Bazhenov Fm Classification Based on Wireline Logs

This paper considers the main aspects of Bazhenov Formation interpretation and application of machine learning algorithms for the Kolpashev type section of the Bazhenov Formation, application of automatic classification algorithms that would change the scale of research from small to large. Machine learning algorithms help interpret the Bazhenov Formation in a reference well and in other wells. During this study, unsupervised and supervised machine learning algorithms were applied to interpret lithology and reservoir properties. This greatly simplifies the routine problem of manual interpretation and has an economic effect on the cost of laboratory analysis

Bazhenov fm unconventional reservoir 3D geological modeling methodology

The Bazhenov Formation has been studied for more than 50 years, but its petroleum potential, optimal STOIIP or resource estimation approaches, the methodology used to select a reservoir, determine its properties are still unclear. The distinctive features of bituminous shale are specific geochemical properties chosen as basic parameters to perform the geological modeling of the Bazhenov deposits and determine the key areas. The main objective of this paper is to choose an optimal 3D geological modeling algorithm and test conventional (petrophysical) and specific (geochemical) properties