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

Abstract

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.Evaluation metrics, including the mean absolute percentage error (MAPE) and the root mean square error (RMSE), are applied to evaluate the performance. It is found that the imputation by the application of BTMF achieves highly satisfactory estimates (in all tested CBM wells, the maximum MAPE and RMSE are 0.1182 and 245.5882, respectively) and is comparable to the other factorization models. Results also show that BTMF is able to account for the variance in gas production with different multi-step rolling predictions and different predicting scenarios, with the MAPE matrix of five CBM wells of different predicting strategies varies from 0.051 to 0.208, 0.121 to 0.307, 0.099 to 0.221, 0.062 to 0.224 and 0.054 to 0.135, respectively. Because of the similar mechanisms of CBM development, this method can provide a reference for predicting the daily gas production of other CBM wells.