Carbonate rocks have the characteristics of strong heterogeneity

changing lithology and various rock types

which make it difficult to recognize their fine lithologic features and seriously restrict the calculation of reservoir parameters as well as subsequent oil/gas development. The carbonate reservoirs in the Leikoupo Formation in western Sichuan Basin were studied to deal with the problem. Core and thin slice observation and other analysis results revealed eight distinctive lithologic facies in the reservoirs: the algal bonded dolomite

crystal powder dolomite

dolomicrite

calcite dolomite

dolomitic limestone

limestone

gypsum dolomite and gypsum. Their log responses were also identified. In addition

machine learning was combined with multi-resolution graph-based clustering to establish a lithology identification training model by using the known and named lithologic samples as training data. Subsequently

the lithology of reservoirs in other wells was predicted with the K-Nearest Neighbor

thus realizing a fine identification of different lithologic facies. Field application of the method shows a 91.3% of overall coincidence rate of lithology recognition

indicating an improved accuracy in lithology identification.