Multivariate Statistical Analysis for Resource Estimation in Unconventional Plays Application to Eagle Ford Shales
SPE Eastern Regional Meeting
Unconventional resource plays have shaken the energy industry in last decade changing the energy importexport equation especially in United States. US shale plays now contribute towards 40% of the total natural gas production (U.S. Energy Information Administration, 2012)With highly heterogeneous formations and Multi-Fractured Horizontal Wells (MFHW), resource estimation in these plays are still debatable. From sophisticated numerical simulation models to much simpler hyperbolic decline curves,
... none can be used with confidence to estimate resources in these shale plays. In this paper we have described the application of multivariate statistical techniques to generate type curves (TC's) for the Arp's decline analysis. Our study includes more than 1500 wells from different fluid type's (dry gas, lean gas, rich gas and light oil) in eagle ford shale (EFS). Data such as well logs, completion, production and geology is included in the analysis as input variables. The analysis demonstrates the application of multivariate statistical techniques such as principal component analysis (PCA), k means clustering, self-organizing maps (SOM) and multivariate regression (MVR). Our analysis show that univariate statistics is insufficient for analyzing the data due to significant amount of heterogeneity in shale reservoirs. Using univariate statistics impact of different variables such as well spacing, condensate gas ratio (CGR), perforated lateral lengths, zipper fracture etc. cannot be accounted in a holistic way. Multivariate analysis can quantify the impact of these variables and can be used as a good predictive tool for determination of Arp's parameters.