Time-series event prediction for the uranium production wells using machine learning algorithms

The efficient estimation of the production rate in the uranium reservoir plays a vital role in the evaluation of operational performance. This paper presents the data-driven production model in the uranium field using LightGBM for the data from the Kazakhstan deposits. We focus on predicting the fault events of the well production solution. Numerical results of this investigation show that LightGBM achieves an accurate prediction with wavelet transformation. The evaluation of the model score is conducted by using metrics such as Recall and F1. With feature engineering by wavelet transformation, we obtained the recall of 0.84 and f1 of 0.89. The LightGBM model with the Morlet wavelet transformation can be useful to solve the issue of prediction maintenance of production well.