Horizontal well staged fracturing is a main development mode used for tight sandstone gas reservoirs,and the reasonable design of hydraulic fracture parameters is crucial for the cost effective development of such reservoirs.The automatic optimization methods based on swarm intelligence optimization algorithm and machine learning surrogate model often face problems such as numerous numerical simulations,slow convergence,and complex update of surrogate model.In addition,it is difficult to obtain the best fracture parameter design by traditional methods such as on-site engineer experience and orthogonal experiment.In order to solve these problems,this paper establishes a new method for optimizing fracture parameters of tight sandstone gas reservoirs based on hybrid optimization algorithm and adaptive deep neural network(DNN).First,a mixed strategy of cyclic iteration between genetic algorithm(GA)and Bayesian adaptive direct search(BADS)is adopted in the hybrid optimization algorithm.In the process of adaptive learning,the"maximum average distance point"is proposed to be the most uncertain solution,which,together with the most promising solution and a small number of Latin hypercube sampling solutions is used to update the DNN surrogate model being optimized.Subsequently,the established optimization method is applied to optimize the fracture parameters of heterogeneous tight gas reservoirs.And the following research results are obtained.First,in standard test function and low-dimensional fracture parameter optimization,GA+BADS hybrid optimization algorithm exhibits much higher optimization speed than GA.Second,in high-dimensional fracture parameter optimization,GA+BADS hybrid optimization algorithm improves the economic net present value(NPV)by CNY1.31 million in about half of the total GA numerical simulation times,and achieves a significant increase in convergence speed and optimization accuracy.Third,compared with GA+BADS hybrid optimization algorithm,adaptive DNN surrogate accelerated optimization can further reduce the number of operations in the numerical simulation by 24.54％when obtaining the same NPV.In conclusion,the proposed optimization method improves the optimization efficiency significantly,provides a feasible and efficient intelligent optimization process in solving the problems in the parameter design of hydraulic fractures in unconventional oil and gas reservoirs,and will powerfully promote the large-scale benefit development of unconventional oil and gas.
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