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Grzegorzewski, M. Gagolewsi, O. Hryniewicz, M. On Comparison of Distorted Histograms. Lepskiy A.

In bk. The fuzzy representation of prior information for separating outliers in statistical experiments. Bronevich A. Research target: Mathematics Computer Science. Priority areas: IT and mathematics. Language: English. Text on another site. Keywords: computational intelligence intelligent data analysis soft computing. Vishnevskiy K. Science, Technology and Innovation. WP BRP. In the last few years the world oil and gas industry has experienced a rapid development growing more strongly than many other industry branches. Modern oil and gas industry aims at the extraction of natural resources at an increasing scale.

The growth of oil production is conditional upon developing new exploration fields to create auspicious investment conditions, stabilize national social and political life using and implementing state-of-the-art technologies. It is efficient and vital for oil and gas companies today to contract different companies and their competences and resources in the development of fields, oil and gas extraction, transport and refining.

It allows incorporating cutting-edge know-how in extracting natural resources by means of implementing new scientific and technological solutions aimed at further leveraging profitability based on inter-company cooperation thus opening opportunities for economic and social development and improvement but also environmental protection and quality of life.

The complicated nature of organizing tenders requires creating new means and instruments which are designed to improve the choice efficiency and reduce the term of decision making. As evidenced by world experience from other industries the most prospective decision in this field are made using Intelligent Data Systems.

This article deals with structure of intelligent information systems aiding decision-making using the case of an electronic tender competition. Edited by: A. Averkin , D. Ignatov , S.

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Mitra et al. Mikheenkova M. Automatic Documentation and Mathematical Linguistics. Dynamics of Information Systems: Mathematical Foundations. Short lists with short programs in short time. Bauwens B. Electronic Colloquium on Computational Complexity. Technical report.

Hasso-Plattner-Institut, Laminations from the Main Cubioid. Timorin V. Cornell University, Goncharuk N. The new proof needs no estimates on integrals, provides thinner exceptional set for quadratic vector fields, and provides limit cycles that stay in a bounded domain. The conference materials. Edited by: V. Nepomniaschy , V. Neural vector quantization for geobody detection and static multivariate upscaling A. High resolution reservoir heterogeneity characterization using recognition technology M. Hassibi, I.

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Ershaghi, F. Extending the use of linguistic petrographical descriptions to characterise core porosity T. Gedeon, P. Wong et al. Part 4. Reservoir and Production Engineering. Using genetic algorithms for reservoir characterisation C. Romero, J. Applying soft computing methods to improve the computational tractability of a subsurface simulation-optimization problem V. Johnson, L.

Neural network prediction of permeability in the El Garia formation, Ashtart oilfield, offshore Tunisia J. Ligtenberg, A. Using RBF network to model the reservoir fluid behavior of black oil systems A. Enhancing gas storage wells deliverability using intelligent systems S. Part 5.

Integrated field studies. Soft computing: Tools for intelligent reservoir characterization and optimum well placement M. Nikravesh, R. Adams, R. Combining geological information with seismic and production data J. Caers, S. Interpreting biostratigraphical data using fuzzy logic: The identification of regional mudstones within the Fleming field, UK North Sea M. Wakefield, R.

Cook et al. Geostatistical characterization of the Carpinteria field, California R. Pawar, E. Edwards, E. Integrated fractured reservoir characterization using neural networks and fuzzy logic: Three case studies A.

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Zellou, A. Part 6. General Applications. Virtual magnetic resonance logs, a low cost reservoir description tool S. Artificial neural networks linked to GIS Y. Yang, M. Intelligent computing techniques for complex systems M. Multivariate statistical techniques including PCA and rule based systems for well log correlation J.

Author Index. Subject Index. This comprehensive book highlights soft computing and geostatistics applications in hydrocarbon exploration and production, combining practical and theoretical aspects. It spans a wide spectrum of applications in the oil industry, crossing many discipline boundaries such as geophysics, geology, petrophysics and reservoir engineering.

Soft Computing and Intelligent Data Analysis in Oil Exploration: Volume 51

It is complemented by several tutorial chapters on fuzzy logic, neural networks and genetic algorithms and geostatistics to introduce these concepts to the uninitiated. The application areas include prediction of reservoir properties porosity, sand thickness, lithology, fluid , seismic processing, seismic and bio stratigraphy, time lapse seismic and core analysis.

There is a good balance between introducing soft computing and geostatistics methodologies that are not routinely used in the petroleum industry and various applications areas. The book can be used by many practitioners such as processing geophysicists, seismic interpreters, geologists, reservoir engineers, petrophysicist, geostatistians, asset mangers and technology application professionals. Nelson qu I heartily congratulate them on the outstanding job they have done in putting this book together. We are always looking for ways to improve customer experience on Elsevier. We would like to ask you for a moment of your time to fill in a short questionnaire, at the end of your visit.

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