Using Global Reservoir Analogs and Stochastic Modeling to Estimate Prospect Resources

Abstract

This study presents an innovative method to improve estimation of prospect resources by combining stochastic modeling (Monte Carlo simulation) and global reservoir analogs. Early in the exploration phase, estimation of prospect resources depends heavily on analogs, from which estimates of parameters such as trap fill, porosity, hydrocarbon saturation and shrinkage factor are extracted to feed a stochastic engine. Typically, these estimates are based on sparse data from one or a few nearby wells or fields, and often fail to capture wide enough ranges of uncertainty. This can be remedied by broadening the search for analogs to include tens or even hundreds of geologically-similar proven reservoirs that have been thoroughly documented and captured into a database according to globally consistent standards. Searches for geologically-similar global analogs can be based on criteria such as age, depositional environment, lithology, and hydrocarbon type, rather than on geographic proximity. Examples in this study show how distributions drawn from global analogs form a much more robust basis for the stochastic resource estimation process.

AAPG Datapages/Search and Discovery Article #90291 ©2017 AAPG Annual Convention and Exhibition, Houston, Texas, April 2-5, 2017