Research And Implementation Of The Optimization Method Of Shale Gas Development Benefit Based On Intelligent Algorithm

Most of shale gas enrichment areas in China are located in mountainous and other zones of complex structure,with poor environment,difficult transportation and high exploration and development costs.Therefore,it is particularly important to establish the development benefit model for shale gas enrichment areas and determine the optimal development scheme in advance.On the other hand,with the increasing complexity of mathematical mode,traditional optimization methods are not enough to solve such problems.Genetic algorithm,particle swarm optimization and other swarm intelligence optimization methods have some defects,such as early stagnation and likeliness to fall into local optimization.Therefore,the establishment of shale gas development benefit model and can provide experts with decision-making reference to shale gas development,assist enterprises to clearly formulate layout measures of shale gas wells,and yield high-quality and efficient exploitation and utilization of shale gas enrichment areas.In view of the above problems,this paper fully absorbs the achievements in benefit optimization research field of shale gas development,establishes single well and block development benefit model of shale gas by analyzing and fitting the historical exploration and development data of Nanchuan complex structural belt,uses intelligence optimization algorithm to carry out benefit optimization research,and seeks improvement in genetic optimization algorithm and particle swarm optimization algorithm.Firstly,in view of the benefit model of shale gas single well development,this paper proposes an improved adaptive genetic algorithm(IAGA)which adopts the strategy of adaptive dynamic adjustment of crossover rate and mutation rate.This method measures the individual concentration of population by using three parameters of maximum,minimum and average fitness value of population,and adaptively adjusts the crossover rate and mutation rate.Secondly,RSSR is adopted to improve the basic selection operator to increase population diversity.Moreover,in view of block development benefit model of the shale gas,an improved particle swarm optimization algorithm based on IAGA is proposed on the basis of previous IAGA algorithm.The selection,crossover and mutation strategies of IAGA algorithm are introduced into the particle swarm optimization algorithm to increase the diversity of particle swarm optimization and improve the convergence speed and accuracy of the model.Finally,on the basis of single well and block development benefit model of shale gas,this paper integrates the two improved optimization algorithms and other classical algorithms,designs and builds an optimization decision-making platform for the development of shale gas geological engineering integration.The research results of this paper are applied to the shale gas development project in Nanchuan complex structural belt to provide the optimal scheme for rapid simulation of shale gas development and display function of the expected workarea map for reference and practical application of experts and developers.