Time Series Modeling for U.S. Natural Gas Forecasting

Al-Fattah, Saud Mohammed

doi:10.2523/10592-ms

Cited by 9 publications

(4 citation statements)

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“…MSE can evaluate the differences between actual values (y true ) and predicted values (y pred ), represented as Equation (13), which can be used to measure the overall performance of the trained model and help reduce forecasting error. Compared with MSE, RMSE represents the average error of a single sample, which helps to understand the physical significance, as defined in Equation (14). MAE gives a direct measure of the difference between predicted outcomes and true values, as defined in Equation (15).…”

Section: Evaluation Indicesmentioning

confidence: 99%

Application of Gated Recurrent Unit (GRU) Neural Network for Smart Batch Production Prediction

Xiao

et al. 2020

Energies

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Production prediction plays an important role in decision making, development planning, and economic evaluation during the exploration and development period. However, applying traditional methods for production forecasting of newly developed wells in the conglomerate reservoir is restricted by limited historical data, complex fracture propagation, and frequent operational changes. This study proposed a Gated Recurrent Unit (GRU) neural network-based model to achieve batch production forecasting in M conglomerate reservoir of China, which tackles the limitations of traditional decline curve analysis and conventional time-series prediction methods. The model is trained by four features of production rate, tubing pressure (TP), choke size (CS), and shut-in period (SI) from 70 multistage hydraulic fractured horizontal wells. Firstly, a comprehensive data preprocessing is implemented, including excluding unfit wells, data screening, feature selection, partitioning data set, z-score normalization, and format conversion. Then, the four-feature model is compared with the model considering production only, and it is found that with frequent oilfield operations changes, the four-feature model could accurately capture the complex variance pattern of production rate. Further, Random Forest (RF) is employed to optimize the prediction results of GRU. For a fair evaluation, the performance of the proposed model is compared with that of simple Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) neural network. The results show that the proposed approach outperforms the others in prediction accuracy and generalization ability. It is worth mentioning that under the guidance of continuous learning, the GRU model can be updated as soon as more wells become available.

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Section: Evaluation Indicesmentioning

confidence: 99%

Application of Gated Recurrent Unit (GRU) Neural Network for Smart Batch Production Prediction

Xiao

et al. 2020

Energies

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“…Using the data from 1970 to 2007, they made a prediction study using a regression model. [16] analyzed the results based on historical data using the time series model for the US natural gas production forecast.…”

Section: Literature Surveymentioning

confidence: 99%

Estimation of Turkey’s Natural Gas Consumption by Machine Learning Techniques

Erdem¹,

Kesen²

2020

Gazi University Journal of Science

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“…Based on a monthly data, the ARIMA model was used with separate autoregressive (AR) models to estimate both heating and non-heating months to capture the seasonal patterns (Aras and Aras, 2004). In his research, Al-Fattah (2006) adopted the use of ARIMA models to predict the USA natural gas production and annual depletion.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting residential natural gas consumption in Egypt

Ismail

El-Metaal

2020

JHASS

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Purpose This paper aims to obtain accurate forecasts of the hourly residential natural gas consumption, in Egypt, taken into consideration the volatile multiple seasonal nature of the gas series. This matter helps in both minimizing the cost of energy and maintaining the reliability of the Egyptian power system as well. Design/methodology/approach Double seasonal autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity model is used to obtain accurate forecasts of the hourly Egyptian gas consumption series. This model captures both daily and weekly seasonal patterns apparent in the series as well as the volatility of the series. Findings Using the mean absolute percentage error to check the forecasting accuracy of the model, it is proved that the produced outcomes are accurate. Therefore, the proposed model could be recommended for forecasting the Egyptian natural gas consumption. Originality/value The contribution of this research lies in the ingenuity of using time series models that accommodate both daily and weekly seasonal patterns, which have not been taken into consideration before, in addition to the series volatility to forecast hourly consumption of natural gas in Egypt.

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Time Series Modeling for U.S. Natural Gas Forecasting

Cited by 9 publications

References 0 publications

Application of Gated Recurrent Unit (GRU) Neural Network for Smart Batch Production Prediction

Application of Gated Recurrent Unit (GRU) Neural Network for Smart Batch Production Prediction

Estimation of Turkey’s Natural Gas Consumption by Machine Learning Techniques

Forecasting residential natural gas consumption in Egypt

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