Fig. 1. Possible pressure losses in completed producing system.
Fig. 2. Determination of system flow capacity using nodal analysis.
Fig. 3. Schematic of gas lift well system
Fig. 4. Graph to determine the optimum gas rate in gas-lift well[10].
Fig. 5. Structure of basic neural network.
Fig. 6. Structure of an ANN using error back-propagation algorithm.
Fig. 7. Nodal analysis for selecting water cut range.
Fig. 8. Nodal analysis for selecting choke size range.
Fig. 9. Structure input-output data of ANN model in choke size seleciton.
Fig. 10. Correlation of coefficient for choke size ANN model.
Fig. 11. Structure input-output data of ANN model in gas injection rate selection.
Fig. 12. Nodal analysis for selecting gas injection rate range.
Fig. 13. Correlation of coefficient for gas lift ANN model.
Table 1. Statistical range of parameter for input data(choke size ANN model).
Table 2. Statistical range of parameter for output data(choke size ANN model).
Table 3. Statistical accuracy of choke size ANN model.
Table 4. Validity verification of choke size ANN model.
Table 5. Statistical range of parameter for input data(gas lift ANN model).
Table 6. Statistical range of parameter for output data(gas lift ANN model).
Table 7. Statistical accuracy of gas lift ANN model.
Table 8. Validity verification of choke size ANN model.
참고문헌
- Cramer, R., Gobel, D., Mueller, K., and Tulalian, R., "A measure of the digital oil fields status - Is it the end of the beginning", SPE Intelligent Energy International, Utrecht, The Netherlands, March 27-29. SPE 149957, (2012).
- Lim, J., Park, H. and Lim, J., "Classification and application of digital oil field System", J. Korean Soc. Miner. Energy Resour. Eng, Vol. 51, No. 5, 750-756, (2014). https://doi.org/10.12972/ksmer.2014.51.5.750
- Al-Jasmi, A., Goel, H.K., Nasr, H., Carvajal, G.A., Johnson, D.W., Cullick, A.S., Rodriguez, J.A., Moricca, G., Velasquez, G., Villamizar, M. and Querales, M., "A surveillance 'Smart Flow' for intelligent digital production operations", SPE Digital Energy Conference and Exhibition, Woodlands, Texas, USA, March 5-7. SPE 163697, (2013).
- Guo, B., Lyons, W.C., and Ghalambor, A., "Petroleum Production Engineering A Computer-Assisted Approach, Gulf Professional Publishing", Oxford, U.K., 182, (2007).
- 김의중., 알고리즘으로 배우는 인공지능, 머신러닝, 딥러닝 입문, 위키북스, (2016).
- Mohammed, A. Al-Khalifa. and Muhammad, A. and Al-Marhoun., "Application of neural network for two-phase flow through chokes", SPE Saudi Arabia section Annual Technical Symposium and Exhibition, Khobar, Saudi Arabia, May 19-22, SPE 169597, (2013).
- Khamehchi, A., Rashidi, F. and Rasouli, H., "Prediction of Gas Lift Parameters Using Artificial Neural Networks", Enhanced Oil Recovery-Iranian Chemical Engineering Journal (Special Issue), Vol. 8, No. 43, (2009).
- Ranjan, A., Verma, S. and Singh, Y., "Gas Lift Opimization using Artificial Neural Network", SPE Middle East Oil & Gas Show and Conference, Manama, Bahrain, March 8-11, SPE 172610, (2015).
- Beggs, H.D., "Production Optimization Using Nodal Analysis", 2th Ed., Vol. 1, OGCI and Petroskills Publications, Tulsa, USA, (2008).
- Robert, N.H. and Potter, K., "Optimization of Gas-Injected Oil Wells", SAS Gloval Forum, (2011).
- Sim, S.S.K., "Pressure-Volume-Temperature Correlations for Crud Oils from the Illinois Basin", Illinois State Geological Survey, 140, (1993).
피인용 문헌
- 시추효율매개변수를 이용한 굴진율 예측 지도학습 모델 비교 연구 vol.25, pp.8, 2021, https://doi.org/10.6109/jkiice.2021.25.8.1032