Computing Gas in Place in a Complex Volcanic Reservoir in China

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractGas-bearing reservoirs of the YingCheng Group in the SongLiao Basin in northeastern China are hosted in a complex assemblage of volcanic rocks. These reservoirs present a large number of interpretation challenges that have made the evaluation of gas-in-place (GIP) problematic. A fit-forpurpose workflow was developed for one accumulation in this area to provide robust GIP estimates in support of a development decision.The workflow involves a number of novel techniques developed to address the challenges presented by these reservoirs. Rock typing was conducted through integration of core descriptions with neutron capture spectroscopy, nuclear magnetic resonance logs, and borehole images using a neural network approach. These rock types, characterizing variations in chemistry and rock texture, were then propagated in a geocellular model using multivariate seismic attribute analysis and distribution rules based on volcanic analogues. Porosity and water saturation from an innovative petrophysical interpretation methodology were propagated throughout the model based on these rock types. The distribution of both rock types and petrophysical properties was performed stochastically and a range of potential GIP estimates was developed.