Artificial Neural Networks Identification of Lithology-Types in Complex Carbonate from Well Logs, Block K, in Uzbekistan

Abstract

In oil and gas exploration of Block K in Amu Darya basin Uzbekistan, the reservoir lithologies are mainly in different carbonate rocks, the more types of rocks, the more various reservoir space is, as a result, it brings some difficulties to the reservoir quantitative evaluation. Therefore, according to this situation that the difficulty in identification of complex carbonate lithologies is, in this study block, artificial neural network analysis method is used in this paper. The method combines mud logging, cutting, core data, well logging, studies logging response characteristics of the different types of carbonate rocks, establishes lithology identification index. In this study, the method is used in identifying the types of carbonate rocks, the identified result compared to actual rocks displays about 70.51~87.23%, and it plays the positive role for reservoir quantitative evaluation.