Amega Yasutra
Machine Learning is an application branch of Artificial Intelligence (Artificial Intelligence) which focuses on developing a system that is able to learn itself without having to repeatedly in programs by humans. In recent years, the Machine Learning application has penetrated the oil world including in the field of production optimization. Some of the things that become focus in production are how to characterize the reservoir through petrophysical and reservoir analysis. The process of petrophysics analysis can identify oil and gas reservoirs through calculating petrophysical parameters. The identification carried out includes calculating water saturation, clay, porosity, permeability obtained from the results of logging analysis on the rocks in the well. There is no collaboration process between Petrofisik data on Well log results and reservoir data in determining the reservoir characterization for various depths can be applied with the Machine Learning. Therefore, in this study the development method was carried out to carry out the characterization of reservoirs that cover the methods of characterization of reservoirs and collaborated with petrophysical data using the Machine Learning method that facilitates the reservoir characterization by tie the reservoir of the data core (which is very limited) with data well Log.
Application of appropriate technology, the application of writing
There is no collaboration process between the petrophysical data of Well log results and reservoir data in determining the characterization of reservoir for various depths.