Amega Yasutra
Machine learning and big data applications have started to be used in the petroleum world, this study aims to look at the possibility of applying artificial intelligence or machine learning in petroleum calculations and applications. In addition, this study aims to collaborate and bridge petrophysics and reservoirs in conducting reservoir characterization using machine learning. In petrophysical analysis using well log data, clay volume, porosity, and water saturation are generated. In reservoir analysis using core data can be used for reservoir characterization, but in very limited data only on the core. The processes carried out by the two are interconnected with each other, especially in the reservoir characterization process. In an effort to improve the results of reservoir characterization, collaboration between petrophysics and reservoirs is needed to facilitate further analysis on a large scale. Broadly speaking, the results of this study are a new method of characterizing reservoirs in the well scale for various depths that do not have core data by using machine learning methods. The proposed method is expected to be able to provide output in the form of reservoir characterization by using machine learning applications.
Penerapan Karya Tulis
Possibility of applying artificial intelligence or machine learning in petroleum calculations and applications