«Proceedings of Gubkin University»

Ensuring safety of well construction based on intelligent early trouble prevention systems

UDC: 331.45:622.24:681.518
DOI: 10.33285/2073-9028-2022-1(306)-40-51

Authors:

¹ Oil and gas research institute Russian Academy of Sciences, Moscow, Russian Federation
² Gubkin Russian State University (National Research University) of Oil and Gas, Moscow, Russian Federation

Keywords: well construction safety, machine learning methods, Big GeoData, geological and technological research, neural network model, drilling of oil and gas wells, detection, trouble prediction and prevention

Annotation:

This article poses and provides solutions of ensuring the safety of the construction of oil and gas wells onshore and offshore using intelligent systems for early trouble prevention based on the results of processing Big GeoData from mudlogging measurements. The advantage of using artificial neural networks to solve the problems of identifying and predicting complications in the construction of oil and gas wells is that in the course of their creation and training, explicit and hidden patterns between geological and geophysical, technical and technological parameters are revealed with a given accuracy. Efficient formation, integration and clustering of ever-increasing multidimensional data volumes from sensors of various types used to measure parameters in the process of drilling wells is carried out using artificial intelligence technologies.

Bibliography:

1. Shirieva N.S., Shiriev A.K., Tlyasheva R.R. & Kafisov F.S. Development of Hazard Assessment Matrix for Wells as an Industrial Safety Assurance Method When Designing Oil and Gas Well Construction. Day 4 Thu, October 29, 2020. DOI: 10.2118/202038-ms
2. Zhao Ying, Sun Ting, Yang Jin, Yin Qishuai, Wei Hongshu, Liu Zhengli, Li Zhong and Yi Huang. “Combining Drilling Big Data and Machine Learning Method to Improve the Timeliness of Drilling”. SPE/IADC International Drilling Conference and Exhibition, The Hague, The Netherlands, March 2019. DOI: https://doi.org/10.2118/194111-MS
3. Carpenter C. Big Data and Machine Learning Optimize Operational Performance and Drill-Bit Design. Journal of Petroleum Technology, no. 73 (12), p. 49–50. DOI: 10.2118/1221-0049-jpt
4. Cornel S., Vazquez G. Use of Big Data and Machine Learning to Optimise Operational Performance and Drill Bit Design. Day 2 Wed, November 18, 2020. DOI: 10.2118/202243-ms
5. Zhang Z., Lai X., Wu M. & Du S. Incident early warning based on sparse autoencoder and decision fusion for drilling process. IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. DOI: 10.1109/iecon48115. 2021.9589058
6. Said Mohammed Mokhtar, Pilgrim Rick, Rideout Geoff, and Stephen Butt. “Theoretical Development of a Digital-Twin Based Automation System for Oil Well Drilling Rigs”. Paper presented at the SPE Canadian Energy Technology Conference, Calgary, Alberta, Canada, March 2022. DOI: https://doi.org/10.2118/208902-MS
7. Avtomatizirovannaya sistema predotvrashcheniya avarij pri stroitel’stve skvazhin. A.N. Dmitrievskij, N.A. Eremin, A.D. CHernikov, A.G. Sboev, O.K. CHashchina-Semenova, L.K. Ficner, M.Ya. Gel’fgat, A.A. Nazaretova. Neftyanoe hozyajstvo, 2021, no. 1, p. 72–76. DOI: 10.24887/0028-2448-2021-1-72-76
8. Application of artificial intelligence methods for identifying and predicting complications in the construction of oil and gas wells: problems and solutions. A.D. Chernikov, N.A. Eremin, V.E. Stolyarov, A.G. Sboev, O.K. Semenova-Chashchina, L.K. Fitsner. Georesursy–Georesources, 2020, no. 22 (3), p. 87–96. DOI: https://doi.org/10.18599/grs.2020.3.87-96
9. Prevention of complications and accidents in the process of drilling oil and gas wells using machine learning methods. A.N. Dmitrievsky, A.G. Sboev, N.A. Eremin, A.D. Chernikov, A.V. Naumov, A.V. Gryaznov, I.A. Moloshnikov, S.O. Borozdin, E.A. Safarova. Georesursy–Georesources, issue 4, p. 79–85. DOI: https://doi.org/10.18599/grs.2020.4.79-85
10. Analiz kachestva dannyh stancii geologo-tekhnologicheskih issledovanij pri raspoznavanii pogloshchenij i gazoneftevodoproyavlenij dlya povysheniya tochnosti prognozirovaniya nejrosetevyh algoritmov. A.I. Arhipov, A.N. Dmitrievskij, N.A. Eremin, A.D. CHernikov, S.O. Borozdin, E.A. Safarova, M.R. Sejnaroev. Neftyanoe hozyajstvo, 2020, no. 8 (1162), p. 63–67. DOI: 10.24887/0028-2448-2020-8-63-67
11. Drilling Problems Forecast System Based on Neural Networ. S. Borozdin, A. Dmitrievsky, N. Eremin, A. Arkhipov, A. Sboev, O. Chashchina-Semenova, L. Fitzner, E. Safarova. SPE Annual Caspian Technical Conference, 2020. DOI: 10.2118/202546-MS
12. Primenenie metodov iskusstvennogo intellekta v zadachah predotvrashcheniya avarijnyh situacij pri stroitel'stve skvazhin. A.N. Dmitrievskij, N.A. Eremin, A.I. Arhipov [i dr.] Nedropol’zovanie XXI vek, 2021, no. 5–6 (92), p. 6–15.