Prediction of reservoir damage caused by fracturing fluid based on BP neural network

• Published in: 2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA) pp. 298 - 301
• Main Authors: Chen, Fei, Bai, Bo, Wang, Zu-Wen, Zhou, Lu, Xu, Ying-Xin, An, Chong-Qing
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2021
• Subjects: BP Neural Network; Costs; Fluids; formation damage; fracturing fluid; Neural networks; Oils; Predictive models; Productivity; reservoir sensitivity; Sensitivity
• DOI: 10.1109/AIEA53260.2021.00070

Low permeability reservoirs need fracturing to obtain productivity. The damage of fracturing fluid to reservoir is an important factor affecting productivity. Obtaining reservoir sensitivity data is an important task for optimizing fracturing fluid. At present, data is still obtained from reservoir sensitivity experiments in China. This traditional method has relatively high veracity, but it costs a lot of manpower and it is often behind the project. Thus, a prediction model which BP neural network is put forward. The success rate is 100% when this method is applied to the two wells of changqing Oil field. The experimental result indicates that BP network, good prediction results can be obtained.