Cleaning out the pulverized coal deposited at the bottom of a coalbed methane (CBM) well is key to achieving continuous CBM drainage and prolonging the workover period. In this study, Fluent is used in conjunction with the standard k-ε model and the Eulerian-Eulerian model to simulate and analyse jet erosion of deposited pulverized coal particles. The depth and width of the stable erosion pit that is formed by jet-impacting deposited pulverized coal under different conditions are determined and provide a theoretical basis for the cleanout of pulverized coal in the bottom of a CBM well. In this paper, the three parameters of the jet target distance, nozzle diameter and nozzle outlet flow velocity are selected to perform an orthogonal simulation. The change trends in the depth and width of the scouring pit with time are determined. The results show that jet impacting of deposited pulverized coal can be categorised into four stages, periods of rapid growth, stability, jet swing and dynamic stability. A sensitivity analysis shows that the nozzle outlet flow velocity has the strongest influence on the depth of the scouring pit among the selected parameters. The depth of the jet impact pit can reach the maximum depth at t = 3 s, while the width of the impact pit can reach the maximum after t = 7 s. This can provide key design parameters for CBM well pulverized coal impacting operation. It is of great significance for capacity damage control during CBM well workover operation.
Beam pumper is the earliest and most popular rod pumper driven by surface dynamic transmission devices. Drawing on modern theories and methods of industrial model design, the model optimization of beam pumper could promote the diversity, serialization, standardization, generalization, precision balance, and energy reduction of beam pumper design. Therefore, this study tries to optimize the model of beam pumper based on a neural network. Specifically, the system efficiency of beam pumper was decomposed, the surface and downhole working efficiencies were analyzed, and the model optimization flow was explained for beam pumper. Then, a radial basis function (RBF) neural network was established and trained by the sample data on beam pumper model. Besides, the mapping between model parameters and the optimization objective (system efficiency) was constructed. Moreover, the authors summed up the model optimization contents of beam pumper and predicted the relevant parameters of model optimization. The results demonstrate the effectiveness of our model.
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