Study on coupling of gas field produced water treatment process and fracturing fluid performance based on multi-parameter algorithm optimization

The water quality of produced water in gas field has an important influence on the performance of fracturing fluid. However, there is a disconnect between the treatment process of produced water and the preparation process of fracturing fluid, which makes the performance of fracturing fluid difficult to adapt to the fluctuation of water quality. In this study, a coupling model of gas field produced water treatment process and fracturing fluid performance based on multi-parameter algorithm optimization is proposed. Firstly, a unified evaluation system including water quality parameters and fracturing fluid performance indexes is constructed, and the weight coefficient is assigned by using the analytic hierarchy process (AHP) combined with entropy weight method. Then, a hybrid optimization model based on genetic algorithm (GA) and neural network is designed. Neural network is used to establish the nonlinear mapping relationship between water quality parameters and fracturing fluid performance, and GA is used to optimize treatment process parameters to achieve the best fracturing fluid performance. The experiment takes the produced water treatment station and fracturing well group in a shale gas field as the object. The results show that the hybrid optimization model can effectively improve the performance of fracturing fluid under the condition of water quality fluctuation, and its optimal value reaches 0.93, and the optimization efficiency is significantly better than that of single GA or neural network method. In addition, through the dynamic coupling and feedback mechanism, the system can respond to the sudden change of water quality in real time to ensure the stability of fracturing fluid performance. This study provides a new idea and method for the collaborative optimization of gas field produced water treatment technology and fracturing fluid performance, and has important practical application value.