The Callovian-Oxfordian carbonate reservoirs in Block A of the Right Bank of the Amu Darya River, Turkmenistan, are featured by multiple types of carbonate rocks, strong reservoir heterogeneity, and a complex relationship between lithology and elec tric property, making it difficult to predict the quality and lateral distribution of the reservoirs. Based on the observation and analysis of core and cast thin section, the lithofacies were subdivided into granular micrite facies, micritic sparry grainstone facies and bioher mal limestone facies according to the degree of reservoir development. The corresponding logging responses of these lithofacies were determined and their electrical properties were analyzed. Then logging identification criteria were summarized for these lithofacies. A series of mathematical analytical techniques such as main coordination analysis, wavelet analysis and multiple parameters progressive discrimination analysis were used to build a mathematical relationship model between lithofacies and logging facies, which can realize automatic identification of lithofacies with logging data. The results showed that the XVm layer is dominated by micritic sparry grainstone facies and biohermal limestone facies, the XVp layer by well developed granular micrite facies and micritic sparry grain stone facies, and the XVac layer by granular micrite facies. The XVm layer is the highest in reservoir quality, followed by the XVp and XVac layers. Reservoir quality prediction results obtained by using this method agree well with real test results with a coinci- dence rate over 80%.