Atomized droplet size prediction for supersonic atomized water drainage and natural gas extraction

In the later stage of natural gas reservoir exploration, the wellbore pressure is reduced and the liquid accumulation is serious, in order to solve the problem of liquid accumulation and low production in low-pressure and low-yield gas wells, the supersonic atomization drainage gas recovery technology is used to improve the recovery rate. By studying the influence of working condition parameters of downhole nozzle atomization drainage gas recovery on atomization effect and liquid carrying rate, a new physical model of atomization nozzle is established, the back propagation (BP) neural network atomization model and BP neural network atomization model optimized by genetic algorithm (GA) is established, and the Matlab is used to train the 45 groups of data sets before the experiment. After the model training, the normalized atomization parameters are trained for sensitivity analysis. The relationship between the strength and weakness of the factors affecting Sotel's average droplet particle size (SMD) is as follows: gas flow (Q(g))>liquid inlet diameter (d)>liquid phase flow (Q(l)). The last 15 sets of data sets outside the training samples were tested by BP model and BP neural model optimized by genetic algorithm (GA-BP), and the size of SMD was predicted. The experimental results show that the determination coefficient R-2 of the established GA-BP network model to the experimental parameters is 0.979 and the goodness of fit is high; the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the predicted value of GA-BP atomization model and the experimental value are 4.471, 1.811 and 0.031 respectively, the error is small, the prediction accuracy is high, and the establishment of the model is accurate. The GA-BP model can efficiently predict SMD under different operating conditions, at present, the new supersonic atomizing nozzle has been successfully applied to the Xushen gas field block of Daqing Oilfield, which can improve the recovery rate of natural gas by 4.5-8.6%, alleviate the problem of effusion near the end of oil exploration, and has certain guiding significance for solving the problem of wellbore effusion and improving production efficiency.

Automated summary

A new supersonic atomization drainage gas recovery technology was developed to improve recovery rate, with established models and neural networks for predicting droplet size accurately. Experimental results demonstrate high accuracy and successful application in increasing natural gas recovery efficiency.