Tel: +998977838464

No. 10 (2026)
Marketing

Scientific articles

NEFT-GAZ KOMPANIYASIDA BOSHQARUV QARORLARINI QABUL QILISHDA SUNʼIY INTELLEKT TEXNOLOGIYALARIDAN FOYDALANISHNING TASHKILIY-IQTISODIY MEXANIZMI

DOI 10.67668/mj/2026iss10/2205

Published 2026-10-15

Authors
Abdullayev Munis Kurbonovich
Marketing — 10-son, oktabr 2026
pdf (Uzbek) Google Scholar Google Scholar

Abstract

Maqolada neft-gaz kompaniyasida boshqaruv qarorlarini qabul qilish jarayoniga sunʼiy intellekt texnologiyalarini integratsiya qilishning tashkiliy-iqtisodiy mexanizmi asoslandi. Tadqiqot qarorni prognoz va mulohaza birikmasi sifatida talqin qiluvchi yondashuv, inson va algoritm oʻrtasidagi qaror tuzilmalari konsepsiyasi, maʼlumot sifati hamda institutsional nazorat qarashlarini yagona tahliliy tizimga birlashtirdi. “Muborak gazni qayta ishlash zavodi” misolida qaror qabul qilish arxitekturasining joriy holati, maʼlumotlar muhitini takomillashtirish zaxiralari va ularning sunʼiy intellekt modellari aniqligiga taʼsiri baholandi. Operatsion, taktik va strategik qarorlar uchun sunʼiy intellekt usullari hamda inson-algoritm hamkorligi shakllarini moslashtiruvchi matritsa, toʻrt qatlamli integratsiya mexanizmi taklif etildi va tegishli loyihalarning iqtisodiy samaradorligi asoslandi. Tahlil maʼlumotlar poydevoriga yoʻnaltirilgan nisbatan kichik investitsiyalar sunʼiy intellekt loyihalari samarasini sezilarli oshirishini koʻrsatdi. Xulosada maʼlumotlar boshqaruvi, qaror huquqlari va algoritmlar auditiga oid takliflar bayon etildi.

Keywords

sunʼiy intellekt, boshqaruv qarori, neft-gaz kompaniyasi, bashoratli tahlil, inson-algoritm hamkorligi, maʼlumot sifati, raqamli yetuklik, algoritmlar auditi.

References

  1. Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Boston: Harvard Business Review Press.
  2. PwC. (2017). Sizing the prize: What is the real value of AI for your business and how can you capitalise? London: PricewaterhouseCoopers. https://www.pwc.com/gx/en/issues/analytics/assets/pwc-ai-analysis-sizing-the-prize-report.pdf
  3. International Energy Agency. (2017). Digitalisation and energy. Paris: IEA. https://www.iea.org/reports/digitalisation-and-energy
  4. Koroteev, D., & Tekic, Z. (2021). Artificial intelligence in oil and gas upstream: Trends, challenges, and scenarios for the future. Energy and AI, 3, 100041. https://doi.org/10.1016/j.egyai.2020.100041
  5. Oʻzbekiston Respublikasi Prezidentining 2020-yil 5-oktyabrdagi PF-6079-son “«Raqamli Oʻzbekiston – 2030» strategiyasini tasdiqlash va uni samarali amalga oshirish chora-tadbirlari toʻgʻrisida”gi Farmoni. https://lex.uz/uz/docs/5031048
  6. Oʻzbekiston Respublikasi Prezidentining 2024-yil 14-oktyabrdagi PQ-358-son “Sunʼiy intellekt texnologiyalarini 2030-yilga qadar rivojlantirish strategiyasini tasdiqlash toʻgʻrisida”gi Qarori. https://lex.uz/docs/-7158604
  7. Kahneman, D., Sibony, O., & Sunstein, C. R. (2021). Noise: A flaw in human judgment. New York: Little, Brown Spark.
  8. Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83. https://doi.org/10.1177/0008125619862257
  9. Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586. https://doi.org/10.1016/j.bushor.2018.03.007
  10. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192-210. https://doi.org/10.5465/amr.2018.0072
  11. Brynjolfsson, E., Rock, D., & Syverson, C. (2017). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics (NBER Working Paper No. 24001). Cambridge, MA: National Bureau of Economic Research. https://doi.org/10.3386/w24001
  12. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116. https://hbr.org/2018/01/artificial-intelligence-for-the-real-world
  13. Sircar, A., Yadav, K., Rayavarapu, K., Bist, N., & Oza, H. (2021). Application of machine learning and artificial intelligence in oil and gas industry. Petroleum Research, 6(4), 379-391. https://doi.org/10.1016/j.ptlrs.2021.05.009
  14. Azmi, P. A. R., Yusoff, M., & Mohd Sallehud-din, M. T. (2024). A review of predictive analytics models in the oil and gas industries. Sensors, 24(12), 4013. https://doi.org/10.3390/s24124013
  15. Sui, X., Lu, X., Ji, Y., Yang, Y., Peng, J., Li, M., & Han, G. (2025). Intelligent oil production management system based on artificial intelligence technology. Processes, 13(1), 133. https://doi.org/10.3390/pr13010133
  16. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., & Aroyo, L. M. (2021). “Everyone wants to do the model work, not the data work”: Data cascades in high-stakes AI. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-15). New York: ACM. https://doi.org/10.1145/3411764.3445518
  17. Cherepovitsyn, A. E., & Tretyakov, N. A. (2023). Development of new system for assessing the applicability of digital projects in the oil and gas sector. Journal of Mining Institute, 262, 628-642. https://pmi.spmi.ru/pmi/article/view/15795
  18. European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L series. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
  19. Begalov, B. A., & Abdullayev, M. K. (2023). Raqamli iqtisodiyot: darslik. Toshkent: Iqtisodiyot. https://inlibrary.uz/index.php/textbooks/article/view/23842
  20. Abdullayev, M. K., & Eshonqulova, Z. M. (2026). Biznes jarayonlarining raqamli transformatsiyasida maʼlumotlarga asoslangan boshqaruv qarorlarini shakllantirish metodologiyasi. Marketing, (9), 207-218.
  21. Abdullayev, M. K. (2026). Neft-gaz sanoatida raqamli texnologiyalarni joriy etish samaradorligini baholashning ilmiy-uslubiy yondashuvlari. Muhandislik va iqtisodiyot, (6), 492-497.

Downloads

Download data is not yet available.