Controls on Fluid Flow and Hydrocarbon Recovery in a Clinoform-Bearing, Fluvial-Dominated Deltaic Reservoir Analog: Ferron Sandstone, Utah
Graham, Gavin H.; Jackson, Matthew D.; Hampson, Gary J.
Fluvial-dominated deltaic parasequences contain paleoseaward-dipping clinoform surfaces that control aspects of stratigraphic and facies architecture. Permeability contrasts associated with clinoforms have been identified as an important control on fluid flow and hydrocarbon recovery. The aim of this study is to quantify the impact of clinoforms on fluid flow in the context of: (1) other uncertain reservoir parameters, such as permeability contrasts between facies associations, and the impact of bed-scale heterogeneity on vertical permeability, and (2) reservoir engineering decisions, including well locations and production rate. We also demonstrate the use of a stochastic modeling algorithm that can rapidly populate existing reservoir models with numerous clinoform surfaces.
We have built a range of three-dimensional reservoir models, using outcrop data from the fluvial-dominated deltaic deposits of the Upper Cretaceous Ferron Sandstone Member, Utah and a novel, surface-based approach which incorporates the numerically modeled clinoform surfaces and yields reservoir-scale models suitable for flow simulation without upscaling. Within clinothems, permeability contrasts between facies associations control sweep efficiency. Sweep efficiency is reduced when water flooding is down depositional dip as oil is bypassed at the toe of each clinothem. Hydrocarbon recovery is higher when produced at lower rate for a longer period as gravity-driven, downward flow sweeps the poorer quality facies within each clinothem. However, the most significant influence on hydrocarbon recovery is the presence of barriers to flow associated with clinoforms; equivalent models that neglect clinoforms over-predict recovery by up to 30%. In the absence of such barriers, clinoforms have negligible impact on recovery.
Our results demonstrate that clinoforms can have a significant impact on flow and hydrocarbon recovery, and their impact is often larger than that of other uncertain reservoir parameters. Yet clinoform surfaces are typically neglected in subsurface reservoir models, partly because they are difficult to characterize in the subsurface, and partly because the modeling tools required to capture them are not available. The numerical algorithm we demonstrate here can be used to populate subsurface models with stochastically distributed clinoform surfaces, using a combination of subsurface and outcrop analogue data.
AAPG Search and Discovery Article #90163©2013AAPG 2013 Annual Convention and Exhibition, Pittsburgh, Pennsylvania, May 19-22, 2013