MACHINE LEARNING WITHOUT A TEACHER TO DETECT ANOMALIES IN NATURAL GAS DEPOSITS
Comparison data of well logs with core data can then be used for so-called supervised machine learning. For this reason, unsupervised learning algorithms can be used in order to carry out preliminary assessment, which help to type the data. This task is especially important for gas fields, since the presence of natural gas in the pore space of the reservoir leads to the appearance of anomalies that can be detected using this approach, which can allow not only to divide the intervals by reservoir properties, but also to identify zones saturated with natural gas. An important task in modeling natural gas fields is facies distribution, the first stage of which is the very identification of facies and determination of their quantity.