Published 1994 as part of Computer Applications 3
Copyright © 1994 The American Association of Petroleum Geologists.  All Rights Reserved.

The chapter by T. Høye et al. details the use of seismic reflectors (top, base, and intrareservoir reflectors) into stochastic models to aid in constructing a structural model of the reservoir. Their objective was to highlight a target well location and assess uncertainty in the modeled results. Depth conversions were aided by modeling surface velocity as a Gaussian random field conditioned on the well locations. Follow their procedures in Stochastic Modeling of Troll West with Special Emphasis on the Thin Oil Zone.

S. A. McKenna and E. P. Poeter illustrate the use of an indicator simulation technique to incorporate soft (imprecise) data into a simulation of facies to help reduce model uncertainty. The soft data used in their study are synthetic data (real data plus noise). Their chapter, Simulating Geological Uncertainty with Imprecise Data for Goundwater Flow and Advective Transport Modeling, is applicable to the petroleum industry.

In Fractal Methods for Fracture Characterization, T. A. Hewett illustrates the concept of fractals and self-similarity and scaling laws using fracture networks. He points out that the most useful fractal sets for describing natural property distributions are those displaying self-similarity.

Description of Reservoir Properties Using Fractals, by M. Kelkar and S. Shibli, rounds out the discussion of fractals with a case study. This work investigates the utility of fractal geometry for describing the spatial correlation structure of rock properties in carbonate and clastic rock types.

A. S. Almeida and P. Frykman obtained stochastic images of porosity and permeability in a Maastrichtian North Sea chalk reservoir. In Geostatistical Modeling of Chalk Reservoir Properties in the Dan Field, Danish North Sea, they use a Gaussian collocated cosimulation algorithm, built on a Markov-type hypothesis, to perform direct cosimulation of spatially interrelated variables. Log-derived porosity was used as soft data during the cosimulation.

Innovative modeling techniques were combined to generate a realistic characterization of a complex eolian depositional environment for a multicompany reservoir management problem. D. L. Cox et al. first model the eolian bedding geometries and dimensions of four different stratification types. Permeability fields were generated within the conditional simulation of reservoir model, scaled-up for flow simulation and compared to historical field production data in Integrated Modeling for Optimum Management of a Giant Gas Condensate Reservoir, Jurassic Eolian Nugget Sandstone, Anschutz Ranch East Field, Utah Overthrust (USA).

C. J. Murray uses a variety of methods, including cluster analysis, discriminant function analysis, and sequential indicator simulation (SIS) to identify and model petrophysical rock types. Simulated annealing was used to postprocess the SIS images such that the images honor rock type transitional frequencies in well data. Follow this process in Identification and 3-D Modeling of Petrophysical Rock Types.

Uncertainty is a statistical term used to describe what we do not know. The results of a geostatistical model include a number of possible outcomes, each equally likely, and each physically portraying, in a variety of ways, the portion of the model we do not know. Thus, for example, a channel may vary in size, shape, and location from outcome to outcome in an area where there is no hard data. Tracking these variations and developing a sense of how variable the model is can be very important. This section's final chapter, by R. M. Srivastava, nicely illustrates the topic of visualizing uncertainty. In The Visualization of Spatial Uncertainty, Srivastava demonstrates a unique way of looking at the various possible outcomes of a given geostatistical model.