Machine learning in petrophysics: Advantages and limitations

Abstract

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.

However, the validity of the outcome from machine learning largely depends on the quantity, quality, representativeness, and relevance of the feeding data including accurate labels. To achieve the best performance, it requires significant effort in data preparation, feature engineering, algorithm selection, architecture design hyperparameter tuning, and regularization. In addition, it needs to overcome technical issues such as imbalanced population, overfitting, and underfitting.

In this paper, advantages, limitations, and conditions of using machine learning to solve petrophysics challenges are discussed. The capability of machine learning algorithms in accomplishing different challenging tasks can only be achieved by overcoming its own limitations. Machine learning, if properly utilized, can become a powerful disruptive tool for assisting a series of critical petrophysics tasks.