Classification and Evaluation of Tight Sandstone Reservoirs Based on MK-SVM

It is difficult to determine the main microscopic factors controlling reservoir quality due to the strong microscopic heterogeneity of tight sandstone reservoirs, which also makes it difficult to distinguish dominant reservoirs. At the same time, there are fewer experimental samples available, and data collected from relevant research are thus worth paying attention to. In this study, based on the experimental results of high-pressure mercury injection of 25 rock samples from Chang 6 reservoir in the Wuqi area, Lasso dimensionality reduction was used to reduce the dimensionality of 14 characteristic parameters to 6, which characterize the microscopic pore structure, while a combination of different kernel functions was used to construct the multi-kernel function of the multi-kernel model to be determined. A multi-kernel support vector machine (MK-SVM) model was established for unsupervised learning of microscopic pore structure characteristic parameters that affect reservoir quality. By optimizing the hyperparameters of the model and obtaining the optimal decision function, the tight sandstone reservoirs were classified into classes I, II and III, and the classification results were verified. The results show that the accuracy of the proposed reservoir classification method is as high as 86%, which can effectively reduce the time loss and save labor costs. It provides an effective method for the comprehensive evaluation of tight sandstone reservoirs.

Automated summary

The strong microscopic heterogeneity of tight sandstone reservoirs makes it difficult to determine the main factors controlling reservoir quality. In this study, a machine learning model was used to classify tight sandstone reservoirs with an accuracy of 86%. This provides an effective method for the comprehensive evaluation of reservoirs.