The back propagation based on the modified group method of data-handling network for oilfield production forecasting

Abstract In this paper, a novel hybrid forecasting model combining modified group method of data handling (GMDH) and back propagation (BP) is introduced for time series oilfield production forecasting. The proposed model takes advantages of both the modified GMDH networks in effective parameter selection and the BP network in excellent nonlinear mapping and provides a robust simulation ability for oilfield production with higher precision. Various production parameters of an actual oilfield were utilized to analyze and test the annual output predicted by proposed model (modified GMDH-BP). The performance of the proposed model was compared with the multiple linear regression (MLR), GMDH, modified GMDH, BP, and the hybrid model combining group method of data handling and back propagation (GMDH-BP) using time series annual production data. The relative error, correlation coefficient (R), root mean square error, mean absolute percentage of error, and scatter index were utilized to investigate the performance of the presented models. The evaluation results indicate that the hybrid model provides more accurate production forecasts compared to other models and exhibits a robust simulation ability for capturing the nonlinear relation of complex production time series prediction of oilfield.