Reservoir Modeling Using Scale-Dependent Data

Abstract

Summary A reservoir description incorporating the precision and scale of all available data (geological, petrophysical, seismic, tracer, and well-test data) is critical for reservoir modeling. The heterogeneous variables sampled by these data are composed of several superimposed functions, each characterized by a unique spatial and temporal scale or support. For example, core, well-log, well-test, and seismic data have supports ranging from inches to thousands of feet. Current geostatistical methods map lithofacies, porosity, and permeability on a network of grid nodes called the geologic modeling cells. Pseudopoint properties that assimilate information from all available data are modeled onto model cells using one of several available conditional-simulation techniques. Some methods attempt to combine data with varying support, and data with multiple-scale support, through simple correlations. For example, one approach to incorporate geophysical data is to use a direct transform of the seismic signal to rock properties through a linear regression or crossplot. Reservoir models built using such linear correlations tend to be case-specific with little generality. This paper presents a method for identifying the impact of multiscale data (data that measure average property over multiple-flow units) on reservoir modeling. It examines the information about the reservoir system each data type carries, for example, what fraction of core scale variability is captured by well-log data. We also present a consistent method for integrating multiscale data. Through a series of numerical simulations, we show the impact of reservoir-property heterogeneity on the fluid-flow performance.