A Geostatistical Model for Calcite Concretions in Sandstone
ABSTRACT 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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ABSTRACT: Petrographic study of the Frewens sandstone, Upper Cretaceous Frontier Formation, documents reservoir-scale diagenetic heterogeneity. Iron-bearing calcite cement occurs as large concretions that generally follow bedding and are most common near the top of the sandstone. Median thickness of the concretions is 0.6 m, length 4.5 m, and width 5.7 m; median volume is 5.2 m3. Concretions comprise 12% of the sandstone. The minus-cement porosity of concretion samples is low, indicating that the calcite precipitated near maximum burial depth. Isotopic and burial history data suggest that the calcite precipitated at ~548C from evolved meteoric water enriched in 18O or from a mixed meteoric-marine pore-water. Shell-bearing transgressive shales above the Frewens sandstone are interpreted to be the source of calcium carbonate. Concretions of this size and distribution would influence fluid flow in a reservoir and would reduce the amount of hydrocarbons in place.Clay Minerals - CLAY MINER. 01/2000; 35(1):95-95.
- Journal of Sedimentary Research - J SEDIMENT RES. 01/2000; 70(4):788-802.
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ABSTRACT: Interpretation of geophysical data or other indirect measurements provides large-scale soft secondary data for modeling hard primary data variables. Calibration allows such soft data to be expressed as prior probability distributions of nonlinear block averages of the primary variable; poorer quality soft data leads to prior distributions with large variance, better quality soft data leads to prior distributions with low variance. Another important feature of most soft data is that the quality is spatially variable; soft data may be very good in some areas while poorer in other areas. The main aim of this paper is to propose a new method of integrating such soft data, which is large-scale and has locally variable precision. The technique of simulated annealing is used to construct stochastic realizations that reflect the uncertainty in the soft data. This is done by constraining the cumulative probability values of the block average values to follow a specified distribution. These probability values are determined by the local soft prior distribution and a nonlinear average of the small-scale simulated values within the block, which are all known. For each realization to accurately capture the information contained in the soft data distributions, we show that the probability values should be uniformly distributed between 0 and 1. An objective function is then proposed for a simulated annealing based approach to enforce this uniform probability constraint. The theoretical justification of this approach is discussed, implementation details are considered, and an example is presented.Mathematical Geology 12/1999; 32(1):49-67. · 1.05 Impact Factor