1. Al-Anazi A, Gates ID (2010) A support vector machine algorithm to classify lithofacies and model permeability in heterogeneous reservoirs. Eng Geol 114(3–4):267–277

   Article  Google Scholar 

2. Al-Anazi A, Gates ID, Azaiez J (2009) Innovative data-driven permeability prediction in a heterogeneous reservoir. In: SPE EUROPEC/EAGE annual conference and exhibition, Amsterdam, The Netherlands

3. Al-Farhan FA, Ayala LF (2006) Optimization of surface condensate production from natural gases using artificial intelligence. J Petrol Sci Eng 53(1–2):135–147

   Article  Google Scholar 

4. Ali M, Chawathe A (2000) Using artificial intelligence to predict permeability from petrographic data. Comput Geosci 26:915–925

   Article  Google Scholar 

5. Almeida MB, Braga AP, Braga JP (2000) SVM-KM: speeding SVMs learning with a priori cluster selection and k-means. In: Proceedings of the 6th Brazilian symposium on neural networks, pp 162–167

6. Ameri S, Aminian K, Mohaghegh S (1994) Predicting the production performance of gas reservoirs. Sci Iran 1(3):231–240

   Google Scholar 

7. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Haussler D (ed) Proceedings of the 5th annual ACM workshop on computational learning theory, pp 144–152

8. Briones MF, Corpoven SA, Rojas GA, Martinez ER (1994) Application of neural network in the prediction of reservoir hydrocarbon mixture composition from production data. In: SPE annual technical conference and exhibition, New Orleans, Louisiana

9. Chang F, Chang Y (2006) Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv Water Resour 29(1):1–10

   Article  Google Scholar 

10. Chen J, Li Z, Zhao D (2009) Prediction of hydrocarbon reservoir parameter using a GA-RBF neural network. In: Computational intelligence and intelligent systems, 4th international symposium, ISICA 2009, Huangshi, China, pp 379–386

11. Monyei CG, Adewumi AO, Obolo MO (2014) Oil well characterization and artificial gas lift optimization using neural networks combined with genetic algorithm. Discrete Dyn Nat Soc 2014:289239. doi:10.1155/2014/289239

    Article  Google Scholar 

12. Elshafei M, Khoukhi A, Abdulraheem A (2010) Neural network aided design of oil production units. In: 2010 10th International conference on information sciences signal processing and their applications (ISSPA). IEEE, pp 638–641

13. Elsharkawy AM (1998) Modeling the properties of crude oil and gas systems using RBF network. In: SPE Asia Pacific oil and gas conference, Perth, Australia

14. Elsharkawy AM, Foda SG (1998) EOS simulation and GRNN modeling of the constant volume depletion behavior of gas condensate reservoirs. Energy Fuels 12(2):353–364

    Article  Google Scholar 

15. Anifowose FA, Labadin J, Abdulraheem A (2013) Prediction of petroleum reservoir properties using different versions of adaptive neuro-fuzzy inference system hybrid models. Int J Comput Inf Syst Ind Manag Appl 5:413–426

    Google Scholar 

16. Fei S, Liu C, Miao Y (2009) Support vector machine with genetic algorithm for forecasting of key-gas ratios in oil-immersed transformer. Expert Syst Appl 36(3):6326–6331

    Article  Google Scholar 

17. Finol JJ, Guo YK, Jing XDD (2001) Permeability prediction in shaly formations: the fuzzy modeling approach. Geophysics 3(67):817–829

    Google Scholar 

18. Ghouti L, Bukhitan S (2010). Hybrid soft computing for PVT properties prediction. In: European symposium on artificial neural networks, computational intelligence and machine learning (ESANN), Belgium

19. Goda HM, Fattah KA, Shokir EM, Sayyouh MH (2003) Prediction of the PVT data using neural network computing theory. In: 27th Annual SPE international technical conference and exhibition, Abuja, Nigeria

20. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading. ISBN 978-0201157673

    MATH  Google Scholar 

21. Hallman JH, Cook I, Muqeem MA, Jarrett CM, Shammari HA (2007) Fluid customization and equipment optimization enable safe and successful underbalanced drilling of high-H₂S horizontal wells in Saudi Arabia. In: IADC/SPE managed pressure drilling and underbalanced operations conference and exhibition, Galveston, Texas, USA

22. Nooruddin H, Anifowose F, Abdulazeez A (2014) Using soft computing techniques to predict corrected air permeability using Thomeer parameters, air porosity and grain density. Comput Geosci 64:72–80

    Article  Google Scholar 

23. Helmy T, Fatai A (2010) Hybrid computational intelligence models for porosity and permeability prediction of petroleum reservoirs. Int J Comput Intell Appl 9(4):313–337

    Article  Google Scholar 

24. Helmy T, Fatai A, Faisal KA (2010) Hybrid computational models for the characterization of oil and gas reservoirs. Expert Syst Appl 37:5353–5363

    Article  Google Scholar 

25. Hossain MI, Helmy T, Hassan MR, Adbulraheem A, Khoukhi A, Elshafei M (2012) Non-hydrocarbons gas components prediction in multistage separator using neural networks. In: Global conference on power control and optimization PCO, Dubai

26. Hurtado N, Aldana M, Torres J (2009) Comparison between neuro-fuzzy and fractal models for permeability prediction. Comput Geosci 13:181–186

    Article MATH  Google Scholar 

27. Ilkhchi K (2006) A fuzzy logic approach for the estimation of permeability and rock types from conventional well log data: an example from the Kangan reservoir in Iran Offshore Gas Field. Iran J Geophys Eng 3:356–369

