Daily production prediction for coalbed methane based on Bayesian temporal matrix factorization

Effective and accurate daily production prediction of coalbed methane (CBM) is significant for enhancing CBM recovery and developing economic evaluations of CBM exploitation. As production time increases, CBM well data gradually show some characteristics of big data and is susceptible to incomplete data and noise data. Traditional geological modeling, however, requires a variety of input factors. In the present study, we apply a time series analysis method based on Bayesian temporal matrix factorization (BTMF) without the deletion of exceptional values to investigate the characteristics of CBM development. In addition, different from the widely used data preprocessing, we innovatively scale the multidimensional data to accomplish the analysis and prediction of different attributive data. By studying the temporal and attributive characteristics of the CBM production curve, the model is applied to a CBM block in China to impute the production data and perform a multi-step rolling prediction of daily gas production.