Applicability of Anomaly Detection Knowledge from Computer Science and Mathematics Publications to Oil and Gas Research
DOI:
https://doi.org/10.25729/esr.2024.01.0002Keywords:
anomaly detection, SPE, bibliometric data, algorithms, MDPI, computer science and mathematicsAbstract
Anomaly detection in equipment processes is crucial for the oil and gas sector. Algorithms for detecting anomalies in measured data are best understood in Computer Science and Mathematics. Therefore, a possible transfer of knowledge from the latter area to the former can have a profound impact. This paper explores the potential for the knowledge transfer by analyzing bibliometric data of Computer Science and Mathematics papers published in MDPI journals, as well as publications available on SPE search platform. The research shows that the main algorithms extensively studied in MDPI publications and found in SPE publications, which address the anomaly detection problem, include Random Forest, Support Vector Machine, Long-term Memory Method and Recurrent Neural Network. The main advantages and disadvantages of these methods are briefly described. This paper provides examples of classical and highly cited publications that describe the work of these algorithms along with examples of articles that illustrate their application in the oil and gas industry. The sections of SPE disciplines with the largest number of publications using the algorithms that are frequently used for anomaly detection are presented.
References
A. Qassab et al., “Autonomous Inspection System for Anomaly Detection in Natural Gas Pipelines,” in Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, Nov. 2020, p. D041S105R002. DOI: 10.2118/202825-MS.
J. Snyder, S. Scott, and R. Kassim, “Self-Adjusting Anomaly Detection Model for Well Operation and Production in Real-Time,” in SPE Oklahoma City Oil and Gas Symposium, Oklahoma City, Oklahoma, USA, Apr. 2019, p. D011S002R005. DOI: 10.2118/195234-MS.
P. Santos et al., “AI Augmented Engineering Intelligence for Industrial Equipment,” in SPE Offshore Europe Conference & Exhibition, Aberdeen, Scotland, UK, Sep. 2023, p. D021S006R003. DOI: 10.2118/215491-MS.
F. Beduschi et al., “Optimizing Rotating Equipment Maintenance Through Machine Learning Algorithm,” in Abu Dhabi International Petroleum Exhibition & Conference, Abu Dhabi, UAE, Nov. 2021., p. D031S088R001. DOI: 10.2118/207657-MS.
Z. S. Irani, R. George, R. Dayal, “Technology for Continuous Cyber Monitoring of Offshore Assets,” in ADIPEC, Abu Dhabi, UAE, Oct. 2023, p. D031S101R003. DOI: 10.2118/217071-MS.
L. Concetti, G. Mazzuto, F. E. Ciarapica, M. Bevilacqua, “An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector,” Applied Sciences, vol. 13, no. 6, p. 3725, 2023. DOI: 10.3390/app13063725.
S. S. Aljameel et al., “An Anomaly Detection Model for Oil and Gas Pipelines Using Machine Learning,” Computation, vol. 10, no. 8, p. 138, 2022. DOI: 10.3390/computation10080138.
L. Coelho E Silva, M. C. R. Murça, “A data analytics framework for anomaly detection in flight operations,” Journal of Air Transport Management, vol. 110, p. 102409, 2023. DOI: 10.1016/j.jairtraman.2023.102409.
E. Quatrini, F. Costantino, G. Di Gravio, R. Patriarca, “Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities,” Journal of Manufacturing Systems, vol. 56, pp. 117–132, 2020. DOI: 10.1016/j.jmsy.2020.05.013.
X. He, E. Robards, R. Gamble, M. Papa, “Anomaly Detection Sensors for a Modbus-Based Oil and Gas Well-Monitoring System,” in 2019 2nd International Conference on Data Intelligence and Security (ICDIS), South Padre Island, TX, USA: IEEE, Jun. 2019, pp. 1–8. DOI: 10.1109/ICDIS.2019.00008.
J. Da Silva Arantes, M. Da Silva Arantes, H. B. Fröhlich, L. Siret, R. Bonnard, “A novel unsupervised method for anomaly detection in time series based on statistical features for industrial predictive maintenance,” Int. J. Data Sci. Anal., vol. 12, no. 4, pp. 383–404, 2021. DOI: 10.1007/s41060-021-00283-z.
