CBMproductivity is the comprehensive indicatorfor measuring potential gasproduction of CBM wells, while the productivity directly affects the economicbenefits of CBM project. Therefore, development of effective CBM productivityprediction model has important guiding significance to the exploration anddevelopment of CBM Wells.CBM is saved in the coal reservoir, and its productivity is determined by manygeological factors, and the relationship is complex, so it is difficult to establishaccurate mathematical expressions to describe the dynamic process. This paper adoptssupport vector regression machine which is widely used in predictive control andother areas currently and the improved Particle Swarm Optimization Algorithm tocreate the nonlinear function mapping relationship between geological factors andproductivity, so as to realize prediction and control of CBM well productivity.The development of support vector regression machine model requires a certainamount of sample data for the training and test of structure. In order to develop thehigh quality prediction model, it is required to set the optimum value of theparameters in the model. Therefore, in order to optimize the parameters in the model,particle swarm optimization algorithm is selected for the optimization of parameters.Although particle swarm optimization algorithm is widely used in various fields,its evolution characteristics easily lead to local convergence. In order to solve theproblem that the algorithm easily falls into local convergence, this paper basicallyproposes three improved particle swarm optimization algorithms.Particle swarm optimization based on the evolution of sub-dimensions startsfrom the evolutionary strategy of standard particle swarm optimization algorithm, andit changes the evolutionary strategy of particles of the population from overallevolution of the particles to each dimension of the particles for successive evolution.When the particle is trapped in local convergence, the sub-dimensions with poordiversity valueis are reinitialized. Regardless of whether they are used in simpleunimodal function or complex multimodal function optimization, compared withStandard Particle Swarm Optimization Algorithm, this algorithm has betteroptimization performance.Hybrid Particle Swarm Optimization based on immune mechanism integratesArtificial Immune Algorithm and Particle Swarm Optimization Algorithm which is based on sub-dimensional evolution, the evolution process is divided into twostages.The first stage is to use Artificial Immune Optimization Algorithm for globaloptimization, so as to provide high quality initial population for the next phaseoptimization. The second stage is to use Particle Swarm Optimization Algorithmwhich is based on sub-dimensional evolution for evolutionary optimization based onmultiple high quality initial population, Therefore, it has higher efficiency ofoptimization.Hybrid particle swarm optimization algorithm of multiple-populationCooperating evolution can be evolved at the same time by Artificial ImmuneAlgorithm, Chaos Algorithm and Particle Swarm Optimization Algorithm of sub-imensional evolution. Agent records current global optimal value obtained from thesethree algorithms. When Particle Swarm Optimization Algorithm gets into localconvergence, it jumps out of local convergence with high quality through the recordedcurrent global optimal value, so as to lay a good foundation for further optimizationwork. Meanwhile, the algorithm also has higher efficiency of optimization.The standard data set Boston Housing is selected as data samples. Two improvedhybrid particle swarm algorithms are applied to optimizing support vector regressionmachine parameters.The results show that the Particle Swarm Optimization Algorithmof multiple-population synergy is more applicable for optimizing model parameters.Through selecting20groups of related data for the20CBM vertical wells ofFanzhuang block to the south of Qinshui basin o develop CBM productivityprediction model with the Particle Swarm Optimization Algorithm Optimizationoptimized by using the support vector machine regression model. Compares with theprediction result of BP neural network and support vector regression machine, theresults show that the improved Particle Swarm Optimization Algorithm Optimizationand support vector machine regression model has higher precision of prediction.Meanwhile, five geological factors which are involved in model development aretested respectively according to the20groups of sample data, the impact of fivegeological factors on CBM productivity are analyzed. |