Spatial allocation affinities of ore reservoirs and their analysis with geostatistics

Abstract

Geostatistics and, more specifically, stochastic modelling of reservoir heterogeneities are being increasingly considered by reservoir analysts and engineers for their potential in generating more accurate reservoir models together with usable measures of spatial uncertainty. Geostatistics provides a probabilistic framework and a toolbox for data analysis with early integration of information. The uncertainty about the spatial distribution of critical reservoir parameters is modeled and trans-ferred all the way to a risk-conscious reservoir management. The stochastic imaging (modeling) algorithms allow the generation of multiple, equiprobable, unsmoothed reservoir models yet all honoring the data available.

Introduction

Numerical models of reservoirs often fails to capture the heterogeneities that are critically important for reservoir performance. With production always being the primary function of well, the available data cue typically biased toward the more productive regions in a reservoir and are regrettably sparse. The interpolation and gridding algorithms commonly used by industry further exacerbate the problem since they are low-pass filters that tend to smooth out the little spatial variability that the sparse data reveal. While core plugs and well logs are not the only sources of information, other data, such as geophysical information, are often difficult to integrate since they have different levels of reliability and are representative of very different volumes of rock. History matching on historical production information and well test data does not guarantee reliable forecasts of a reservoir's future performance.

Spatial Data Analysis

Geostatistics begins with an emphasis on describing and modelling the spatial variability of reservoir properties and the spatial correlation between related properties such as porosity and seismic velocity. These models can then be used in the construction of numerical models for a variety of purposes-interpolations for a property whose average is critically important, stochastic simulations for a property whose extremes are critically important. Whether one needs to transfer information from one reservoir to another, or between different units within the same reservoir, or whether one needs to transfer information from one discipline to another, a quantitative vehicle is necessary. Geostatistical models of spatial variability and dependence provide a quantitative summary of geological observations, and can therefore serve as a such a vehicle. Geostatistical models make it easier to compare data from different sedimentary basins, from different formations, and from different horizons, and are therefore valuable aids to any attempt at building a variables at different locations.