QAZISH QUDUQLARINING MATEMATIK MODELLARI TAHLILI
Referat
Ushbu maqolada ishlatiladigan matematik modellarning keng qamrovli ko'rib chiqilishi keltirilgan
tog' jinslarining o'zgaruvchanligi sharoitida quduqni burg'ulash jarayonlarini tavsiflash
murakkab va geterogen geologik shakllanishlarga xosdir. In
litologik o'tishlar keskin bilan birga bo'lgan muhitlar
tog' jinslarining mustahkamligi va deformatsiya xususiyatlarining o'zgarishi, an'anaviy
penetratsiya tezligini bashorat qilish va optimal burg'ulashni tanlashga yondashuvlar
parametrlar etarli darajada samarali bo'lmaydi. Shuning uchun, hisobga olinadigan modellar
matkap uchining dinamik harakati va uning heterojen muhit bilan o'zaro ta'siri
alohida ahamiyat kasb etadi. Tadqiqot modellarning to'rtta asosiy sinfini ko'rib chiqadi:
fizik-mexanik, energiyaga asoslangan, kinematik va empirik. Har bir guruh
kiritish parametrlari, bashorat qilish aniqligi, moslashuvi asosida tizimlashtirilgan
o'zgaruvchan geologik sharoit va muhandislik vazifalarida amaliy qo'llanilishi.
Neyron kabi zamonaviy aqlli yondashuvlarga alohida e'tibor beriladi
tarmoqlar, loyqa mantiqiy tizimlar va o'z-o'zini o'rganishga qodir gibrid algoritmlar
real vaqt rejimida ishlash. Maqolada ko'p komponentli modellarni qurish xulosa qilinadi
jismoniy tamoyillarni mashinani o'rganish usullari bilan birlashtirish juda muhimdir. Bunday
integratsiya muhandislik prognozlarining ishonchliligini sezilarli darajada oshiradi;
operatsion xavflarni minimallashtiradi, burg'ulashning barqarorligi va samaradorligini ta'minlaydi
litologik jihatdan o'zgaruvchan va geomexanik jihatdan noaniq bo'lgan operatsiyalar
muhitlar.
Mualliflar haqida
Adabiyotlar ro'yxati
Maurer, W. C. (1962). The “Perfect-Cleaning” Theory of Rotary Drilling. Journal of Petroleum Technology,
(11), 1270–1274.
Teale, R. (1965). The Concept of Specific Energy in Rock Drilling. International Journal of Rock Mechanics
and Mining Sciences, 2(1), 57–73.
Toshniyozov L.G., Toshov J.B., Liu Songyong, Research of the stress-strain state of the rock in contact with
the elements of the drill bit during drilling // Technical science and innovation Article 17, Vol. 2020, Issue 3, (2020).
- 112-121
Bourgoyne, A. T., & Young, F. S. (1974). A Multiple Regression Approach to Optimal Drilling and Bit
Performance (SPE Paper No. 4926). Society of Petroleum Engineers.
Kalantari, S., Hashemalhosseini, H., & Baghbanan, A. (2018). Estimating rock strength parameters using
drilling data. International Journal of Rock Mechanics and Mining Sciences, 104, 45–52.
https://doi.org/10.1016/j.ijrmms.2018.02.005
Ayoub, M., Goh, S., Diab, D., & Ahmed, Q. (2017). Modeling of Drilling Rate of Penetration Using Adaptive
Neuro-Fuzzy Inference System. International Journal of Applied Engineering Research, 12(22), 12880–12891.
Hegde, C., & Gray, K. E. (2017). Use of Machine Learning and Data Analytics to Increase Drilling Efficiency
for Nearby Wells. Journal of Natural Gas Science and Engineering, 40, 327–335.
https://doi.org/10.1016/j.jngse.2017.02.010
Al Hamlawi, I. T., et al. (2021). MSE Based Drilling Optimizer Project for Large National Drilling Contractor.
In Proceedings of the SPE Annual Technical Conference and Exhibition, Abu Dhabi. Paper D032S234R001.
Стеклянов Б.Л., Штейнерт В.А., Рахимов Р.М. Динамические составляющие породоразрушающих
бурильных инструментов / «Steinert Industries GmbH & Co. KG», №6, - 2008, С. 19-21.
Тошов Ж. Б. Повышение эффективности бурения взрывных скважин на карьерах за счёт разработки
нового комбинированного долота: дис. … канд. техн. наук: 05.15.11 – Физические процессы горного
производства / Навоийский гос. горный ин-т. – Навоий, 2007. – 138 с.
Гурина Е. Г., Ключников Н. Ю., Поспелов А. Н. (2023). Гибридные модели для оценки аварийности
при бурении на основе телеметрии и машинного обучения. // Сборник научных трудов ТПУ. — №2. — С. 45–
OnePetro Technical Paper (2025). Hybrid Physics-ML Models for Real-Time ROP Prediction. // SPE/IADC
Drilling Conference & Exhibition. https://www.onepetro.org/conference-paper/SPE-215400-MS
Willard J., Jia X., Xu L., Steinbach M., Kumar V. (2021). Integrating Physics-Based Modeling with Machine
Learning: A Survey. // arXiv preprint arXiv:2003.04919.
