Analysis of interrelations between the criteria of optimal control over the process of drilling the wells

Authors

DOI:

https://doi.org/10.15587/1729-4061.2017.97934

Keywords:

optimal control, drilling process, optimality criteria, interrelations, the Farrar-Glauber method

Abstract

We analyzed one of the promising directions of optimal control over the process of drilling wells – the realization of automated control in real time by the criterion "minimum specific energy consumption".

A comprehensive assessment was conducted of the relations between the two optimality criteria of the drilling process – the minimum cost per meter of drilling and specific energy consumption. We used the Farrar-Glauber method for the analysis

This is predetermined by the fact that the models that are employed for computing the cost per meter of drilling include the duration of drilling with one bit and footage per bit. However, they can be defined only upon completion of the bit run, which lasts for several tens of hours. This prevents applying the criterion "minimum cost per meter of drilling" to solve the problems of control in real time. In contrast to that criterion, specific energy consumption can be controlled continuously in the course of drilling a well.

With the help of the Farrar-Glauber method, we established that there is complete multicollinearity between the criteria "minimum cost per meter of drilling" and "minimum specific energy consumption" at the change of axial force on the bit and the frequency of its rotation. The degree of completeness in the multicollinearity among the examined criteria is found:

– at the change in axial force to a bit F: det t=0,305;   (16.003>3.8); F>Ftable (31.808>4.60); t12>ttable (5.639>2.145).

– at the change in rotation frequency ω: det t=0,114;   (30.011>3.8); F>Ftable (94.913>4.49); t12>ttable (9.742>1.746).

At the change in the consumption of a washing fluid, under conditions of the experiment, the multicollinearity between the investigated criteria of optimal control is missing: Q: det t=0.84;   (2.35<3.8); F>Ftable (2.662<4.60); t12>ttable (1.631<2.145).

The obtained results are important and useful for the application of a dualistic approach to solving the problem of optimal control over the process of drilling in real time and the formation of optimality criterion based on the principles of energy-informational approach. This makes it possible to directly process information on the specific energy consumption and to provide intelligent support for the decision-making processes when a drilling master defines optimal drilling mode parameters. 

Author Biographies

Lev Kopystynskyy, Ivano-Frankivsk National Technical University of Oil and Gas Karpatska str., 15, Ivano-Frankivsk, Ukraine, 76019

Postgraduate student

Department of automation computer-integrated technologies

Vitaliai Kropyvnytska, Ivano-Frankivsk National Technical University of Oil and Gas Karpatska str., 15, Ivano-Frankivsk, Ukraine, 76019

PhD, Associate Professor

Department of computer systems and networks

Andriy Lagoyda, Ivano-Frankivsk National Technical University of Oil and Gas Karpatska str., 15, Ivano-Frankivsk, Ukraine, 76019

Assistant

Department of automation computer-integrated technologies

George Sementsov, Ivano-Frankivsk National Technical University of Oil and Gas Karpatska str., 15, Ivano-Frankivsk, Ukraine, 76019

