Advertisement

Hybrid clustering-estimation for characterization of thin bed heterogeneous reservoirs

  • Behzad Tokhmechi
  • Vamegh Rasouli
  • Haleh Azizi
  • Minou Rabiei
Original Article
  • 21 Downloads

Abstract

Modeling heterogeneous reservoirs is cumbersome as it requires a great effort to determine the variation of properties with respect to direction, while the lack of adequate data makes this a difficult task. Generating static models for heterogeneous reservoirs remains an important challenge in petroleum engineering applications which requires more investigations. Some heterogeneous reservoirs, such as thin bed reservoirs, may be divided into some homogeneous subzones where characterization of these homogeneous sub-reservoirs and their integration can represent the properties of the heterogeneous reservoir. To investigate this concept, in this paper, three exemplar reservoirs (ER) were generated. The heterogeneity in the data is increased from ER1 to ER2 and ER3. In the first step, each reservoir was studied as one single zone, so the results can be compared with the proposed method in this work. Ordinary kriging (OK) and multilayer perceptron neural network (MLP) were used for modeling of these exemplar reservoirs. This study showed that OK cannot model reservoir characteristics, whereas the MLP yielded reasonably acceptable results. In the next step, a hybrid clustering classification-based method was applied to divide the reservoir to homogeneous subzones. Each reservoir was modeled in terms of its homogeneous subzones. The homogeneous subzones were modeled using OK and MLP. The results showed that the developed model was successful in modeling the heterogeneity at a reasonable CPU processing time. Also, it was seen that in case of using the simple modeling techniques, MLP neural network yields more reasonable results, compared with OK.

Keywords

Clustering Classification Ordinary kriging Multilayer perceptron neural network CPU processing time Homogeneous subzones 

