an integration of conventional rock typing methods and fuzzy c-means clustering algorithm: a case study

Determination of rock types in hydrocarbon reservoirs (whether carbonate or clastic) is very important. therefore, various methods for identifying rock types have been introduced and developed in recent years. in this study, we have applied some conventional rock typing methods in combination with a clustering machine learning algorithm in order to optimize the number of clusters and eventually group input data in accurate rock types. the results show that flow zone indicator (fzi) method integrated with the fuzzy c-means (fcm) algorithm was the best approach for detecting rock types with the range of correlation coefficient (r2) from 0.81 to 0.87.