Hierarchical clustering of Pristane and Phytane data to identify the sweet spots in the shale reservoirs

Abstract

Shale plays have gained prominence as unconventional hydrocarbon reservoirs in the last two decades. The prime aim of most of the petroleum operators is to extract hydrocarbon in a timely and cost-effective manner. For this, operators carry out extensive technical analysis of shale plays to find the sweet spots. Technical studies behind sweet spot mapping often require data like core data, wireline logs and seismic data which involves more capital expenditure. During limited availability of such data, low-cost information like fluid geochemistry can be used to identify sweet spots. Many authors have used Pristane (Pr), Phytane (Ph) and normal alkane (nC17, nC18) to determine the depositional environment, maturity and the kerogen type of the source rock in the conventional hydrocarbon system. This data set combination has rarely been used for sweet spot mapping in shale plays. In this study, an attempt was made to integrate data analytics and fluid geochemical data (mainly Ph/nC18 and Pr/nC17) to map the sweet spots within a producing shale play of North America, i.e. the Eagle Ford Shale.Fluid geochemistry data of 77 wells were used and with the help of hierarchical clustering all the data were segregated into 3 groups and integrated with the present and the paleo-structure maps to identify the sweet spots. Finally, the sweet and non-sweet spots were validated with the help of a few other datasets like Th/U ratio, core and log TOC and the actual production data.This study helped in demarcating the geographical distribution of the sweet and non-sweet spots within the study area of the Eagle Ford Shale. It has also explained the usefulness of fluid geochemical data and hierarchical clustering as a tool for the identification of sweet spots in the absence of an adequate amount of wireline logs, core and seismic data.