Hybrid ANFIS with ant colony optimization algorithm for prediction of shear wave velocity from a carbonate reservoir in Iran

Abstract

Shear wave velocity (Vs) data are key information for petrophysical, geophysical and geomechanical studies. Although compressional wave velocity (Vp) measurements exist in almost all wells, shear wave velocity is not recorded for most of elderly wells due to lack of technologic tools. Furthermore, measurement of shear wave velocity is to some extent costly. This study proposes a novel methodology to remove aforementioned problems by use of hybrid adaptive neuro fuzzy inference system (ANFIS) with ant colony optimization algorithm (ACO) based on fuzzy c–means clustering (FCM) and subtractive clustering (SCM). The ACO is combined with two ANFIS models for determining the optimal value of its user–defined parameters. The optimization implementation by the ACO significantly improves the generalization ability of the ANFIS models. These models are used in this study to formulate conventional well log data into Vs in a quick, cheap, and accurate manner. A total of 3030 data points was used for model construction and 833 data points were employed for assessment of ANFIS models. Finally, a comparison among ANFIS models, and six well–known empirical correlations demonstrated ANFIS models outperformed other methods. This strategy was successfully applied in the Marun reservoir, Iran.