T. H. A. Musa, A. Bouras, “Anomaly Detection: A Survey,” in Proceedings of Sixth International Congress on Information and Communication Technology, vol. 217, X.-S. Yang, S. Sherratt, N. Dey, and A. Joshi, Eds. Singapore: Springer Singapore, 2022, pp. 391–401. DOI: 10.1007/978-981-16-2102-4_36.
A. B. Nassif, M. A. Talib, Q. Nasir, F. M. Dakalbab, “Machine Learning for Anomaly Detection: A Systematic Review,” IEEE Access, vol. 9, pp. 78658–78700, 2021. DOI: 10.1109/ACCESS.2021.3083060.
S. Osiński, D. Weiss, “Carrot2: Design of a Flexible and Efficient Web Information Retrieval Framework,” in Advances in Web Intelligence, vol. 3528, P. S. Szczepaniak, J. Kacprzyk, and A. Niewiadomski, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 439–444. DOI: 10.1007/11495772_68.
C. Carpineto, S. Osiński, G. Romano, D. Weiss, “A survey of Web clustering engines,” ACM Comput. Surv., vol. 41, no. 3, pp. 1–38, 2009. DOI: 10.1145/1541880.1541884.
R. Krovetz, “Viewing morphology as an inference process,” Artificial Intelligence, vol. 118, no. 1–2, pp. 277–294, 2000. DOI: 10.1016/S0004-3702(99)00101-0.
A. S. Nayak, A. P. Kanive, N. Chandavarkar, R. Balasubramani, “Survey on Pre-Processing Techniques for Text Mining,” IJECS, vol. 5, no. 6, pp. 16875–16879, 2016. DOI: 10.18535/ijecs/v5i6.25.
L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. DOI: 10.1023/A:1010933404324.
A. AlSaihati, S. Elkatatny, A. Mahmoud, A. Abdulraheem, “Early Anomaly Detection Model Using Random Forest while Drilling Horizontal Wells with a Real Case Study,” in SPE/IADC Middle East Drilling Technology Conference and Exhibition, Abu Dhabi, UAE, May 2021, p. D032S040R001. DOI: 10.2118/202144-MS.
B. Alharbi, Z. Liang, J. M. Aljindan, A. K. Agnia, X. Zhang, “Explainable and Interpretable Anomaly Detection Models for Production Data,” SPE Journal, vol. 27, no. 01, pp. 349–363, 2022. DOI: 10.2118/208586-PA.
O. Akinsete, A. Oshingbesan, “Leak Detection in Natural Gas Pipelines Using Intelligent Models,” in SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, Aug. 2019, p. D023S009R001. DOI: 10.2118/198738-MS.
S. Tewari, U. D. Dwivedi, M. Shiblee, “Assessment of Big Data Analytics Based Ensemble Estimator Module for the Real-Time Prediction of Reservoir Recovery Factor,” in SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, Mar. 2019, p. D041S038R003. DOI: 10.2118/194996-MS.
M. A. Del Pino Fiorillo, “Automating Dynamometer Charts Interpretation with Machine Learning,” in SPE Latin American and Caribbean Petroleum Engineering Conference, Port of Spain, Trinidad and Tobago, Jun. 2023, p. D021S010R003. DOI: 10.2118/213191-MS.
C.-C. Chang, C.-J. Lin, “LIBSVM: A library for support vector machines,” ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 1–27, 2011. DOI: 10.1145/1961189.1961199.
R. Akkurt et al., “An Unsupervised Stochastic Machine Learning Approach for Well Log Outlier Identification,” in Proceedings of the 10th Unconventional Resources Technology Conference, Houston, Texas, USA: American Association of Petroleum Geologists, 2022. DOI: 10.15530/urtec-2022-3721358.
B. Alotaibi, B. Aman, M. Nefai, “Real-Time Drilling Models Monitoring Using Artificial Intelligence,” SPE Middle East Oil and Gas Show and Conference, Manama, Bahrain, Mar. 2019, p. D021S001R004. DOI: 10.2118/194807-MS.
C. Szegedy et al., “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA: IEEE, Jun. 2015, pp. 1–9. DOI: 10.1109/CVPR.2015.7298594.
H. Zhang et al., “Drilling and Completion Anomaly Detection in Daily Reports by Deep Learning and Natural Language Processing Techniques,” in Proceedings of the 8th Unconventional Resources Technology Conference, Online: American Association of Petroleum Geologists, 2020. DOI: 10.15530/urtec-2020-2885.
