The Ardross Reservoir Gridblock Analog: Sedimentology, Statistical Representivity, and Flow Upscaling
Philip Ringrose, Gillian Pickup, Jerry Jensen, Margaret Forrester, 1999. "The Ardross Reservoir Gridblock Analog: Sedimentology, Statistical Representivity, and Flow Upscaling", Reservoir Characterization—Recent Advances, Richard A. Schatzinger, John F. Jordan
Download citation file:
We have used a reservoir gridblock-size outcrop (10 × 100 m) of fluvio- deltaic sandstones to evaluate the importance of internal heterogeneity for a hypothetical waterflood displacement process. Using a dataset based on probe permeameter measurements taken from two vertical transects representing “wells” (5 cm sampling) and one “core” sample (exhaustive 2-mm-spaced sampling), we evaluate the permeability variability at different lengthscales, the correlation characteristics (structure of the variogram function), and importance of volume and data support. We then relate these statistical measures to the sedimentology.
We show how the sediment architecture influences the effective tensor permeability at the lamina and bed scales, and then calculate the effective relative permeability functions for a waterflood. We compare the degree of oil recovery from the formation: (1) using averaged borehole data and no geological structure, and (2) modeling the sediment architecture of the interwell volume using mixed stochastic/deterministic methods.
We find that the sediment architecture has an important effect on flow performance, mainly due to bed-scale capillary trapping and a consequent reduction in the effective oil mobility. The predicted oil recovery differs by 18% when these small-scale effects are included in the model. Traditional reservoir engineering methods using average permeability values only prove acceptable in high-permeability and low-heterogeneity zones. The main outstanding challenge, represented by this illustration of sub-gridblock scale heterogeneity, is how to capture the relevant geological structure along with the inherent geo-statistical variability. An approach to this problem is proposed.
Figures & Tables
Optimum reservoir recovery and profitability result from guidance by an effective reservoir management plan. Success in developing the most appropriate reservoir management plan requires knowledge and consideration of (1) the reservoir system, including rocks, fluids, and rock-fluid interactions, as well as wellbores and associated equipment and surface facilities; (2) the technologies available to describe, analyze, and exploit the reservoir; and (3) the business environment under which the plan will be developed and implemented. Reservoir management plans de-optimize with time as technology and the business environment change or as new reservoir information becomes available. Reservoir characterization is the process of creating an interdisciplinary high-resolution geoscience model that incorporates, integrates, and reconciles various types of geological and engineering information from pore to basin scale. The reservoir data are then conceptually and quantitatively modeled and compared to the historical production data and fluid flow distribution patterns within and beyond the limits of the reservoir to match well production histories and predict their behavior. The goals of reservoir characterization are to simultaneously (1) maintain high displacement efficiency, (2) optimize high sweep efficiency, (3) provide reliable reservoir performance predictions, and (4) reduce risk and maximize profits. Notice that in addition to the technical concepts that we normally associate with "characterization," maximizing profits is an essential element of this process. Papers from the Fourth International Reservoir Characterization Technical Conference (1997), sponsored by the U.S. Department of Energy, this publication is a unique compilation of 27 papers covering every aspect of reservoir characterization and has been a popular AAPG publication since that time. Using an interdisciplinary approach, the papers address qualitative information as well as integrated quantified data and culminate in a fully integrated study.