, January 1, https://doi.org/10.2118/64428-MS.

[26] T. Ounsakul, T. Sirirattanachatchawan, W. Pattarachupong, Y. Yokrat, P. Ekkawong,Artificial lift selection using machine learning, International Petroleum Technology Conference (2019, March 22), https://doi.org/10.2523/IPTC-19423-MS.

[27] R.v. Loon, bigdata-made simple [Online]. Available, http://bigdatamadesimple. com/machine-learning-explained-understanding-supervised-unsupervisedandreinforcement-learning/, 05 Feb 2018. (Accessed 6 August 2018).

[28] S. Telford, M.I. Mazhar, I. Howard, Condition Based Maintenance (CBM) in the Oil and Gas Industry: an Overview of Methods and Techniques, 2011.