«Onshore and offshore oil and gas well construction»

Use of artificial intelligence when controlling and searching for optimal solutions when drilling oil and gas wells

UDC: 004.032.26+622.248.54
DOI: -

Authors:

¹ Kuban State Technological University, Krasnodar, Russia

Keywords: artificial intelligence, neural networks, well drilling, accident prevention, automated system

Annotation:

In the oil and gas industry, the use of artificial intelligence (AI) helps improving the efficiency and productivity of technologies. It leads to more successful results, thus emphasizing the importance of AI in the industry. Production technologies are developing thanks to the production and infrastructure evolution, taking into account the terms of equipment use and condition, ensuring the creation of effective business processes based on intelligent and robotic control, as well as the implementation of machine learning algorithms for working with big data. The key role is played by the sequence of stages implementation: from data collection up to integration of objects into a single control system with support for remote monitoring, formation of intelligent control with reduction of human involvement, subsequent standardization and scaling of solutions. The authors of the article propose using artificial intelligence methods to improve planning processes, as well as preventing complications and emergency situations. Methods for optimizing the drilling process of oil and gas wells are considered.

Bibliography:

1. Shmelev V.A. Upravlenie protsessom bureniya neftyanykh i gazovykh skvazhin s tsel'yu povysheniya ego effektivnosti // Str-vo neftyanykh i gazovykh skvazhin na sushe i na more. – 2022. – № 6(354). – S. 5–12. – DOI: 10.33285/0130-3872-2022-6(354)-5-12
2. Shmelev V.A. Avtomatizirovannaya sistema adaptivnogo upravleniya bureniem neftyanykh i gazovykh skvazhin // Str-vo neftyanykh i gazovykh skvazhin na sushe i na more. – 2021. – № 12(348). – S. 29–34. – DOI: 10.33285/0130-3872-2021-12(348)-29-34
3. Gharbi R.B.C., Mansoori G.A. An introduction to artificial intelligence applications in petroleum exploration and production // J. of Petroleum Science and Engineering. – 2005. – Vol. 49, Issue 3. – R. 93–96. – DOI: 10.1016/j.petrol.2005.09.001
4. Dodiya M., Shah M. A systematic study on shaping the future of solar prosumage using deep learning // Int. J. of Energy and Water Resources. – 2021. – Vol. 5, Issue 4. – P. 477–487. – DOI: 10.1007/s42108-021-00114-8
5. Desai M., Shah M. An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN) // Clinical eHealth. – 2020. – Vol. 4, Issue 1. – P. 1–11. – DOI: 10.1016/j.ceh.2020.11.002
6. Panchiwala S., Shah M. A Comprehensive Study on Critical Security Issues and Challenges of the IoT World // J. of Data, Information and Management. – 2020. – Vol. 2, Issue 4. – P. 257–278. – DOI: 10.1007/s42488-020-00030-2
7. Comparative anatomization of data mining and fuzzy logic techniques used in diabetes prognosis / H. Thakkar, V. Shah, H. Yagnik, M. Shah // Clinical eHealth. – 2020. – Vol. 4, Issue 1. – P. 12–23. – DOI: 10.1016/j.ceh.2020.11.001
8. Rahmanifard H., Plaksina T. Application of artificial intelligence techniques in the petroleum industry: a review // Artificial Intelligence Review. – 2019. – Vol. 52, Issue 4. – P. 2295–2318. – DOI: 10.1007/s10462-018-9612-8
9. Reservoir Engineer and Artificial Intelligence Techniques for Data Analysis / O. Tapias, C.P. Soto, J. Sandoval [et al.] // SPE Asia Pacific Oil and Gas Conf. and Exhibition, Jakarta, Indonesia, April 17–19, 2001. – DOI: 10.2118/68743-MS
10. Avtomatizirovannaya sistema predotvrashcheniya avariy pri stroitel'stve skvazhin / A.N. Dmitrievskiy, N.A. Eremin, A.D. Chernikov [i dr.] // Neft. khoz-vo. – 2021. – № 1. – S. 72–76. – DOI: 10.24887/0028-2448-2021-1-72-76
11. Dmitrievskiy A.N., Eremin N.A., Stolyarov V.E. Rol' informatsii v primenenii tekhnologiy iskusstvennogo intellekta pri stroitel'stve skvazhin dlya neftegazovykh mestorozhdeniy // Nauch. zhurn. Rossiyskogo gazovogo obshchestva. – 2020. – № 3(26). – S. 6–21.