Estimation of porosity from seismic attributes using a committee model with bat-inspired optimization algorithm

Porosity is one of the most petrophysical parameters, which has profound impact on reservoir characterization, reserves estimation, and production forecasting. This parameter is determined from experimental implementation in laboratory core analysis, as well as interpretation of porosity log. Since there exists no reliable source of porosity data (core data and porosity log data) before of drilling a well, developing a reliable model for estimation of porosity from seismic attributes is highly valuable. In current study, a merged model is proposed for identifying formulation between porosity and seismic attributes in a field which is located in the Persian Gulf. In the first step, suitable seismic attributes which have prominent influence on porosity are extracted through forward stepwise selection variable method and considered as input parameters of the model. Secondly, input variables are transformed into higher correlated data space by virtue of nonparametric method so-called alternating conditional expectation (ACE). In third step, making quantitative correlation between ACE transformed of input parameters and porosity through improved intelligence model, including optimized neural network (ONN), optimized support vector regression (OSVR), and optimized fuzzy logic (OFL) are achieved. Optimization method which embedded in intelligence models formulation for ameliorating those performances is bat-inspired algorithm (BA). In the last step, outputs of improved models are combined through a committee machine (CM) in the sake of enhancing in the prediction accuracy. Optimal contribution of improved models in overall estimation is computed by mean of the BA method. Comparison between the individual models and the CM model shows that integrating models with the CM produce results with lowest error and highest correlation and consequently is superior. The results prove that proposed strategy in this study is reliable alternative way for mapping functional dependency between porosity and seismic attributes.