References

  1. Al-Zainaldin S, Glover PWJ, Lorinczi P (2017) Synthetic fractal modelling of heterogeneous and anisotropic reservoirs for use in simulation studies: implications on their hydrocarbon recovery prediction. Transp Porous Media 116(1):181–212CrossRefGoogle Scholar
  2. Alexandridis AK, Zapranis AD (2013) Wavelet neural networks: a practical guide. Neural Netw 42:1–27CrossRefGoogle Scholar
  3. Alexandridis A, Livanis E (2008) Forecasting crude oil prices using wavelet neural networks. In: 5th Student conference of management science and technology, Athens, GreeceGoogle Scholar
  4. Almeida JA (1999) Use of geostatistical models to improve reservoir description and flow simulation in heterogeneous oil fields. Master thesis, University Technica De LisboaGoogle Scholar
  5. Armstrong M (ed) (1998) Basic linear geostatistics. Springer, BerlinGoogle Scholar
  6. Avci E (2007) An expert system based on wavelet neural network-adaptive 115 normentropy for scale invariant texture classification. Expert Syst Appl 32:919–926CrossRefGoogle Scholar
  7. Bakshi V, Stephanopoulos R, Bhavik G (1992) Wavelets as basic functions for localized learning in multi-resolution hierarchy. IEEE 2:140–145Google Scholar
  8. Bishop CM (1997) Neural networks for pattern recognition. Oxford University Press, OxfordGoogle Scholar
  9. Boughrara H, Chtourou M, Amar CB, Chen L (2014) MLP neural network using modified constructive training algorithm: application to face recognition. In: image processing, application and system conference (IPAS), Sousse, TunisiaGoogle Scholar
  10. Cao L, Hong Y, Fang H, He G (1995) Predicting chaotic time series with wavelet networks. Phys D 85:225–238CrossRefGoogle Scholar
  11. Cao J, Yang J, Wang Y, Wang D, Shi Y (2015) Extreme learning machine for reservoir parameter estimation in heterogeneous sandstone reservoir. Math Probl Eng 2015.  https://doi.org/10.1155/2015/287816 Google Scholar
  12. Chen Y, Yang B, Dong J (2006) Time-series prediction using a local linear wavelet neural network. Neurocomputing 69:449–465CrossRefGoogle Scholar
  13. Cheng JL (2006) Wavelet neural networks with a hybrid learning approach. J Inf Sci Eng 22:1367–1387Google Scholar
  14. Cui Q, Wang X, Li C, Cai Y, Liang P (2016) Improved Thomas–Fiering and wavelet neural network models for cumulative errors reduction in reservoir inflow forecast. J Hydro Environ Res 13:134–143CrossRefGoogle Scholar
  15. Daubechies I (1990a) The wavelet transform, time frequency localization and signal analysis. IEEE Trans Inf Theory 39:961–1005CrossRefGoogle Scholar
  16. Daubechies I (1990b) Ten lectures on wavelets. SIAMGoogle Scholar
  17. Deutsch CV (2006) What in the reservoir is geostatistics good for? J Can Pet Technol 45:201–225CrossRefGoogle Scholar
  18. Duda RO, Hart PE, Stork DG (2003) Pattern classification, 2nd edn. Wiley, New YorkGoogle Scholar
  19. Fang Y, Chow TWS (2006) Wavelets based neural network for function approximation. Lect Notes Comput Sci 3971:80–85CrossRefGoogle Scholar
  20. Fengqi H, Lijuan S (2015) Wavelet neural network in the design and application of hydrological forecast. In: international conference on intelligent transportation, big data and smart city, Halong Bay, VietnamGoogle Scholar
  21. Fletcher R (1980) Practical methods of optimization, vol 1. Unconstrained optimization, Wiley, New YorkGoogle Scholar
  22. Hamada GM, Elshafei MA (2009) Neural network prediction of porosity and permeability of heterogeneous gas sand reservoirs. In: SPE Saudi Arabia section technical symposium, Al-Khobar, Saudi Arabia, SPE-126042-MSGoogle Scholar
  23. Hewett TA (1993) Modelling reservoir heterogeneity with fractals. Quant Geol Geostat 5:455–466CrossRefGoogle Scholar
  24. Hewett TA (1986) Fractal distribution of reservoir heterogeneity and their influence of fluid transport. In: 61st Ann. tech. conf. New Orleans, Louisiana, SPE 15385Google Scholar
  25. Hu LY, Le Ravalec-Dupin M (2004) Elements for an integrated geostatistical modeling of heterogeneous reservoirs. Oil Gas Sci Technol Rev IFP 59(2):141–155CrossRefGoogle Scholar
  26. Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New YorkGoogle Scholar
  27. Jin J, Kim J (2015) Forecasting natural gas prices using wavelets, time series, and artificial neural networks. PLoS One 10(11):1–23CrossRefGoogle Scholar
  28. Karimi A, Moeini F, Shamsoddini-Moghadam MJ, Hosseini SA, Mohammadi AH, Hemmati-Sarpardeh A (2016) Modeling the permeability of heterogeneous oil reservoirs using a robust method. Geosci J 20:259–271CrossRefGoogle Scholar
  29. Li-hong L, Xu XY, Liu YF, Xiao-li L (2010) An uncertainty oriented grade estimation method based on fuzzy wavelet neural network. In: 2nd International workshop on intelligent systems and applications, Wuhan, ChinaGoogle Scholar
  30. Masoudi P, Aifa T, Memarian H, Tokhmechi B (2018) Uncertainty assessment of porosity and permeability by clustering algorithm and fuzzy arithmetic. J Petrol Sci Eng 161:275–290CrossRefGoogle Scholar
  31. Masoudi P, Memarian H, Aifa T, Tokhmechi B (2017) Geometric modelling of volume of investigation of well-logs for thin-bed characterization. J Geophys Eng 14:426–444CrossRefGoogle Scholar
  32. Michael HA, Li H, Boucher A, Sun T, Caers J, Gorelick SM (2010) Combining geologic-process models and geostatistics for conditional simulation of 3-D subsurface heterogeneity. Water Resour Res 46:1–20CrossRefGoogle Scholar
  33. Nasiri J (2013) TOC estimation from logs using wavelet neural network, case study: Kockatea shale, Perth sedimentary basin, Western Australia. MSc thesis, Shahrood University of TechnologyGoogle Scholar
  34. Nasiri J, Tokhmechi B, Rezaee MR (2012) TOC estimation using wavelet neural network from well log data. In: The first international conference of oil, gas, petrochemical and power plant, Tehran, IranGoogle Scholar
  35. Okkan U (2012) Wavelet neural network model for reservoir inflow prediction. Sci Iran A 19(6):1445–1455CrossRefGoogle Scholar
  36. Rasouli V, Tokhmechi B (2010) Difficulties in using geostatistical models in reservoir simulation. In: SPE 126191, EgyptGoogle Scholar
  37. Shiri Y, Tokhmechi B, Zarei Z, Koneshloo M (2012) Self-affine and ARX-models zonation of well logging data. Phys A Stat Mech Appl 391(21):5208–5214CrossRefGoogle Scholar
  38. Souche L, Mahdavi R, Mohammad NM, Alim S, Masoudi R, Basa D (2015) Reservoir modeling of complex thin-bedded deep water deposits integrating well data, seismic inversion and depositional model from Offshore Sabah, Malaysia. In: International petroleum technology conference, Doha, QatarGoogle Scholar
  39. Theodoridis S, Koutroumbos K (2002) Pattern classification, 2nd edn. Elsevier/Academic, San DiegoGoogle Scholar
  40. Tyagi AK, Bastia R, Das M (2008) Identification and evaluation of the thin bedded reservoir potential in the east coast deep water basins of India. In: 7th International conference & exposition on petroleum geophysics, HyderabadGoogle Scholar
  41. Tyagi AK, Guha R, Voleti D, Sxena K (2009) Challenges in the reservoir characterization of a laminated sand shale sequence. In: 2nd SPWLA-India symposium, IndiaGoogle Scholar
  42. Vahedi R, Tokhmechi B, Koneshloo M (2016) Permeability up-scaling in fractured reservoirs, using different optimized mother wavelets at each level. J Min Environ 7(2):239–250Google Scholar
  43. Veitch D (2005) Wavelet neural networks data analysis, networks and nonlinear dynamics. MSc thesis, University of YorkGoogle Scholar
  44. Xiao-li L, Yu-ling X, Li-hong L, Qin-jin G (2009) A nonlinear grade estimation method based on wavelet neural network. In: 4th International conference on bio-inspired computing, Beijing, ChinaGoogle Scholar
  45. Yu L, Pang Y, Wei X, Xu S (2011) Forecasting crude oil spot price by wavelet neural networks using OECD petroleum inventory levels. New Math Nat Comput 7(2):281–297CrossRefGoogle Scholar
  46. Zhang XS (2000) Neural networks in optimization. Springer, Berlin, pp 61–103CrossRefGoogle Scholar
  47. Zhang Z (2007) Learning algorithm of wavelet network based on sampling theory. Neurocomputing 71:244–269CrossRefGoogle Scholar
  48. Zhang Q, Beveniste A (1992) Wavelet networks. IEEE Trans Neural Netw 3:889–898CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Mining, Petroleum and Geophysics EngineeringShahrood University of TechnologyShahroodIran
  2. 2.Department of Petroleum EngineeringUniversity of North DakotaGrand ForksUSA

Personalised recommendations