Equivalent Circulating Density Prediction Using a Hybrid ARIMA and BP Neural Network Model
- Chao Han (China University of Petroleum East China) | Zhichuan Guan (China University of Petroleum East China) | Jingjiao Li (China University of Petroleum East China) | Huaigang Hu (China University of Petroleum East China) | Yuqiang Xu (China University of Petroleum East China)
- Document ID
- Society of Petroleum Engineers
- Abu Dhabi International Petroleum Exhibition & Conference, 11-14 November, Abu Dhabi, UAE
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- Equivalent Circulating Density, ARIMA, Data Mining, BP neural network, Hybrid Methodology
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- 27 since 2007
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A hybrid methodology is presented to predict equivalent circulating density (ECD), which combines autoregressive integrated moving average (ARIMA) and back propagation neural network (BPNN) models. Research results are compared to previously published ECD prediction method that is based on theoretical calculation of hydraulic parameters.
The hybrid methodology is based on data analysis theory. It uses ARIMA model to capture the linear trends of ECD, and then the BP neural network is used to predict the nonlinear and stochastic change law of ECD. Finally BP neural network prediction results are used to correct the prediction error of ARIMA to get the ECD prediction results. With a deepwater well in the South China Sea, a simulation experiment is carried out to verify the comprehensiveness and accuracy of the method presented in this paper.
The prediction results are similar to those of the traditional hydraulics model, which considers the effect of temperature and pressure on drilling fluid density and rheological parameters. Comparisons are also provided for three classical time series prediction models, including the support vector machine, multiple linear regression and grey prediction. The root mean square error (RMSE) and the mean absolute deviation (MAD) were selected as evaluation indicators. The comparison results show that the hybrid model can reflect the variation law of ECD more accurately. Therefore, relative to the traditional methods of prediction, the hybrid methodology has the advantages of advanced modeling thought and simple operation, and it can be selected for prediction of ECD.
Because of the effects of high temperature, high pressure and uncertain factors, the accurate prediction of ECD is very difficult. This paper provides a novel idea for accurately predicting ECD by analyzing the implicit relationship of ECD series data through data mining.
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