Method for high resolution processing of conventional acoustic slowness log

Based on analyzing the vertical response characteristic of conventional acoustic logging, researching thoroughly the very complex nonlinear relationship between conventional acoustic slowness log with low resolution and slowness log with high resolution, forecasting model is established using training samples consisting of key wells' slowness curves with high, low vertical resolution to create the other well's slowness curve with higher vertical resolution by means of Artificial Neural Networks(ANN) technique. Some 8 wells in Daqing Oilfield are selected as object of establishing model and forecasting. As shown in the practical applications, the average relative error of high resolution acoustic slowness log predicted is 6.80%, the maximum average relative error is 24.8% at 1.631 km while the minimum average relative error is 0.06% at 1.629 km. Therefore,it has wide application perspective.