R. Mercante, T. A. Netto, “Virtual Multiphase Flowmeter Using Deep Convolutional Neural Networks,” SPE Journal, vol. 28, no. 05, pp. 2448–2461, 2023. DOI: 10.2118/214681-PA.
K. Zhang et al., “Prediction of Field Saturations Using a Fully Convolutional Network Surrogate,” SPE Journal, vol. 26, no. 04, pp. 1824–1836, 2021. DOI: 10.2118/205485-PA.
S. A. Marhon, C. J. F. Cameron, S. C. Kremer, “Recurrent Neural Networks,” in Handbook on Neural Information Processing, vol. 49, M. Bianchini, M. Maggini, and L. C. Jain, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 29–65. DOI: 10.1007/978-3-642-36657-4_2.
A. Alakeely, R. N. Horne, “Simulating the Behavior of Reservoirs with Convolutional and Recurrent Neural Networks,” SPE Reservoir Evaluation & Engineering, vol. 23, no. 03, pp. 0992–1005, 2020. DOI: 10.2118/201193-PA.
C. G. Ezechi, E. R. Okoroafor, “Integration of Artificial Intelligence with Economical Analysis on the Development of Natural Gas in Nigeria; Focusing on Mitigating Gas Pipeline Leakages,” in SPE Nigeria Annual International Conference and Exhibition, Lagos, Nigeria, Jul. 2023, p. D031S018R004. DOI: 10.2118/217163-MS.
Q. Yin et al., “Machine Learning for Deepwater Drilling: Gas-Kick-Alarm Classification Using Pilot-Scale Rig Data with Combined Surface-Riser-Downhole Monitoring,” SPE Journal, vol. 26, no. 04, pp. 1773–1799, 2021. DOI: 10.2118/205365-PA.
S. Hochreiter, J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. DOI: 10.1162/neco.1997.9.8.1735.
M. C. Kara, M. Majeran, B. Peterson, T. Wimberly, G. Sinclair, “A Machine Learning Workflow to Predict Anomalous Sanding Events in Deepwater Wells,” in Offshore Technology Conference, Virtual and Houston, Texas, Aug. 2021, p. D031S033R002. DOI: 10.4043/31234-MS.
P. Nivlet et al., “Towards Real-Time Bad Hole Cleaning Problem Detection Through Adaptive Deep Learning Models,” in Middle East Oil, Gas and Geosciences Show, Manama, Bahrain, Feb. 2023, p. D021S073R005. DOI: 10.2118/213643-MS.
P. Aditama, T. Koziol, Dr. M. Dillen, “Development of an Artificial Intelligence-Based Well Integrity Monitoring Solution,” in ADIPEC, Abu Dhabi, UAE, October 2022, p. D031S110R004. DOI: 10.2118/211093-MS.
C. Ledig et al., “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI: IEEE, Jul. 2017, pp. 105–114. DOI: 10.1109/CVPR.2017.19.
Z. Zhang, W. Song, W. Wang, “Broadband reconstruction of seismic signal with generative recurrent adversarial network,” in Second International Meeting for Applied Geoscience & Energy, Houston, Texas: Society of Exploration Geophysicists and American Association of Petroleum Geologists, Aug. 2022, pp. 2178–2182. DOI: 10.1190/image2022-3746443.1.
F. Marques, P. Costa, F. Castro, M. Parente, “Self-Supervised Subsea SLAM for Autonomous Operations,” in Offshore Technology Conference, Houston, Texas, May 2019, p. D011S002R006. DOI: 10.4043/29602-MS.
R. S. Sutton, A. G. Barto, “Reinforcement Learning: An Introduction,” IEEE Trans. Neural Netw., vol. 9, no. 5, pp. 1054–1054, 1998. DOI: 10.1109/TNN.1998.712192.
M. Alzahrani, B. Alotaibi, B. Aman, “Novel Stuck Pipe Troubles Prediction Model Using Reinforcement Learning,” in International Petroleum Technology Conference, Riyadh, Saudi Arabia, Feb. 2022, p. D021S042R003. DOI: 10.2523/IPTC-22151-MS.
R. Miftakhov, A. Al-Qasim, I. Efremov, “Deep Reinforcement Learning: Reservoir Optimization from Pixels,” in International Petroleum Technology Conference, Dhahran, Kingdom of Saudi Arabia, Jan. 2020., p. D021S052R002. DOI: 10.2523/IPTC-20151-MS.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Energy Systems Research
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
