Abstract：The formation of carbonate rocks is affected by many factors such as sedimentation， diagenesis and sedimentary environment. Usually the pore structure of carbonate rocks is related to the lithofacies of rocks. The mineral composition of carbonate rocks is complex， and the pore types of carbonate reservoirs are diverse， resulting in significant differences in rock physical properties between carbonate rocks of the identical mineral， the same lithology， and the different lithofacies. With strong solubility， the matrix modulus and dry skeleton modulus of carbonate rocks are greatly influenced by the pore structure of the rock. It is difficult to obtain the physical model of the rock accurately. The artificial intelligence lithofacies prediction method take core analysis data and logging data as input， and applies the depth learning method to predict lithofacies in whole well section. The results have high resolution， good continuity， strong reliability， and less manual intervention. Combining with quantitative pore structure analysis， it can effectively improve the accuracy of the petrophysical modeling of carbonate rocks. Through the practical application of this method in the carbonate rock formation of the Cretaceous M Formation in Block M， this paper has achieved good results， which proves the effectiveness of this method.
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