KaydedilenlerDerginizi ekleyin

Dergiler / Sigma Journal of Engineering and Natural Sciences / 2020 / Cilt: 38 Sayı: 1

A new time-dependent model for controlling the gas injection pressure in continuous gas lift

Mohammad Reza MAHDIANI
Ehsan KHAMEHCHI
Amir Abolfazl SURATGAR
Sayfa
265–279
Yayın Tarihi
2020-03-27
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

In addition as by accurate estimation of pressure at the injection point, flow instability can be predicted and prevented, this model can be of great help to prohibit the problem of flow instability.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:There are a number of models for gas lift in the literature, but most of them suffer from large scale simplifications and, hence, unacceptable errors.
📊
Temel Bulgular & Veri:Results show that the new model yields accurate results and hence serves as an excellent tool for control problems.
💡
Sonuç & Katkı:In addition as by accurate estimation of pressure at the injection point, flow instability can be predicted and prevented, this model can be of great help to prohibit the problem of flow instability.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Gas lift · real gas · lag time

Abstract

There are a number of models for gas lift in the literature, but most of them suffer from large scale simplifications and, hence, unacceptable errors. Those simplifications include ignoring the temperature profile, assuming ideal gas, and neglecting the time- delay between applications of the gas injection pressure on the surface and sensing its effect at point of injection. In this study, all mentioned deficiencies are overcome and a new model is developed for predicting the pressure profile in the annulus and at the injection point. Then using sensitivity analysis, the applicability and accuracy of this model are discussed and compared with earlier models. Results show that the new model yields accurate results and hence serves as an excellent tool for control problems. In addition as by accurate estimation of pressure at the injection point, flow instability can be predicted and prevented, this model can be of great help to prohibit the problem of flow instability.

🔖

Bu Makaleye Atıf Yap

MAHDIANI, M. R., KHAMEHCHI, E., SURATGAR, A. A. (2020). A new time-dependent model for controlling the gas injection pressure in continuous gas lift. Sigma Journal of Engineering and Natural Sciences, Cilt: 38 Sayı: 1, 265–279. https://ajindex.com/articles/897565

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (15)

  1. G. Linga and H. Lund, “A two-fluid model for vertical flow applied to CO2 injection wells,” Int. J. Greenh. Gas Control, vol. 51, pp. 71–80, 2015
  2. M. R. Mahdiani and E. Khamehchi, “Preventing Instability Phenomenon in Gas-lift Optimization,” Iran. J. Oil Gas Sci. Technol., vol. 4, no. 1, pp. 49–65, Jan. 2015
  3. M. R. Mehregan, A. Mohaghar, and A. Esmaeili, “Developing a mathematical model for optimizing oil production Using Gas-Lift Technology,” Indian J. Sci. Technol., vol. 9, no. 5, Feb. 2016
  4. A. Ebrahimi and E. Khamehchi, “Developing a novel workflow for natural gas lift optimization using advanced support vector machine,” J. Nat. Gas Sci. Eng., vol. 28, pp. 626–638, Jan. 2016
  5. C. Merlini Giuliani and E. Camponogara, “Derivative-free methods applied to daily production optimization of gas-lifted oil fields,” 2015
  6. E. Khamehchi and M. R. Mahdiani, “An Introduction to Gas Lift,” in Gas Allocation Optimization Methods in Artificial Gas Lift, Springer International Publishing, 2017, pp. 1–5
  7. N. Chithra Chakra, K. Y. Song, M. M. Gupta, and D. N. Saraf, “An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs),” J. Pet. Sci. Eng., vol. 26, no. 3–4, pp. 18–33, 2013
  8. C. A. Larsen and H. A. Asheim, “Experimental Investigation of Gas Lift Instability and Dynamic Regulation to Control It (Russian),” in SPE Annual Caspian Technical Conference and Exhibition, 2014
  9. A. Plucenio, G. A. Mafra, and D. J. Pagano, “A CONTROL STRATEGY FOR AN OIL WELL OPERATING VIA GAS LIFT,” IFAC Proc. Vol., vol. 39, no. 2, pp. 1081–1086, 2006
  10. S. P. Boyd, “Real-time Embedded Convex Optimization,” IFAC Proc. Vol., vol. 42, no. 11, p. 9, 2009
  11. M. Campos, H. Teixeira, F. Liporace, and M. Gomes, “Challenges and problems with advanced control and optimization technologies,” IFAC Proc. Vol., vol. 42, no. 11, pp. 1–8, 2009
  12. E. Khamehchi and M. R. Mahdiani, “Gas Allocation Optimization Methods in Artificial Gas Lift.” Springer, 2016
  13. A. Plucenio, D. J. Pagano, E. Camponogara, A. Traple, and A. Teixeira, “Gas-lift Optimization and Control with Nonlinear MPC,” IFAC Proc. Vol., vol. 42, no. 11, pp. 904–909, 2009
  14. S. M. Elgsæter, O. Slupphaug, and T. A. Johansen, “A structured approach to optimizing offshore oil and gas production with uncertain models,” Comput. Chem. Eng., vol. 34, no. 2, pp. 163–176, 2010
  15. E. Khamehchi and M. R. Mahdiani, “The Fitness Function