China holds abundant deep reservoirs, where temporary plugging and diversion fracturing is a key technology for efficient development. Effective transport and plugging of agents within fractures to boost net pressure are key to the success of this technology. Moreover, optimizing the transport parameters can provide practical data support for field operations. While numerical simulation is an important optimization tool, it often yields single-point parameter solutions and requires lengthy computation, limiting its real-time applicability. In contrast, surrogate modeling enables fast target prediction and enhances optimization efficiency. Among various multi-objective optimization algorithms, the non-dominated sorting genetic algorithm II (NSGA-II) demonstrates strong performance in both speed and convergence. In this study, a temporal convolutional network (TCN) was ultimately selected to construct the surrogate model for rapid prediction, which was coupled with NSGA-II for optimization. The main findings are: (1) The TCN model achieved prediction errors below 5%, with transport pattern predictions showing over 80% agreement with simulations; (2) Cost was highly sensitive to the mass concentration of temporary plugging agents and injection rate, minimizing cost reduced their optimal ranges; (3) The mass concentration had little influence on most objectives except cost, resulting in minimal variation in its optimal range across different criteria; (4) Injection rate strongly influenced the dimensionless average velocity of temporary plugging agents, minimizing it required lower injection rate; (5) Carrying liquid viscosity significantly impacted dimensionless inlet pressure, leading to higher optimal ranges when high dimensionless inlet pressure was desired.
 

Deep Reservoirs, Optimization, Temporary Plugging Agents, TCN, NSGA-II