Machine learning in petrophysics: Advantages and limitations

Machine learning provides a powerful alternative data-driven approach to accomplish many petrophysical tasks from subsurface data. It can assimilate information from large and rich data bases and infer relations, rules, and knowledge hidden in the data. When the physics behind data becomes extremely complex, inexplicit, or even unclear/unknown, machine learning approaches have the advantage of being more flexible with wider applicability over conventional physics-based interpretation models. Moreover, machine learning can be utilized to assist many labor-intensive human interpretation tasks such as bad data identification, facies classification, and geo-features segmentation out of imagery data.