Fracture identification by semi-supervised learning using conventional logs in tight sandstones of Ordos Basin, China

Abstract

Fracture identification using conventional logs is a cost effective way of identifying fracture zones in reservoirs. However, there are challenging problems including complex well log responses and small amount of labelled data available from cores or image logs, making it difficult to build a prediction model with a good generalization capability. To address these problems, a semi-supervised learning method termed Laplacian support vector machine (LapSVM) is introduced in this work, which is a combination of the supervised kernel method and the unsupervised clustering method. LapSVM inherits SVM's capability of handling nonlinear problems and overcomes partially the issue of limited labelled data by using the unsupervised clustering technique with the help of abundant well log information. To examine the effectiveness of LapSVM for fracture identification in tight reservoirs, a dataset from the tight sandstones of the Ordos Basin in China is used. Both statistical and geological evaluations indicate that LapSVM outperforms other three nonlinear SVM methods tested. It has been demonstrated that LapSVM can provide an accurate and effective means for the identification of fracture zones in tight reservoirs.