The application of machine learning under supervision in identification of shale lamina combination types — A case study of Chang 73 sub-member organic-rich shales in the Triassic Yanchang Formation, Ordos Basin, NW China

Abstract

Organic rich laminated shale is one type of favorable reservoirs for exploration and development of continental shale oil in China. However, with limited geological data, it is difficult to predict the spatial distribution of laminated shale with great vertical heterogeneity. To solve this problem, taking Chang 73 sub-member in Yanchang Formation of Ordos Basin as an example, an idea of predicting lamina combinations by combining 'conventional log data — mineral composition prediction — lamina combination type identification' has been worked out based on machine learning under supervision on the premise of adequate knowledge of characteristics of lamina mineral components. First, the main mineral components of the work area were figured out by analyzing core data, and the log data sensitive to changes of the mineral components was extracted; then machine learning was used to construct the mapping relationship between the two; based on the variations in mineral composition, the lamina combination types in typical wells of the research area were identified to verify the method. The results show the approach of 'conventional log data — mineral composition prediction — lamina combination type identification' works well in identifying the types of shale lamina combinations. The approach was applied to Chang 73 sub-member in Yanchang Formation of Ordos Basin to find out planar distribution characteristics of the laminae.