Predicting Oil Productivity of High Water Cut Fractured Horizontal Wells in Tight Oil Reservoirs Based on KAN

作者：Hongjun Zhang, Yi Tao, Dalin Zhou, Hongbo Zhang, Yuyang Zhang, Rui Xue, Zhuyi Zhu, Zhigang Wen · 发表于：Processes · 年份：2025 · DOI：10.3390/pr13113629 · 被引用次数：2 · 研究领域：Hydraulic Fracturing and Reservoir Analysis、Reservoir Engineering and Simulation Methods、Oil and Gas Production Techniques

The high water cut period represents a critical phase in the development of tight oil wells, and accurately forecasting productivity during this stage is essential for effective oilfield development planning. However, traditional reservoir engineering methods find it difficult to handle complex oil-water seepage behaviors and cannot accurately predict the productivity of tight sandstone oil wells in the high water cut period. Therefore, this paper proposes a method for predicting the productivity of tight oil reservoirs based on a hybrid deep learning algorithm, using the geological, engineering, and development parameters of 342 fractured horizontal wells in the Z211 block of Heshui Oilfield. The model was based on the KAN deep learning algorithm, and the WOA meta-heuristic optimization algorithm was used to optimize the KAN model parameters. Combined with multi-dimensional parameters such as oil well geology, engineering and development, an efficient and accurate productivity prediction model was established. Based on the interpretability of the model itself, the key features of the model and the factors affecting productivity are explained in combination with the SHAP (SHapley Additive exPlanations) value and the Pearson coefficient, revealing the changing relationship of productivity and the degree of influence of different parameters on productivity. The results indicate that the KAN-WOA model demonstrates strong performance in both prediction accuracy and robustness for...