Risk prediction for petroleum exploration based on Bayesian network classifier

Abstract

Effective prediction of petroleum exploration risks is critical for boosting oil and gas extraction efficiency and increasing economic benefits. To improve the accuracy and efficiency of risk prediction, this paper proposes a scalable k-dependence Bayesian network classifier (SKDB) method for solving the risk prediction problem of oil and gas exploration. The effectiveness and efficiency of the SKDB method were illustrated by taking the Jurassic Sangonghe Formation hydrocarbon exploration risk prediction in the Junggar Basin as an example. The model accuracy comparison results showed that SKDB has the highest accuracy compared with the state-of-the-art methods, and the accuracy was improved by 3.55∼7.63%. The oil and gas probability map prediction results indicated that the SKDB method not only showed high hydrocarbon probability in the reservoir areas, but also had better trend inference ability compared with other methods. Based on the SKDB prediction results, the remaining hydrocarbon favorable areas of the Sangonghe Formation reservoir were visualized and three types of favorable hydrocarbon distribution zones were preferentially selected, namely Type I (highly favorable), Type II (moderately favorable) and Type III (low favorable). The application results can provide a decision basis for the next oil and gas exploration and optimization strategy.