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• W4313494105 abstract "Abstract This study proposes the use of different machine learning techniques to predict the estimated ultimate recovery (EUR) as a function of the hydraulic fracturing design. A set of data includes 200 well production data, and completion designs were collected from oil production wells in the Niobrara shale formation. The completion design parameters include the lateral length, the number of stages, the total injected proppant and slurry volumes, and the maximum treating pressure measured during the fracturing operations. The data set was randomly split into training and testing with a ratio of 75:25. Different machine learning methods were to predict EUR from the completion design including linear regression, random forest (RF), and decision tree (DT) in addition to gradient boosting regression (GBR). EUR prediction from the completion data showed a low accuracy. As result, an intermediate step of estimating the well IP30 (the initial well production rate for the first month) from the completion data was carried out; then, the IP30 and the completion design were used as input parameters to predict the EUR. The linear regression showed some linear relationship between the output and the inputs, where the EUR can be predicted with a linear relationship with an R -value of 0.84. In addition, a linear correlation was developed based on the linear regression model. Moreover, the other ML tools including RF, DT, and GBR presented high accuracy of EUR prediction with correlation coefficient ( R ) values between actual and predicted EUR from the ML model higher than 0.9. This study provides ML application with an empirical correlation to predict the EUR from the completion design parameters at an early time without the need for complex numerical simulation analysis. Unlike the available empirical DCA models that require several months of production to build a sound prediction of EUR, the main advantage of the developed models in this study is that it requires only an initial flow rate along with the completion design to predict EUR with high certainty." @default.
• W4313494105 created "2023-01-06" @default.
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• W4313494105 date "2023-01-04" @default.
• W4313494105 modified "2023-09-30" @default.
• W4313494105 title "RETRACTED ARTICLE: Data-driven EUR for multistage hydraulically fractured wells in shale formation using different machine learning methods" @default.
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• W4313494105 doi "https://doi.org/10.1007/s13202-022-01602-1" @default.
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