Doctor of Technical Sciences, Professor

Department of automation computer-integrated technologies

References

  1. Hutak, O. V. (2010). Analiz multykolinearnosti kryteriiv optymalnosti protsesu burinnia naftovykh i hazovykh sverdlovyn dolotamy typu RDS. Naftohazova enerhetyka, 1 (12), 98–101.
  2. Elmgerbi, A., Thonhauser, G., Prohaska, M., Nascimento, A., Roohi, A. (2016). Application of Computer Programming to Estimate Volumetric Change of an Active Drilling Fluid System Cause by Elastic Deformation of an Open Borehole Section Wall. Global Journal of Computer Science and Technology, 16 (3), 15–30.
  3. Jacobs, T. (2015). Automated Drilling Technologies Showing Promise. Journal of Petroleum Technology, 67 (06), 50–55. doi: 10.2118/0615-0050-jpt
  4. Aldred, W., Bourque, J., Mannering, M., Chapman, C., du Castel, B., Hansen, R. et. al. (2012). Drilling Automation. Oilfield Review, 24 (2), 18–27.
  5. Rassenfoss, S. (2011). Automated Drilling Raises Control Issues. Journal of Petroleum Technology, 63 (09), 36–37. doi: 10.2118/0911-0036-jpt
  6. Larsen, H. F., Alfsen, T., Kvalsund, R., Iversen, F. P., Welmer, M., Hult, O., Ekrene, S. (2010). The Automated Drilling Pilot on Statfjord C. IADC/SPE Drilling Conference and Exhibition. doi: 10.2118/128234-ms
  7. Thorogood, J. L., Florence, F., Iversen, F. P., Aldred, W. D. (2009). Drilling Automation: Technologies, Terminology and Parallels With Other Industries. SPE/IADC Drilling Conference and Exhibition. doi: 10.2118/119884-ms
  8. Detournay, E., Richard, T., Shepherd, M. (2008). Drilling response of drag bits: Theory and experiment. International Journal of Rock Mechanics and Mining Sciences, 45 (8), 1347–1360. doi: 10.1016/j.ijrmms.2008.01.010
  9. Fernandez, M., Ibanez, D., Storey, D. (2005). Significant Results in Field Trials (Argentina) of an Electronically Controlled Automatic Drilling System. Proceedings of SPE Latin American and Caribbean Petroleum Engineering Conference. doi: 10.2523/94889-ms
  10. Aldred, W., Belaskie, J., Isangulov, R., Crockett, B., Edmondson, B., Florence, F., Srinivasan, S. (2005). Changing the Way We Drill. Olifield Review, 17 (1), 42–49.
  11. Thorhauser, G. (2004). Using Real-Time Data for Automated Drilling. Oil Gas European Magazine, 4, 170–173.
  12. Horbiichuk, M. I., Kropyvnytska, V. B. (2008). Optymalne keruvannia protsesom mekhanichnoho burinnia. Naftova i hazova promyslovist, 3, 20–22.
  13. Fadieieva, I. H. (2003). Fazzi-model sposterezhennia za sobivartistiu budivnytstva naftovykh i hazovykh sverdlovyn. Kompiuterne modeliuvannia ta informatsiini tekhnolohii v nautsi, ekonomitsi ta osviti, 190–194.
  14. Khakimov, L. Z., Dverii, V. P. (2003). Optymalni vytraty promyvnoi ridyny dlia burinnia sverdlovyn dolotamy diametrom 215,9. Naftova i hazova promyslovist, 4, 24–25.
  15. Sementsov, H. N., Sabat, N. V., Hutak, O. V. (2014). Razvytye metodov syhnalnoi ydentyfykatsyy burymosty hornykh porod v realnom vremeny. Avtomatyzatsyia, telemekhanyzatsyia y sviaz v neftianoi promyshlennosty, 2, 31–36.
  16. Danyliuk, M. O., Fadieieva, I. H. (2001). Rozvytok modelei upravlinnia protsesom burinnia hlybokykh sverdlovyn na bazi nechitkoi lohiky. Enerhetyka: ekonomika, tekhnolohii, ekolohiia, 1, 61–65.
  17. Zhukovskyi, A. A. (1985). Metody statycheskoi optymyzatsyy protsessa burenyia. Hornyi zhurnal. Yzvestyia vuzov, 8, 96–101.
  18. Multykolinearnist. Alhorytm Farrara-Hlobera. Wikipedia. Available at: http://uk.wikipedia.org/wiki/Мультиколінеарність
  19. Haber, R., Unbehauen, H. (1990). Structure identification of nonlinear dynamic systems – A survey on input/output approaches. Automatica, 26 (4), 651–677. doi: 10.1016/0005-1098(90)90044-i
  20. McCode, S., Davies, P., Seidel, D. (1991). On the use of nonlinear autoregressive moving average models for simulation end system identification. American Coutrol Conference, 2559–2562.
  21. Pottmann, M., Unbehauen, H., Seborg, D. E. (1993). Application of a general multi-model approach for identification of highly nonlinear processes-a case study. International Journal of Control, 57 (1), 97–120. doi: 10.1080/00207179308934380

Downloads

Published

2017-04-29

How to Cite

Kopystynskyy, L., Kropyvnytska, V., Lagoyda, A., & Sementsov, G. (2017). Analysis of interrelations between the criteria of optimal control over the process of drilling the wells. Eastern-European Journal of Enterprise Technologies, 2(3 (86), 40–50. https://doi.org/10.15587/1729-4061.2017.97934

Issue

Section

Control processes