Application of artificial neural network for predicting production flow rates of gaslift oil wells
1 Hanoi University of Mining and Geology, Hanoi, Vietnam
2 Petrovietnam Domestic Exploration Production Operating Company Limited, Ho Chi Minh City, Vietnam
- Keywords: ANN, Enhence oil recovery, Gas lift, Oil production flow rate.
- Received: 28th-Jan-2022
- Revised: 4th-May-2022
- Accepted: 4th-June-2022
- Online: 30th-June-2022
- Section: Civil Engineering
In petroleum industry, the prediction of oil production flow rate plays an important role in tracking the good performance as well as maintaining production flow rate. In addition, a flow rate modelling with high accuracy will be useful in optimizing production properties to achieve the expected flow rate, enhance oil recovery factor and ensure economic efficiency. However, the oil production flow rate is traditionally predicted by theoretical or empirical models. The theoretical model usually gives predicted results with a wide variation of error, this model also requires a lot of input data that might be time-consuming and costly. The empirical models are often limited by the volume of data set used to construct the model, therefore predicted values from the applications of these models in practical condition are not highly accurate. In this research, the authors propose the use of an artificial neural network (ANN) to establish a better relationship between production properties and oil production flow rate and predict oil production flow rate. Using production data of 5 wells which use continuous gas lift method in X oil field, Vietnam, an ANN system was developed by using back-propagation algorithm and tansig function to predict production flow rate from the above data set. This ANN system is called a back-propagation neural network (BPNN). In comparison with the oil production flow rate data collected from these studied continuous gas lift oil wells, the predicted results from the constructed ANN achieved a very high correlation coefficient (98%) and low root mean square error (33.41 bbl/d). Therefore, the developed ANN models can serve as a practical and robust tool for oilfield prediction of production flow rate.
Achong I., (1961). Revised Bean Performance Formula for Lake Maracaibo Wells. Internal Report, Shell Oil Co. Houston, TX.
Elgibaly, A.A., Ghareeb, M., Kamel, S. and El-Bassiouny, M.E.S., (2021). Prediction of gas-lift performance using neural network analysis. AIMS Energy, 9(2), pp.355-379.
Al-Attar, H.H. and Abdul-Majeed, G.H., (1988). Revised bean performance equation for East Baghdad oil wells. SPE Production Engineering, 3(01), pp.127-131.
Al-Towailib, A.I., Al-Marhoun, M.A., (1994). A new correlation for two-phase flow through chokes. Journal of Canadian Petroleum Technology, 33(05). https://doi.org/10.2118/94-05-03
Ashford, F.E., (1974). An evaluation of critical multiphase flow performance through wellhead chokes. Journal of Petroleum Technology, 26(08), pp.843-850.
Azim, R.A., (2020). Prediction of multiphase flow rate for arti cially owing wells using rigorous arti cial neural network technique. Flow Measurement and Instrumentation, 76, p.101835.
Barjouei, H.S., Ghorbani, H., Mohamadian, N., Wood, D.A., Davoodi, S., Moghadasi, J. and Saberi, H., (2021). Prediction performance advantages of deep machine learning algorithms for two-phase flow rates through wellhead chokes. Journal of Petroleum Exploration and Production, 11(3), pp.1233-1261.
Baxendell, P.B., (1958). Producing wells on casing flow-an analysis of flowing pressure gradients. Transactions of the AIME, 213(01), pp.202-206.
Gilbert W.E., (1954). Flowing and gas-lift well performance. In Drilling and Production practice, American Petroleum Institute. API-801-30H.
George, A., (2021). Predicting Oil Production Flow Rate Using Artificial Neural Networks-The Volve Field Case. In SPE Nigeria Annual International Conference and Exhibition. OnePetro. Lagos, Nigeria. https://doi.org/10.2118/208258-MS
Gorjaei, R.G., Songolzadeh, R., Torkaman, M., Safari, M. and Zargar, G., (2015). A novel PSO-LSSVM model for predicting liquid rate of two phase flow through wellhead chokes. Journal of Natural Gas Science and Engineering, 24, pp.228-237.
Ghorbani, H., Wood, D.A., Moghadasi, J., Choubineh, A., Abdizadeh, P. and Mohamadian, N., (2019). Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study. Journal of Petroleum Exploration and Production Technology, 9(2), pp.1355-1373. https://doi.org/10.1007/s13202-018-0532-6.
Khamehchi, E., Rashidi, F. and Rasouli, H., (2009). Prediction of gas lift parameters using artificial neural network. Enhanced Oil Recovery–Iranian Chemical Engineering Journal (Special Issue), 8(43).
Khan, M.R., Tariq, Z. and Abdulraheem, A., (2020). Application of artificial intelligence to estimate oil flow rate in gas-lift wells. Natural Resources Research, 29(6), pp.4017-4029. doi:10.1007/s11053-020-09675-7
Mirzaei-Paiaman, A. and Salavati, S., (2013). A new empirical correlation for sonic simultaneous flow of oil and gas through wellhead chokes for Persian oil fields. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 35(9), pp.817-825.
Mohaghegh, S., 2000. Virtual-intelligence applications in petroleum engineering: Part 1 - Artificial neural networks. Journal of Petroleum Technology, 52(09), pp.64-73.
Poettmann, F.H. and Beck, R.L., (1963). New charts developed to predict gas-liquid flow through chokes. World Oil, 184(3), pp.95-100.
Rashid, S., Ghamartale, A., Abbasi, J., Darvish, H. and Tatar, A., (2019). Prediction of critical multiphase flow through chokes by using a rigorous artificial neural network method. Flow Measurement and Instrumentation, 69, p.101579.
Ros N.C.J., (1960). An analysis of critical simultaneous gas/liquid flow through a restriction and its application to owmetering. Applied Scientific Research, 9 (1), 374 pages. http://doi.org/10.1016/j.flowmeasinst.2020.101835
Tangren, R.F., Dodge, C.H. and Seifert, H.S., (1949). Compressibility effects in two‐phase flow. Journal of Applied physics, 20(7), pp.637-645.
Tripathy, S.S., Saxena, R.K. and Gupta, P.K., (2013). Comparison of statistical methods for outlier detection in proficiency testing data on analysis of lead in aqueous solution. American Journal of Theoretical and Applied Statistics, 2(6), pp.233-242.