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| Machine Learning based prediction methods on shale gas recovery |
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Abstract Shale gas is a kind of unconventional natural gas which exists in mud shale with adsorption, free and fluid phase. The proved reserves in China are abundant and widely distributed, and the buried depth is below 3000 meters. The key technologies of shale gas production are horizontal well and hydraulic fracturing, while the more difficult and challenge for efficient production is to predict recovery. If the recovery can be predicted, we can evaluate the degree of reservoir reconstruction, and know the direct effect of current construction parameters on gas production. In recent years, with the rise of deep learning, it has become a popular method to solve engineering problems. In this paper, three machine learning methods, DNN, SVM and Xgboost, are used to establish the prediction models from reservoir and construction parameters to recovery factor by analyzing data of horizontal wells in shale gas production in the Fuling District area. The advantages and disadvantages of various models are analyzed, and the importance of relevant parameters is also discussed. Under the condition of small data in shale gas construction, the reasonable recovery prediction models are set up, which has the prospect of engineering application.
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Received: 24 January 2021
Published: 17 June 2021
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2021, Vol. 42 
