Alternatives to Decline-Curve Models for Unconventional Reservoirs: A Case for Data-Driven Discovery of Natural Laws



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Decline curve models, such as the Arps decline model and its variants, are fairly inaccurate for unconventional reservoirs. Therefore, usually hybrid models, combining early transient-flow models such as Duong and SEPD with Arps for boundary-dominated flow, are usually employed. However, in unconventional reservoirs with multiphase flow these transitions are gradual. Further, for transition period between transient flow and boundary dominated flow, there is no consensus for what is the appropriate model structure. In this study large database of available field data referring to production from unconventional reservoirs is analyzed using multivariate statistical method, such as principal component analysis (PCA). The analysis suggests that over 90% of variability in the data can be captured with only one or two latent variables. Therefore, an appropriate model structure naturally emerges from the data, thus eliminating the need to separate production into different flow-related regimes with explicit formulas for corresponding decline curves.



Data-driven modeling, Decline curve analysis, Principal component analysis, Unconventional reservoirs