Abstract:Accurate prediction of fracturing sweet spots in tight reservoirs is the key to rational well placement and successful stimulation. It is difficult to predict the fracturing sweet spot of Mahu tight conglomerate reservoir due to its complex geological characteristics and strong heterogeneity. In view of the lack of effective methods to predict the fracturing sweet spot of the Mahu conglomerate reservoir and the urgent need to improve the fracturing effect of horizontal wells, based on the analysis of the main controlling factors of fracturing effect, this paper took stimulated reservoir volume (SRV) as the prediction index. Firstly, the existing evaluation model of fracturing sweet spot based on the fracability is optimized, and the prediction model of fracturing sweet spot based on machine learning is established for tight conglomerate reservoir. Finally, the prediction method for fracturing sweet spot is formed for Mahu tight reservoir. The results show that the models established by Cui, Di, La etc. have high precision in the prediction model of fracturing sweet spots based on the fracability. Among fracture sweet spot prediction models based on machine learning algorithm, random forest, GRBT and Bagging models show good performance. Although the performance of the fracturing sweet spot model based on the compressibility calculation is better under the current data, the prediction accuracy of the fracturing sweet spot model based on machine learning will continue to improve as the field data is updated and the accuracy improves. The research results have important guiding significance for evaluation of fracturing sweet spots and comprehensive sweet spots, well placement and fracturing stimulation design of Mahu tight conglomerate reservoir.