The Effect of Geologic Parameters and Uncertainties on Subsurface Flow: Deepwater Depositional Systems

Abstract

Abstract The application of reservoir simulation as a tool for reservoir development and management is widespread in the oil and gas industry. Moreover, it is recognized that the results of any reservoir simulation model are strongly influenced by the underlying geologic model. However, the direct relationship between geologic parameters and subsurface flow is obscure. In this paper we explore this relationship in a deepwater depositional system using data from two reservoir analogs: the shallow seismic dataset from the Mahakam Fan and outcrop data from the Brushy Canyon Formation of West Texas. Shallow seismic data from the Mahakam Fan area shows a high-resolution deepwater channel-levee system consisting of 10 migrating channels. Using an experimental design framework and a series of three increasingly complex models, we investigated the effect of nine different geologic factors on several different measures of the flow behavior. Our results show that, as expected, different geologic factors influence different measures of the flow. Most significant is the clear effect that the proportion and organization of the different internal facies making up the channels have on the recovery factor and net oil production. The Brushy Canyon outcrops used in this work represent sand-rich proximal deposits of a distributary lobe complex. Here we built models on a very small length scale to investigate the effects of sheet-like reservoir architecture as well as internal facies distribution of the sheets on subsurface flow. Again, an experimental design framework was employed, this time to examine the influence of 11 input variables. The proportion and organization of the internal lobe facies has a significant influence on the subsurface flow here but in these distributary lobe complexes other variables, including the stacking of the lobes, were also found to be important. The models in this study address flow behavior in deepwater, sparse well environments. Using models from the simple to the complex, we found that several parameters incorporated in the complex models, and not in simple models, had a significant impact on the predicted flow.