Search

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.

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. [ABSTRACT FROM AUTHOR]

The self-organizing Group Method of Data Handling (GMDH) functional network is effective in predicting oilfield production. During operation the division of data sample depending on artificial classification cannot lead to global optimum in great probability and the variables are probably eliminated early in the iterative process in traditional GMDH algorithm. Recent years, GMDH model has been improved through many artificial intelligent models, but few people take the optimization of the model structure into account. In this paper, different training and testing set grouping and the effects of variables transmission were studied. The modified GMDH algorithm was optimized using the original variables preservation method and the random sample method, which was applied to the oilfield production forecasting simulation. The results of the modified GMDH algorithm, the traditional GMDH algorithm, ANNs and the empirical equations for predicting annual oil production were compared. The simulative results indicated that the modified GMDH model was the best tool for data-fitting with lowest error (RMSE = 13.9440, MAPE = 0.1121 and SI = 0.0378) and highest accuracy (R = 0.9984). [ABSTRACT FROM AUTHOR]