Oilfield Block Development Effect Evaluation Method Based on PCA-SVM

Accuracy evaluating the development effects of oilfield blocks has always been a difficult problem in oilfield management. Traditional evaluation methods usually require oilfield experts to give evaluation results after integrating multiple factors and complex calculations, which have low interpretability and calculation efficiency. This paper applies data mining methods to the problem of oilfield block development effect evaluation, and proposes an oil field block development effect evaluation method based on PCA-SVM. Firstly, principal component analysis(PCA) is used to reduce the dimensionality of the oil field evaluation index data to eliminate the correlation between factors, and then the determined principal components are used as input features of the SVM model, thereby improving the prediction accuracy of the model. Experimental results show that the PCA-SVM model performs better on this small sample data set, with an accuracy of 0.9091. The evaluation results are more in line with the actual situation of the oil field, and it also improves the calculation efficiency of block development effect evaluation.