Summarized Applications of Machine Learning in Subsurface Geosciences

Abstract

Geoscientists have been implementing machine learning (ML) algorithms for several classifications and regression related problems in the last few decades. ML’s implementation in geosciences came in different phases, and often these broadly followed or lagged after certain advances in computer sciences. We can trace back some of the early applications of modern ML techniques to 1980–1990. Geoscientists were mostly dealing with deterministic analytical solutions at that time, and they were encouraged to do so at their organizations. This is also the time when geostatistics started flourishing in reservoir characterization and modeling efforts. Then, the early 2000’s saw a slight uptick in ML applications, mostly neural networks and decision trees. Since 2014–2015, a lot of ML-related work was published. In addition to open-source languages, this also has to do with access to the massive volume of data from unconventional reservoirs. And then, since 2017, there has been an explosion of deep learning related work. This again corresponds to the convolutional neural network architecture published by Goodfellow in 2014. Initially, the ML work in geosciences focused on petrophysics, seismic, and now core and thin section images. Another growing trend is the application of ML in passive geophysical data analysis (seismology, gravity, and magnetic, etc.). As of now, most of the published studies on ML are confined to outlier detection, facies, fracture, and fault classification, rock property (e.g., poro-perm-fluid saturation-total organic carbon-geomechanics) prediction, predicting missing logs/variables, and well log correlation. In this chapter, we will review some of these popular research problems tackled by ML.