A Geostatistical Model for Calcite Concretions in Sandstone

Department of Petroleum Engineering, Louisiana State University, Baton Rouge, Louisiana, United States
Mathematical Geology (Impact Factor: 1.05). 06/2003; 35(5):549-575. DOI: 10.1023/A:1026282602013


Diagenetic cements commonly reduce sandstone porosity and permeability and change patterns of subsurface flow in oil and gas reservoirs and aquifers. Calcite concretions were mapped on an outcrop exposure of a Cretaceous-age tide-influenced deltaic sandstone in Wyoming, USA. A digitized image of this outcrop was analyzed to compute descriptive statistics including concretion proportion and semivariograms. The concretions were modeled using indicator geostatistics. The vertical trend in concretion proportion observed in the outcrop was incorporated stochastically using a proportion curve and truncated Gaussian simulation. Realizations of concretion distributions were refined using simulated annealing and multipoint statistics to obtain statistically and geologically reasonable images. Flow simulations examined alternative models for concretion distributions, comparing flow responses of a model based on outcrop observations to models based on stochastic images. Stochastic models can predict flow responses (breakthrough time, upscaled permeability, and sweep efficiency) with mean relative errors of 0.2–6.8%. Simpler models based on power-law averages cannot predict the flow responses accurately. Because the concretions are small compared to interwell distances and seismic resolution, the methods and statistics presented in this paper can improve predictions of the effects of calcite concretions on subsurface flow behavior even where conditioning data are sparse. This geostatistical description of concretions is appropriate for analogous sandstone reservoirs with calcite cement sourced from adjacent beds.

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Available from: Christopher D. White, May 19, 2014
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    • "However, the accessibility for GPS mapping or the quality of LiDAR data of the modelled outcrop is a crucial factor and partially or poorly exposed areas cannot be studied . The third and more commonly used category focuses on stochastic simulations based on, among others, the Truncated Gaussian Simulation algorithm (TGSim) (White et al., 2003), Sequential Indicator Simulation algorithm (SISim) (Kjonsvik et al., 1994; Zappa et al., 2006; Aigner et al., 2007; Koehrer et al., 2010) or Indicator Kriging algorithm (IK) (Falivene et al., 2007; Tolosana-delgado et al., 2008). The flexibility to honour data input and the relatively short time required for simulation are two powerful advantages of this method. "
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