Tight Reservoirs Classification using Random Forest: A Case Study of He 8 Member in Eastern Yan'an Gas Field

Abstract

Tight sandstone reservoir is very important in oil and gas exploration in China. Tight reservoirs classification and evaluation are a frontier research field. There are many indexes involved in reservoirs classification, and it is necessary to judge the reservoir type according to personal experience, which consumes lots of time and manpower. Therefore, a new classification method of tight reservoirs using random forest is proposed. Firstly, the high pressure mercury injection curves of tight sandstone reservoirs of He 8 member of Lower Shihezi Formation in eastern Yan'an Gas Field are selected as the research data. Four characteristics for classification are obtained by principal component analysis. Secondly, the random forest using CART is used to classify and obtain the results of reservoir classification. Finally, classification results are verified and parameters of the random forest are optimized. Experimental results show that the proposed reservoirs classification method has high accuracy and low calculation cost. It can effectively reduce time loss and save manpower, and has good generalization.