Data-driven intelligent productivity prediction model for horizontal fracture stimulation - Li, Qian; Sui, Yiyong; Luo, Mengying; Guan, Bin; Liu, Lu; Zhao, Yuan (Frontiers in Earth Science, 2025)
Traditional methods for predicting post-fracturing productivity in horizontal fractures primarily use fracture and formation parameters for calculations. Complex fracture data are difficult to obtain, and these methods do not consider the effects of displacement mechanisms, fracturing techniques, or time factors on post-fracturing productivity. To address the limitations and shortcomings of existing post-fracturing performance prediction methods for horizontal fractures, a horizontal fracture well productivity prediction model was established by combining physical mechanisms with data-driven approaches. First, based on physical mechanisms, factors influencing well productivity were selected from reservoir properties and fracturing operations. Second, relevant characteristic parameters were chosen from geological conditions, production characteristics, and fracturing techniques to perform clustering analysis on fracturing intervals in the data sample. Intervals with similar multidimensional physical features were grouped into the same category. Under the assumption of similar characteristics and mechanisms, correlation analysis was conducted for each fracturing interval category to identify the dominant controlling factors affecting post-fracturing productivity in each reservoir type. Machine learning algorithms were used to establish intelligent models describing the relationships between post-fracturing production enhancement effects, dominant factors, and production time for each reservoir category. Finally, during fracturing design, the optimal productivity prediction model was matched to each interval based on its characteristics to predict post-fracturing productivity. Additionally, the influence patterns of proppant volume on well productivity were comprehensively analyzed to optimize reasonable proppant volumes for different wells and intervals. Field validation showed that the productivity prediction model achieved an average error of 7.06%, providing a basis for horizontal fracture engineering design and achieving cost reduction and efficiency improvement in oilfield development.