Many geostatistical variables have sample distributions that are highly positively skewed. Because of this, significant deskewing of the histogram and reduction of variance occurs when going from sample to block support, where blocks are of larger volume than samples. When making estimates in both mining and non-mining applications we often wish to map the spatial distribution on the basis of block support rather than sample support.

From its inception as a separate discipline, geostatis tics sought recognition from practitioners, not from math ematicians or physicists. and rightfully so. Indeed, the theory was essentially established by the 1950's by Kol mogorov and Wiener and exposed by Matern (1960), Whit tle (1963), and Matheron (1965), among others. But there is a long, hard way between a concept expressed by matrix notations in a Hilbert space and its implementation and routine application. It is my opinion that the main con tribution of geostatistics has been and still is implementa. tion, an essential follow-up step much too often forsaken by theoreticians. <...>

Estimating mineral resources from drill hole data is an activity that is fraught with difficulty. Most classical statisticians would regard the data for  any ore reserve estimate as dangerously inadequate. This would often apply even in cases where the geologist felt that the deposit had been "overdrilled". <...>

The discipline which is now known as geostatistics began to develope over thirty years ago for mining evaluation and since has extended to other fields of activity. Around 1960 in particular, G. Matheron built linear geostatistics. Some of its tools (variogram, kriging) are widely used nowadays. Linear geostatistics makes it possible for instance to evaluate the metal content of a mining block or panel by estimating the mean of the grades of the points in it from samples. The reader of this book is supposed to be familiar with linear geostatistics. <...>

This introductory chapter presents a discrete approach specially designed for modeling the geometry and the properties of natural objects such as those encountered in biology and geology.

С каждым годом в мире становится все меньше неразведанных участков и месторождений нефти и газа, и в таких условиях для увеличения добычи или ее поддержания нефтяные компании переходят к разработке объектов в сложных геологических условиях. К ним относятся и нетрадиционные коллекторы: сланцевые нефть и газ, нефтематеринские толщи, содержащие уже созревшие углеводороды (УВ), но еще не мигрировавшие, низкопроницаемые плотные породы. Эти залежи не контролируются структурными, стратиграфическими, литологическими и прочими традиционными факторами. [47] Кроме того, к сложным геологическим условиям, безусловно, относятся и тектонически экранированные ловушки, и залежи в глубоководных акваториях, и многие другие.

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 characterised by a variogram model. If the simulations also honour the data themselves, they are said to be ‘conditional simulations’.

Большинство месторождений нефти, газа и газового конденсата Западно-Сибирской нефтегазоносной провинции (НГП) находятся на завершающих стадиях разработки, с неуклонным истощением традиционных запасов углеводородов (УВ) и снижением темпов роста нефтегазодобычи. Основные эксплуатационные объекты характеризуются высокой степенью вовлеченности в разработку (78%), выработанностью (33%) залежей разного фазового состава и значительным обводнением продукции скважин.

GSLIB is the name of a directory containing the geostatistical software developed at Stanford. Generations of graduate students have contributed to

this constantly changing collection of programs, ideas, and utilities. Some of the :nost widely used public-domain geostatistical software [58, 62, 721 and many more in-house programs were initiated from GSLIB. It was decided to open the GSLIB directory to a wide audience, providing the source code to seed new developments, custom-made application tools, and, it is hoped, new theoretical advances <...>

1. Geostatistics for Environmental and Geotechnical Applications: A Technology Transferred

2. Describing Spatial Variability Using Geostatistical Analysis

3. Geostatistical Estimation: Kriging

4. Modeling Spatial Variability Using Geostatistical Simulation-

5. Geostatistical Site Characterization of Hydraulic Head and Uranium Concentration in Groundwater

6. Integrating Geophysical Data for Mapping the Contamination of Industrial Sites by Polycyclic Aromatic Hydrocarbons: A Geostatistical Approach

7. Effective Use of Field Screening Techniques in Environmental Investigations: A Multivariate Geostatistical Approach