Traditional minor repair machines have low control efficiency and low automation. In order to improve the brake control efficiency of the oilfield minor repair machine, the oilfield minor repair machine brake control system based on wireless sensor network is designed. The front-end controller collects the data of the oilfield repair machine through the wireless sensor and realizes the communication between the sensor nodes and the data interaction with the communication node through the field bus inside the equipment. The server uploads the processed data through the 3G network, and the server receives the data from the front-end controller and saves it in the database. The remote-control center accesses the server through the Internet to view the front-end controller data. The brake control module uses the expert fuzzy PID control method to control the speed, oil pressure, and operating status of the oilfield minor repair machine and sends the data to the front-end control center to complete the oilfield minor repair remote-control machine by sending control instructions. Experimental results show that the system can meet the braking control requirements of oilfield minor repair machines, the output can quickly track input changes, the braking control effect is good, the dynamic deviation of the machine can be reduced, and the braking control efficiency can be improved.
In the stable production stage CBM wells have the characteristics of high gas production and low water production. The use of continuous velocity tube technology for drainage can achieve better drainage results. Accurate and rapid prediction of the pressure drop of velocity pipe string production in a coalbed methane well has become the key to the operation and management of velocity pipe technology. This paper uses the nonlinear mapping and prediction capabilities of the BP neural network to build a three-layer BP neural network to construct a velocity pipe string production pressure drop prediction model. The model is based on gas production, water production, bottom hole pressure, pipe string diameter, and well depth. The five factors are input, and the pressure drop of the pipe string is the output, which can quickly and accurately realize the pressure drop analysis and calculation of the speed pipe drainage. The analysis shows that it is feasible to use the BP neural network to calculate and analyse the pressure drop of the velocity string of coalbed methane wells.
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.
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