Accurate prediction of petroleum reservoir performance requires reliable models of the often complex reservoir heterogeneity. Geostatistical simulation techniques generate multiple realizations of the reservoir model, all equally likely to be drawn. Traditional to geostatistics, geological continuity is represented through the variogram. The variogram is limited in describing complex geological...

In the domain of oil exploration, geostatistical methods aim at simulating petrophysical properties in a 3D grid model of reservoir. Generally, only a small amount of cells are populated with properties. Roughly speaking, the question is: which properties to give to cell c, knowing the properties of n cells at a given distance from c? Obviously, the population of the whole reservoir must be com...

Geostatistical models often require a variogram or covariance model for kriging and krigingbased simulation. Next to the initial decision of stationarity, the choice of an appropriate variogram model is the most important decision in a geostatistical study. Common practice consists of fitting experimental variograms with a nested combination of proven models such as the spherical, exponential, ...

It is now common in the mining industry to deal with several correlated attributes, which need to be jointly simulated in order to reproduce their correlations and assess the multivariate grade risk reasonably. Approaches to multivariate simulation which remove the correlation between attributes of interest prior to simulate and then re-impose the relationship afterward have been gaining popula...

In mining projects, all uncertainties associated with a project must be considered to determine the feasibility study. Grade uncertainty is one of the major components of technical uncertainty that affects the variability of the project. Geostatistical simulation, as a reliable approach, is the most widely used method to quantify risk analysis to overcome the drawbacks of the estimation methods...

Current geostatistical simulation methods allow generating multiple realizations that honor all available data, such as hard and secondary data under certain geological scenarios (e.g. 3D training image-based models, multi-Gaussian law, Boolean models). However, it is difficult to simulate large models that honor highly nonlinear response functions (e.g. remote sensing data, geophysical data or...

We consider the problem of model selection for geospatial data. Spatial correlation is often ignored in the selection of explanatory variables, and this can influence model selection results. For example, the importance of particular explanatory variables may not be apparent when spatial correlation is ignored. To address this problem, we consider the Akaike Information Criterion (AIC) as appli...

The Shenhua Carbon Capture and Storage (CCS) project at the Shenbei Slope injection site in North Yulin is the first 100,000 tonper-year scale CCS pilot project in China with an injection operation lasting nearly 3 years. In this study, we investigate various geostatistical methods and their impact on the respective geologic models on which simulation is performed to understand the phenomena ob...