It is usually difficult to establish a petrophysical model or empirical formula for the assessment of mineral concentrations in oil- or gas-bearing shale. The radial basis function (RBF) network can be used to construct the mapping between well logs and mineral concentration. In this work, the basic principle of RBF is discussed in detail, including network structure, basis function, and network training method. The nearest neighbor algorithm is selected for the network training. Then, one case study for the mineralogy analysis is applied to show how to construct an optimum RBF network. The Gaussian spread in RBF is investigated to improve the mineral composition prediction accuracy through leave-one-out cross validation and optimum wireline logs as inputs are chosen. Finally, the concentrations of minerals such as quartz, feldspars, calcite and pyrite as well as clay minerals are calculated, and they are all in good agreement with X-ray Diffraction (XRD) measurement results. Furthermore, the errors analysis indicates that the RBF method is effective and applicable for the assessment of mineral concentrations in organic shale.