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

Jia Guo,Wei Huang,Hong Xie,Huipeng Yang,Hongmei Wang,Kai Yang,Fajun Guo

doi:10.1007/s13202-018-0582-9

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.

Highlights

The production of oil field is a fundamental indicator that providing decision-making basis for oil production investment and adjustment disposition
The comparison chart indicates that the hybrid model combining the modified group method of data handling (GMDH) network and back propagation (BP) algorithm is more approximate to the actual production for the oilfield than other models listed
We have demonstrated systematically how the yearly production of oilfield could be represented by a hybrid model combining the modified GMDH and BP models

Summary

Introduction

The production of oil field is a fundamental indicator that providing decision-making basis for oil production investment and adjustment disposition. 1945; Hubbert 1980; Wang and Feng 2016) and have been well developed in the past decades This method is widely used for the advantage of simple principle and handy calculation, but has a larger prediction error when dealing with complex nonlinear system. The major advantage of BP is strong adaptive, power fault tolerance, and high fitting accuracy This method is sensitive to the topological construction and different types and quantities of input factors may lead to different results (Yu et al 2008). The dynamic change of oil production is mainly affected by changes in liquid production, water injection rate, and reservoir water cut, which will have a lasting influence upon the whole life of petroleum recovery. Based on the analysis above, we have initially take these influence factor into consideration: the total amount of oil and water wells (x1), the number of active wells (x2), the number of new wells (x3), the injection rate of last year (x4), water cut (x5), the oil production rate (x6), recovery percent of reserves (x7), and oil production of last year (x8)

Methodology

Methods

Results and discussion

Conclusion