Abstract
Mineral quantification is essential to evaluate gas hydrate (GH) resources because the mineral composition is closely related to the origin of sediment, the reservoir properties, and even the existence of GH. However, it is difficult to analyze the mineral compositions of GH-rich unconsolidated layers because of complex compositional combinations. Thus far, mineral composition has relied on experts' analysis. In this study, a machine learning model was developed that can efficiently probe the mineral composition from the X-ray diffraction (XRD) patterns of GH sediments in the Ulleung Basin, Korea, which has a complex composition including 12 minerals. It is the first time that machine learning algorithms have been applied to complex natural GH sediment with 12 minerals, including amorphous materials. To build a reliable data-driven analysis model, after data acquisition and preprocessing were conducted, various machine learning algorithms were employed, including convolutional neural network (CNN), recurrent neural network (RNN), multi-layer perceptron (MLP), and random forest (RF). For data acquisition, 488 sediment samples were analyzed by XRD experiment, and the intensity profiles were manually analyzed by an expert to obtain their mineral compositions. In particular, the intensity profile according to the XRD angle of incidence serves as the input data, and the corresponding 12 mineral composition provides label data for supervised learning. The RF approach yields the best prediction result with an average mean absolute error (MAE) of 2.56%, and the other algorithms also exhibits reasonable performance with average MAEs of less than 3%. If XRD is newly performed for GH sediment, the intensity profile can be automatically analyzed by the trained machine learning model in seconds. This approach will enable experts to analyze mineral compositions efficiently and reliably.
NSTL