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Uncertainty quantification in the Permian Basin using conventional and modified bootstrap methodology

Petroleum Research 2023年第4期8卷 439-454页

作者： Chukwuemeka o.okoli Scott D.Goddard obadare o.awoleke Department of Petroleum Engineering University of Alaska FairbanksFairbanks99775AlaskaUSA National Energy Technology Laboratory 1450 Queen Avenue SWAlbany97321OregonUSA Department of Mathematics&Statistics University of Alaska FairbanksFairbanks99775AlaskaUSA

Various uncertainty quantification methodologies are presented using a combination of several deter-ministic decline curve analysis models and two bootstrapping *** probabilistic models are applied to 126 sample wells from the Permian *** are presented for 12-72 months of pro-duction hindcast given an average well production history of 103 *** on the coverage rate and the forecast error(with the coverage rate being more significant in our choice of the best probabilistic models)and using up to one-half of the available production history for a group of sample wells from the Permian Basin,we find that the CBM-SEPD combination is the best probabilistic model for the Central Basin Platform,the MBM-Arps combination is the best probabilistic model for the Delaware Basin,the CBM-Arps is the best probabilistic model for the Midland Basin,and the best probabilistic model for the overall Permian Basin is the CBM-Arps when early time data is used as hindcast and CBM-SEPD for when one-quarter to one-half of the data is used as *** three-quarters or more of the available production history is used for analysis,the MBM-SEPD probabilistic model is the best combination in terms of both coverage rate and forecast error for all the sub-basins in the *** novelty of this work lies in its extension of bootstrapping methods to other decline curve analysis *** work also offers the engineer guidance on the best choice of probabilistic model whilst attempting to forecast production from the Permian Basin.

关键词： Uncertainty quantification Machine learning Bootstrapping Reservoir forecasting Frequentist probabilistic models

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