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References
Al-Bulushi N, King PR, Blunt MJ, Kraaijveld M (2009). Development of artificial neural network models for predicting water saturation and flow distribution. Journal of Petroleum Science and Engineering 68:197-208. |
|
Alimoradi A, Moradzadeh A, Bakhtiari MR (2011). Methods of water saturation estimation: Historical perspective. Journal of Petroleum and Gas Engineering 3(2):45-53. |
|
Baziar S, Shahripour HB, Tadayoni M, Nabi-Bidhendi M (2018). Prediction of water saturation in a tight gas sandstone reservoir by using four intelligent methods: a comparative study. Neural Computing and Applications 30(4):1171-1185. |
|
Blades A, Stright DH (1975). Predicting high volume lift performance in wells coning water. Journal of Canadian Petroleum Technology 14(4):61-70. |
|
Byrne WB, Morse RA (1973). The effects of various reservoir and well parameters on water coning performance. In SPE Symposium on Numerical Simulation of Reservoir Performance, Houston, Texas, USA, 11-12 January. |
|
Gharib H, Elsakka A, Chaw N (2018). Artificial neural network (ann) prediction of porosity and water saturation of shaly sandstone reservoirs. Advances in Applied Science Research 9:26-31. |
|
Gholanlo HH, Amirpour M, Ahmadi S (2016). Estimation of water saturation by using radial based function artificial neural network in carbonate reservoir: A case study in sarvak formation. Petroleu, 2(2):166-170. |
|
Hamada GM, Al-Gathe AA, Al-Khudafi AM (2015). Hybrid artificial intelligent approach for determination of water sat- uration using archie's formula in carbon- ate reservoirs. Journal of Petroleum and Environmental Biotechnology 6(6). |
|
Helle HB, Bhatt A (2002). Fluid saturation from well logs using committee neural networks. Petroleum Geoscience 8:109-118. |
|
Kuo MC (1983). A simplified method for water coning predictions. In SPE Annual Technical Conference and Exhibition, San Francisco, California, USA, 5-8 October. |
|
Mahmoudi S, Mahmoudi A (2014). Water saturation and porosity prediction using back-propagation artificial neural network (bpann) from well log data. Journal of Engineering and Technology 5(2):1-8. |
|
Mungan N (1975). A theoretical and experimental coning study. Society of Petroleum Engineers Journal 15(3):247-254. |
|
Muskat M, Wyckoff HD (1935). An approximate theory of water coning in oil production. Transactions of the AIME 114(1):144-163. |
|
Shokir EME-M (2004). Prediction of the hydrocarbon saturation in low resis- tivity formation via artificial neural net- work. In SPE Asia Pacific Conference on Integrated Modelling for Asset Management, Kuala Lumpur, Malaysia, 29-30 March. |
|
Van T (1994). Water coning in a frac- tured reservoir. In SPE Annual Technical Conference and Exhibition, New Orleans, Louisiana, USA, 25-28 September. |
|
Yang W, Wattenbarger RA (1991). Water coning calculations for vertical and horizontal wells. In SPE Annual Technical Conference and Exhibition, Dallas, Texas, USA, 6-9 October. |
|
Zendehboudi S, Elkamel A, Chatzis I, Ahmadi MA, Bahadori A, Lohi A (2014). Estimation of breakthrough time for water coning in fractured systems: Experimental study and connectionist modeling. AIChE Journal 60(5):1905-1919. |
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