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The estimation of formation permeability in a carbonate reservoir using an artificial neural network

Yeganeh, M ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1080/10916466.2010.490805
  3. Publisher: 2012
  4. Abstract:
  5. Reservoir permeability is an important parameter that its reliable prediction is necessary for reservoir performance assessment and management. Although many empirical formulas are derived regarding permeability and porosity in sandstone reservoirs, these correlations cannot be accurately depicted in carbonate reservoir for the wells that are not cored and for which there are no welltest data. Therefore, having a framework for estimation of these parameters in reservoirs with neither coring samples nor welltest data is crucial. Rock properties are characterized by using different well logs. However, there is no specific petrophysical log for estimating rock permeability; thus, new methods need to be developed to predict permeability from well logs. One of the most powerful tools that we applied by the authors is artificial neural network (ANN), whose advantages and disadvantages have been discussed by several authors. In particular, 767 data sets were used from five wells of Bangestan reservoir in a southwestern field of Iran. Depth, Neutron (NPHI), Density (RHOB), Sonic (DT) logs, and evaluated total porosity (PHIT) from log data were used as the input data and horizontal permeability obtained by coring was as target data. Sixty percent of these data points were used for training and the remaining for predicting the permeability (i.e., validation and testing). An appropriate ANN was developed and a correlation coefficient (R) of 0.965 was obtained by comparing permeability predictions and the actual measurements. As a result, the neural science can be used effectively to estimate formation permeability from well log data
  6. Keywords:
  7. Artificial neural network ; Back propagation carbonate reservoir ; Permeability ; Carbonate reservoir ; Correlation coefficient ; Data points ; Data sets ; Empirical formulas ; Formation permeability ; Input datas ; Log data ; Permeability prediction ; Petrophysical ; Reservoir performance ; Reservoir permeability ; Rock permeability ; Rock properties ; Sandstone reservoirs ; Total porosity ; Well log data ; Well logs ; Estimation ; Forecasting ; Mechanical permeability ; Neural networks ; Neutron logging ; Reservoir management ; Well logging ; Petroleum reservoir engineering
  8. Source: Petroleum Science and Technology ; Volume 30, Issue 10 , 2012 , Pages 1021-1030 ; 10916466 (ISSN)
  9. URL: http://www.tandfonline.com/doi/abs/10.1080/10916466.2010.490805