The application of NiCo-LDHs as electrode materials for supercapacitors has attracted widespread attention.

The necessity on construction for practical teaching base on campus for petroleum engineering is described from such three aspects as requirement of practical talents training, requirement of vocational skills training as well as requirement of subject construction and campus culture construction in this article, the construction mode of practical teaching base on campus for petroleum engineering is discussed and the advantage of establishing the practical teaching base on campus cooperated jointly by campus and enterprise is analyze. Finally, the efficiency produced by the practical teaching on campus is summarized with combination of the operation condition of practical teaching base on campus for petroleum engineering.

Based on Fisher discriminant theory, the Fisher discriminant analysis model (FDA) was established for predicting the possibility of drilling downhole accidents. Six factors such as WOB、pump pressure、pump flow、running speed、ROPand torque were selected as the discriminant factors of the FDA mode. A series of data from drilling downhole accidents were taken as the training samples, and then some practical engineering datas were used to verify this mode. It was showed that FDA model is one of simple and accurate method in solving the prediction of drilling downhole accidents.

The cementing quality is directly related to the normal operation of the gas well, therefore, the evaluation of cementing quality is key to the correctly use the gas well as well as to take measures to protect the gas well. In this paper, four first wave amplitudes at the same depth point when using the borehole compensated sonic logger with double transceiver technique to carry out the acoustic amplitude log operation are served as the discriminant factors to evaluate the cementing quality. Taking the engineering actual measured data as the learning samples and using the particle swarm optimization to optimize the parameters of support vector machine, this paper established the intelligent evaluation model for cementing quality based on particle swarm optimization (PSO) and support vector machine (SVM). The model employs the excellent characteristic of SVM which has high speed of solving and could describe nonlinear relation as well as the characteristic of PSO which has global optimization. Through test of engineering samples, the research result showed that this model has fast astringency and high precision, providing a new method and approach for the fast and accurate evaluation of the well cementing quality.