Based on the massive static and dynamic data of 137 fractured wells in WY shale gas block in Sichuan, China, this paper carried out the analysis of shale gas fracturing production influencing factors, production prediction model, and fracturing parameter optimization model research. Taking geological, engineering, fracturing operation, and production data of fractured wells in WY block as data set, the main control analysis method is used to construct the shale gas fracturing production influencing factors as the sample set. A production prediction model based on six machine learning (ML) algorithms including random forest (RF), back propagation (BP) neural network, support vector regression (SVR), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multivariable linear regression (LR) has been established; the evaluation results show that the XGBoost model has the best performance on this sample set. The selection method of shale gas well fracturing operation scheme set is studied; the production rate and the ratio of cost and profit (ROCP) are comprehensively considered to select the final fracturing operation scheme. Research result shows that the data-driven production prediction model and fracturing parameter optimization model can not only be used to predict the production of shale gas fracturing and optimize operation parameters but also realize the sensitivity analysis of fracturing parameters and the effect comparison of fracturing operation schemes, which has good field application value.
With a rapid growth in the shale gas industry, the development of a corresponding reservoir engineering theory was promptly needed. The decline laws for shale gas reservoirs differed dramatically from the traditional methods designed for conventional gas reservoirs. This paper summarized and compared the characteristics, assumptions and limitations of world's widely-used rate decline analysis methods for shale gas. Also, based on the field situations of gas wells, a rate decline analysis method, which is compatible with shale gas production, was presented. The method was validated by comparing its results with production data of shale gas wells in the Sichuan Basin. The advantage of the method, which is proposed in this study, is that it is not biased toward any gas production regime or WHP. By taking into account the different production regimes, it could provide a better analysis of the rate behavior characteristics in shale gas wells.Keywords: Shale gas, Rate analysis, Wellhead pressure, Flow regime Arps (1945) presents the exponential and hyperbolic rate decline theory for oil and gas wells, which are still widely utilized in petroleum industry for decline analysis and EUR calculation. According to Arps decline theory, the rate vs time relation consists of three forms: exponential, hyperbolic and harmonic, all of which could be modelled by the following Eq.( Arps, 1945):
REVIEW OF RATE ANALYSIS METHODS FOR SHALE GASWhere, q t is defined as production rate at time t, q i is initial rate, D i is Arps' initial decline constant, b is Arps' hyperbolic decline constant.When b = 0, it is the exponential decline or constant decline as D does not change with time.When 0 < b < 1, it is the most widely-used hyperbolic decline relation in which D = + − q q (1 bDt) t i i (1/b)
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