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’.
Compositional data arise naturally in several branches of science, including geology. In geochemistry, for example, these constrained data seem to occur typically, when one normalizes raw data or when one obtains the output from a constrained estimation procedure, such as parts per one, percentages, ppm, ppb, molar concentRations, etc.
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 <...>
This book is aimed at postgraduates, undergraduates and workers in industry who require an introduction to geostatistics. It is based on seven years of courses to undergraduates, M.Sc. students and short courses to industry, and reflects the problems which have been encountered in presenting this material to mining engineers and geologists over a wide age range, and with an equally wide range of numerical ability. The book would provide the foundation of a course of about 20 to 30 hours, or of a five-day short course.
This text intends to be a technical one. This means that techniques to solve identified problems will be presented. As the theory which serves as a basis for these techniques is very new, and relatively unfamiliar to the mineral industry, several chapters or sections will be devoted to it. These two ideas of a technique and a theory have been my guideline in preparing this course on the geostatistical estimation of mineral resources. The main target was to stay, as much as possible, close to the practical problems. This is the reason for the many examples which are intermeshed with the text; however, in many cases, staying t o o close t o a problem obscures the broader frame into which a question has to be asked before finding a correct answer. This is the reason for some theoretical digressions, which may seem to some as an attempt to try and make things look complicated. Certainly, in a particular mine, many problems can be solved without a total understanding of the complete theory. On the other hand, when one considers all the problems occurring in different mines, one cannot hope to solve them without having a good grasp, a synthetic view of the theory of regionalized variables as developed by G. Matheron in France, the most advanced developments of which have just been published in the Proceedings of a N.A.T.O. Advanced Study Institute (Guarascio, Huijbregts, David, 1976) <...>
The distribution of ore grades within a deposit is of mixed character, being partly structured and partly random. On one hand, the mineralizing process has an overall structure and follows certain laws, either geological or metallogenic; in particular, zones of rich and poor grades always exist, and this is possible only if the variability of grades possesses a certain degree of continuity. Depending upon the type of ore deposit, this degree of continuity will be more or less marked, but it will always exist; mining engineers can indeed be thankful for this fact because, otherwise, no local estimation and, consequently, no selection would be possible. However, even though mineralization is never so chaotic as to preclude all forms of forecasting, it is never regular enough to allow the use of a deterministic forecasting technique . This is why a scientific (at least, simply realistic) estimation must necessarily take into account both features - structure and randomness inherent in any deposit. Since geologists stress the first of these two aspects, and statisticians stress the second, I proposed, over 15 years ago, the name geostatistics to designate the field which synthetizes these two features and opens the way to the solution of problems of evaluation of mining deposits <...>
Proceedings of the seventh European conference on geostatistics for environmental applications / Материалы Седьмой Европейской конференции по геостатистике для применения в окружающей среде
Characterising spatial and temporal variation in environmental properties, generatingmaps from sparse samples, and quantifying uncertainties in the maps, are key concerns across the environmental sciences. The body of tools known as geostatistics offers a powerful means of addressing these and related questions. This volume presents recent research in methodological developments in geostatistics and in a variety of specific environmental application areas including soil science, climatology, pollution, health, wildlife mapping, fisheries and remote sensing, amongst others.
