seismic inversion is a method that extracts acoustic impedance data from the seismic traces. sourcewavelets are band-limited, and thus seismic traces do not contain low and high frequency information.therefore, there is a serious problem when the deterministic seismic inversion is applied to real dataand the result of deterministic inversion is smooth. low frequency component is obtained from w...

This paper presents an overview of geostatistical simulation with particular focus on aspects of importance to its application for quantification of risk in the mining industry. Geostatistical simulation is a spatial extension of the concept of Monte Carlo simulation. In addition to reproducing the data histogram, geostatistical simulations also honour the spatial variability of data, usually c...

This paper describes one solution to the problem of constraining geostatistical models by well-test results which has been obtained within the frame of the HELIOS research project conduced by Elf and IFP in the field of reservoir engineering. To do so, a numerical simulation program devoted to the simulation of well-tests has been coupled with a nonlinear constrained optimization program to mak...

Seismic inversion is a method that extracts acoustic impedance data from the seismic traces. Source wavelets are band-limited, and thus seismic traces do not contain low and high frequency information. Therefore, there is a serious problem when the deterministic seismic inversion is applied to real data and the result of deterministic inversion is smooth. Low frequency component is obtained fro...

Due to the existence of a constant sum of constraints, the geochemical data is presented as the compositional data that has a closed number system. A closed number system is a dataset that includes several variables. The summation value of variables is constant, being equal to one. By calculating the correlation coefficient of a closed number system and comparing it with an open number system, ...

Inverse problems in geophysics require the introduction of complex a priori information and are solved using computationally expensive Monte Carlo techniques where large portions of the model space are explored . The geostatistical method allows for fast integration of complex a priori information in the form of covariance functions and training images. We combine geostatistical methods and inv...

among the usual interpolation methods, kriging and co-kriging are frequently used in the interpolation of precipitation data as one the best linear unbiased estimators, despite these advantages, there models show smoothness representation and because they are based on regional averages of the data, they predict maximum and minimum values   lower and higher than real values respectively. therefo...

A new methodology has been developed to design optimal monitoring network to estimate the transient pollution plume resulting from active pollution sources in a contaminated aquifer. An optimization algorithm is linked with geostatistical kriging model as well as a numerical simulation model. Simulated annealing is used as the optimization tool. The physical process in the aquifer, i.e., the fl...

In this article objective have been made to reviews different geostatistical methods available to estimate and simulate petrophysical properties (porosity and permeability) of the reservoir. Different geostatistical techniques that allow the combination of hard and soft data are taken into account and one refers the main reason to use the geostatistical simulation rather than estimation. Uncert...

The paper presents some contemporary approaches to the spatial environmental data analysis, processing and presentation. The main topics are concentrated on the decision–oriented problems of environmental and pollution spatial data mining and modelling: valorisation and representativity of data with the help of exploratory data analysis, topological, statistical and fractal measures of monitori...