Deriving Constraints on Small-Scale Variograms due to Variograms of Large-Scale Data

The application of kriging-based geostatistical algorithms to integrate large-scale seismic data calls for direct and cross variograms of the seismic variable and primary variable (e.g., porosity) at the modeling scale, which is typically much smaller than the seismic data resolution. In order to ensure positive definiteness of the cokriging matrix, a licit small-scale coregionalization model has to be built. Since there are no small-scale secondary data, an analytical method is presented to infer small-scale seismic variograms. The method is applied to estimate the 3-D porosity distribution of a West Texas oil field given seismic data and porosity data at 62 wells.     

Practical considerations in the application of simulated annealing to stochastic simulation

Realizations generated by conditional simulation techniques must honor as much data as possible to be reliable numerical models of the attribute under study. The application of optimization methods such as simulated annealing to stochastic simulation has the potential to honor more data than conventional geostatistical simulation techniques. The essential feature of this approach is the formulation of stochastic imaging as an optimization problem with some specified objective function. The data to be honored by the stochastic images are coded as components in a global objective function. This paper describes the basic algorithm and then addresses a number of practical questions: (1) what are the criteria for adding a component to the global objective function? (2) what perturbation mechanism should be employed in the annealing simulation? (3) when should the temperature be lowered in the annealing procedure? (4) how are edge/border nodes handled? (5) how are local conditioning data handled? and (6) how are multiple components weighted in the global objective function?     

Geostatistical simulation of spatial variability of convective storms in Mexico City Valley

Precipitation is one of the main components of the hydrological cycle and knowledge of its spatial distribution is fundamental for the prediction of other closely related environmental variables, for example, runoff, flooding and aquifer recharge. Most of the precipitation in Mexico City is due to convective storms characterized by a high spatial variability, implying that modeling its behavior is very complex. In this work stochastic simulation techniques with a geostatistical approach were applied to model the spatial variability of the rainfall of three convective storms. The analysis of the results shows that using the proposed methodology spatial distributions of rain are obtained that reproduce the statistical characteristics present in the available information.