Abstract
In the process of shale gas drilling, geo-steering plays an important role in shale gas drilling. This paper analyzes the constituent elements of shale formation, and selects the most suitable constituent elements of shale formation. A particle swarm optimization algorithm based on improved inertia weight and acceleration factor is proposed to optimize the parameters of support vector machine. The lithology identification model of shale formation is established based on IPSO-SVM. According to the experimental analysis based on the field historical data, the recognition rate of IPSO-SVM is increased by 17.79%, 10.17% and 8.05%, respectively, compared with SVM, GA-PSO and PSO-SVM. In terms of running time, the running time of IPSO-SVM is 13.76s and 9.5s shorter than that of GA-PSO, PSO-SVM, respectively. By comparing the experimental results of different models, IPSO-SVM has the advantages of strong robustness, strong reliability, high accuracy and fast convergence speed. It provides a theoretical basis for precise geo-steering and finding the optimal shale layer.
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