    Article  Google Scholar 

28. Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

29. Jaubert JN, Avaullee L, Souvay JF (2002) A crude oil data bank containing more than 5000 PVT and gas injection data. J Petrol Sci Eng 34(1–4):65–107

    Article  Google Scholar 

30. Jupudi RS, Zamansky V, Fletcher TH (2009) Prediction of light gas composition in coal devolatilization. Am Chem Soc Energy Fuels 23:3063–3067

    Article  Google Scholar 

31. Khoukhi A, Oloso M, Abdulraheem A, El-Shafei M, Al-Majed A (2011) Support vector machines and functional networks for viscosity and gas/oil ratio curves prediction. Int J Comput Intell Appl 10(3):269–293

    Article  Google Scholar 

32. Lashin A, El-Din SS (2012) Reservoir parameters determination using artificial neural networks: Ras Fanar field, Gulf of Suez, Egypt. Arab J Geosci 6(8):1866–7511

    Google Scholar 

33. Mahmudi M, Sadeghi MT (2013) The optimization of continuous gas lift process using an integrated compositional model. J Petrol Sci Eng 108:321–327

    Article  Google Scholar 

34. Moghadassi AR, Parvizian F, Hosseini SM, Fazalali AR (2009) A new approach for estimation of PVT properties of pure gases based on artificial neural network model. Braz J Chem Eng 26(1):199–206

    Article  Google Scholar 

35. Mohaghegh S, Balan B, Ameri S, McVey DS (1996) A hybrid neuro-genetic approach to hydraulic fracture treatment design and optimization. In: SPE annual technical conference and exhibition, Denver, Colorado, USA

36. Mohaghegh S, Platon V, Ameri S (1998) Candidate selection for stimulation of gas storage wells using available data with neural networks and genetic algorithms. In: SPE eastern regional meeting, Pittsburgh, Pennsylvania

37. Nagi J, Kiong TS, Ahmed SK, Nagi F (2009) Prediction of PVT properties in crude oil systems using support vector machines. In: Proceedings of ICEE 2009 3rd international conference on energy and environment, Malacca, Malaysia

38. Nikravesh M, Aminzadeh F (2003) Soft computing for intelligent reservoir characterization and modeling. In: Soft Computing and Intelligent Data Analysis, Nikravesh M, Aminzadeh F, Zadeh LA (eds) Developments in petroleum science series, vol 51. Elsevier, Amsterdam, pp 3–32

    Google Scholar 

39. Oloso MA, Khoukhi A, Abdulraheem A, Elshafei M (2009a) A genetic-optimized artificial neural networks for predicting viscosity and gas/oil ratio curves. In: SPE/EAGE reservoir characterization and simulation conference and exhibition, Abu-Dhabi, UAE

40. Oloso MA, Khoukhi A, Abdulraheem A, Elshafei M (2009b) Prediction of crude oil viscosity and gas/oil ratio curves using recent advances to neural networks. In: SPE/EAGE reservoir characterization and simulation conference, Abu-Dhabi, UAE

41. Osman EA, Al-Marhoun MA (2005) Artificial neural networks models for predicting PVT properties of oil field brines. In: 14th SPE middle east oil and gas show and conference, Bahrain

42. Osman EA, Abdel-Wahhab OA, Al-Marhoun MA (2001) Prediction of oil PVT properties using neural networks. In: SPE middle east oil show. Society of Petroleum Engineers, Manama, Bahrain

43. Saemi M, Ahmadi M, Varjani AY (2007) Design of neural networks using genetic algorithm for the permeability estimation of the reservoir. J Petrol Sci Eng 59:97–105

    Article  Google Scholar 

44. Sun Q, Li J, Zhang C, Liu X (2005) High watercut reservoir permeability prediction by flow unit, ANFIS and multi-statistics. Well Logging Technol 29(2):121–124

    Google Scholar 

45. Sundgren H, Winquist F, Locker I, Lundstrom I (1991) Artificial neural networks and gas sensor arrays: quantification of individual components in a gas mixture. Meas Sci Technol 2:464–469

    Article  Google Scholar 

46. Tran QA, Zhang QL, Li X (2003) Reduce the number of support vectors by using clustering techniques. In: International conference on machine learning and cybernetics, vol 2. IEEE, pp 1245–1248

47. Varotsis N, Gaganis V, Nighswander J, Guieze P (1999) A novel non-iterative method for the prediction of the PVT behavior of reservoir fluids. In: SPE annual technical conference and exhibition, Houston, Texas, USA

48. Wang P, Pope G (2001) Proper use of equations of state for compositional reservoir simulation. J Petrol Technol 53(7):74–81

    Article  Google Scholar 

49. Wong PM, Aminzadeh F, Nikravesh M (2002) Intelligent reservoir characterization. In: Wong PM, Aminzadeh F, Nikravesh M (eds) Soft computing for reservoir characterisation and modeling, studies in fuzziness and soft computing. Springer, Berlin, pp 3–12

    Google Scholar 

50. Xiao-mei L, Qin-hua S, Jian-xin L, Wei-fang L (2009) Prediction of fracture porosity of carbonate reservoir with seismic attributes, multi-analysis and ANFIS. Well Logging Technol 33(3):631–684

    Google Scholar 

51. Xie D, Wilkinson D, Yu T, Ramon S (2005) Permeability estimation using a hybrid genetic programming and fuzzy/neural inference approach. In: SPE annual technical conference and exhibition, Dallas, Texas, USA

52. Zhang S, Zhang C, Yang Q (2003) Data preparation for data mining. Appl Artif Intell 17(5–6):375–381

    Article  Google Scholar 

Download references