Discrimination of AVO Types in Shallow Reservoirs Based on Random Forest Algorithm

As we all know, AVO technology can be used to identify gas-bearing reservoirs and is of great significance to oil and gas exploration. The manual identification of AVO types of reservoirs has large human interference factors, low identification accuracy and long time-consuming. Therefore, this paper introduces the random forest algorithm, uses bootstrap repeated sampling and branch and leaf node splitting techniques to generate a large number of decision tree classifiers, and realizes the identification of the AVO type of the reservoir by counting the classification results of all decision trees. Firstly, a velocity density model is established based on logging data in the working area. Secondly, use the Shuey approximation formula to calculate the AVO curve and obtain the fitted polynomial corresponding to the curve. Thirdly, the morphological feature parameters are extracted according to the fitting polynomial as the input parameters of the training data set of the random forest algorithm, and the artificial AVO type recognition results are used as the output parameters to train and obtain a decision tree classifier. Finally, using the characteristic parameters of the AVO curve of the actual pre-stack seismic data as input parameters, the classification of the AVO type of the reservoir in the working area is obtained through the classification of the random forest decision tree. By comparing the results with the approximate support vector machine algorithm, it can be seen that the two algorithms approximate the AVO type discrimination results of the reservoir, and both have high accuracy, but the random forest algorithm requires fewer feature attributes, shows stronger generalization and has better universality.