Journal of
Petroleum and Gas Engineering

  • Abbreviation: J. Petroleum Gas Eng.
  • Language: English
  • ISSN: 2141-2677
  • DOI: 10.5897/JPGE
  • Start Year: 2010
  • Published Articles: 117

Full Length Research Paper

Prediction of the water saturation around wells with bottom water drive using artificial neural networks

Muhammad Alrumah
  • Muhammad Alrumah
  • Public authority for Applied Education and Training, Kuwait.
  • Google Scholar
Turgay Ertekin
  • Turgay Ertekin
  • Institute for Turgay Ertekin is Pennsylvania State University, USA.
  • Google Scholar

  •  Received: 01 October 2018
  •  Accepted: 28 January 2019
  •  Published: 31 March 2019


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.