Development of Monitoring and Forecasting Technology Energy Efficiency of Well Drilling Using Mechanical Specific Energy

Andrey Kunshin (), Mikhail Dvoynikov, Eduard Timashev and Vitaly Starikov
Additional contact information
Andrey Kunshin: Department of Wells Drilling, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia
Mikhail Dvoynikov: Department of Wells Drilling, Saint Petersburg Mining University, 199106 Saint Petersburg, Russia
Eduard Timashev: Department of Technical Regulation and Development of Corporative Science and Project Complex, PJSC NK Rosneft, 117997 Moscow, Russia
Vitaly Starikov: Department of Energy Geoscience Infrastructure and Society, Heriot-Watt University, Dubai Knowledge Park, Dubai P.O. Box 38103, United Arab Emirates

Energies, 2022, vol. 15, issue 19, 1-23

Abstract: This article is devoted to the development of technology for improving the efficiency of directional well drilling by predicting and adjusting the system of static and dynamic components of the actual weight on the bit, based on the real-time data interpretation from telemetry sensors of the bottom hole assembly (BHA). Studies of the petrophysical and geomechanical properties of rock samples were carried out. Based on fourth strength theory and the Palmgren–Miner fatigue stress theory, the mathematical model for prediction of effective distribution of mechanical specific energy, using machine learning methods while drilling, was developed. An algorithm was set for evaluation and estimation of effective destruction of rock by comparing petrophysical data in the well section and predicting the shock impulse of the bit. Based on the theory provided, it is assumed that the given shock impulse is an actual representation of an excessive energy, conveyed to BHA. This excessive energy was quantitively determined and expressed as an adjusting coefficient for optimal weight on bit. The developed mathematical and predictive model helps to identify the presence of ineffective rock destruction and adjust drilling regime accordingly. Several well drilling datasets from the North Sea were analyzed. The effectiveness of the developed mathematical model and algorithms was confirmed by testing well drilling data.

Keywords: well; optimization; control; operating parameters; drill string dynamics; weight on the bit; bit vibrations and shocks; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/19/7408/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/19/7408/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:19:p:7408-:d:937238

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().