Dissertation > Industrial Technology > Oil and gas industry > Oil machinery and equipment and automation > Oil and gas storage and transportation machinery and equipment > Oil and gas database, oil and gas tank

Jintan gas storage volume forecasting and seasonal peak shaving of gas pipeline operation simulation

Author SongHuanHuan
Tutor HuangKun
School Southwest Petroleum University
Course Oil and Gas Storage and Transportation Engineering
Keywords underground gas storage seasonal peaking amount forecast Extreme LearningMachine pipeline network simulation
Type Master's thesis
Year 2013
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In recent years, with the rapid development of natural gas industry, the unbalance of the supply and consumption becomes increasingly prominent. In order to relieve the seasonal unbalance, our country has constructed several underground gas storage to solve the city gas peak-shaving problem and meet the seasonal gas supply adjusting. Due to lack of experience, the peak-shaving adjusting programs are always made by artificial estimate, which leads to lack of scientificalness. So, it is essential to predict and calculate the adjusting amounts systematically and scientifically.The forecast of city gas load should be done before calculating the peaking amount of underground gas storage. Employing the artificial neural network model, this paper proposes a new gas load calculation method suitable for medium and long-term gas load forecast-Differential Evolution Extreme Learning Machine, which is on the basis of Extreme Learning Machine principle and combined with the differential evolution algorithm. Meanwhile, three kinds of peaking prediction methods are explored, the basic steps of underground gas storage peaking calculation are summed as well.Secondly, based on the theoretical basis, the gas load forecast model for Jintan underground gas storage is established. When using MATLAB artificial neural networks toolbox for forecasting the natural gas consumption of M city, the average monthly temperature, personal GDP, gas price and other factors in the same year are all put into consideration when establishing the model. And then complete the forecasting of quarter peaking amount of gas storage. This model has a higher accuracy compared with2012historical data. Forecast results show that the city’s annual gas consumption in2013is34.56376×108m3. The maximum peaking volume appears in January and December; the peaking amount in2013will increase over the same month in2012, the largest increase being about40%.Finally, the monthly peaking amount of underground gas storage to some extent is restricted by the actual gas production condition. So, on the basis of the forecast, we apply TGNET software to simulate the gas production pipe network under the condition of monthly peaking amount. What’s more, according to2012’s peaking forecast, the increase of10%and20%and the full capacity of pipe network are used to simulate the minimum production pressure of every well, obtaining the pressure changes under different conditions, putting forward corresponding range of operating parameters to provide a reference for actual operation.

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