Oilfield analogy and productivity prediction based on machine learning: Field cases in PL oilfield, China

In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity(MDI) method. To establish productivity prediction models, linear regression(LR), random forest regression(RF), support vector regression(SVR), backpropagation(BP), extreme gradient boosting(XGBoost), and light gradient boosting machine(Light GBM) algorithms are used. Their performance is evaluated using the coefficient of determination(R^(2)), explained variance score(EV), mean squared error(MSE), and mean absolute error(MAE) metrics. The Light GBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query,oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development.