Application of Machine Learning Algorithms for Prediction of Reservoir Properties in Bazhenov Formation from Simultaneous Inversion

Summary The work explores how different machine learning algorithms can be used to predict Bazhenov formation reservoir properties such as rock type, heavy hydrocarbons and kerogen volume fraction, total organic carbon content, total, effective and dynamic porosity and water saturation from the results of simultaneous inversion of seismic data. The workflow for data processing and handling is proposed and application of various machine-learning models is investigated. Finally, practical issues of data interoperability between different pieces of software are discussed and tips on implementation of the obtained trends in reservoir modeling are given.