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1st Workshop on Intelligent Computing in the Petroleum Industry

作者： Nikravesh, M Univ Calif Berkeley Dept EECS Div Comp Sci BISC Program Berkeley CA 94720 USA

Reservoir characterization plays a crucial role in modern reservoir management. It-helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multi-disciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment;data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： reservoir characterization reservoir modeling reservoir management computational intelligence pattern recognition signal processing pattern classification

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Intelligent reservoir characterization (IRESC)

Intelligent reservoir characterization (IRESC)

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IEEE International Conference on Industrial Informatics (INDIN 2003)

作者： Nikravesh, M Hassibi, M Univ Calif Berkeley BISC Program Div Comp Sci Dept EECS Berkeley CA 94720 USA

ISBN: (纸本)0780382005

in this study, a new integrated methodology will be developed to identify the nonlinear relationship and mapping between 3-D seismic data, production log and will be applied to producing field. The method uses conventional techniques such as geostatistical and classical pattern recognition [1] in conjunction with modern techniques such as soft computing (neuro computing, fuzzy logic, genetic computing, and probabilistic reasoning) [2-4]. An important task of our research is to use clustering techniques recognize the optimal location of a new well to be drilled based on 3-D seismic data and available production log/data or other viable logs. The classification task will be accomplished in three ways;1) k-means clustering, 2) fuzzy clustering, and 3) neural network clustering to recognize the similarity cubes. Then the relationship between each cluster and production log will be recognized around the wellbore and the results will be used to reconstruct and extrapolate the production log away from the wellbore. This advanced 3-D seismic and log analysis and interpretation can be used to predict;1) mapping between production data and seismic data, 2) reservoir connectivity based on multi-attributes analysis, 3) pay zone estimation, and 4) optimum well placement.

关键词： Fuzzy logic

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Web-based bisc decision support system

Web-based BISC decision support system

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IEEE International Conference on Industrial Informatics (INDIN 2003)

作者： Serag-Eldin, G Nikravesh, M Univ Calif Berkeley BISC Program Div Comp Sci Dept EECS Berkeley CA 94720 USA

ISBN: (纸本)0780382005

Most of the existing search systems' 'software' are modeled using crisp logic and queries. We introduce fuzzy querying and ranking as a flexible toot allowing approximation where the selected objects do not need to match exactly the decision criteria resembling natural human behavior. The model consists of five major modules: the Fuzzy Search Engine, the Application Templates the User Interface, the Database and the Evolutionary Computing. The system is designed in a generic form to accommodate more diverse applications and to be delivered as stand-alone software to academia and businesses.

关键词： crisp logic fuzzy querying and ranking evolutionary computation

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bisc-decision support system and information systems in enterprise: Evolutionary computing-based optimization multi-aggregator fuzzy decision trees

BISC-decision support system and information systems in ente...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Nikravesh, M Univ Calif Berkeley BISC Program Div Comp Sci Dept EECS Berkeley CA 94720 USA

ISBN: (纸本)0780392981

Most of the existing search systems' 'software' are modeled using crisp logic and queries. We introduce fuzzy querying and ranking as a flexible tool allowing approximation where the selected objects do not need to match exactly the decision criteria resembling natural human behavior. The model consists of five major modules: the Fuzzy Search Engine, the Application Templates, the User Interface, the Database and the Evolutionary Computing. The system is designed in a generic form to accommodate more diverse applications and to be delivered as stand-alone software to academia and businesses.

关键词： Decision support systems

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Computational intelligent for reservoir management

Computational intelligent for reservoir management

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IEEE International Conference on Industrial Informatics (INDIN 2003)

作者： Nikravesh, M Univ Calif Berkeley Dept EECS Div Comp Sci BISC Program Berkeley CA 94720 USA

ISBN: (纸本)0780382005

Reservoir characterization plays a crucial role in modern reservoir management. It helps to make sound reservoir decisions and improves the asset value of the oil and gas companies. It maximizes integration of multidisciplinary data and knowledge and improves the reliability of the reservoir predictions. The ultimate product is a reservoir model with realistic tolerance for imprecision and uncertainty. Soft computing aims to exploit such a tolerance for solving practical problems. In reservoir characterization, these intelligent techniques can be used for uncertainty analysis, risk assessment, data fusion and data mining which are applicable to feature extraction from seismic attributes, well logging, reservoir mapping and engineering. The main goal is to integrate soft data such as geological data with hard data such as 3D seismic and production data to build a reservoir and stratigraphic model. While some individual methodologies (esp. neurocomputing) have gained much popularity during the past few years, the true benefit of soft computing lies on the integration of its constituent methodologies rather than use in isolation.

关键词： Risk assessment

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Concept-based search and questionnaire systems

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SOFT COMPUTING 2008年第3期12卷 301-314页

作者： Nikravesh, Masoud Univ Calif Berkeley EECS Dept BISC Program Div Comp Sci Berkeley CA 94720 USA Univ Calif Berkeley Imaging & Informat Grp LBNL Berkeley CA 94720 USA

World Wide Web search engines including Google, Yahoo and MSN have become the most heavily-used online services (including the targeted advertising), with millions of searches performed each day on unstructured sites. In this presentation, we would like to go beyond the traditional web search engines that are based on keyword search and the Semantic Web which provides a common framework that allows data to be shared and reused across application. For this reason, our view is that "Before one can use the power of web search the relevant information has to be mined through the concept-based search mechanism and logical reasoning with capability to Q&A representation rather than simple keyword search". In this paper, we will first present the state of the search engines. Then we will focus on development of a framework for reasoning and deduction in the web. A new web search model will be presented. One of the main core ideas that we will use to extend our technique is to change terms-documents-concepts (TDC) matrix into a rule-based and graph-based representation. This will allow us to evolve the traditional search engine (keyword-based search) into a concept-based search and then into Q&A model. Given TDC, we will transform each document into a rule-based model including it's equivalent graph model. Once the TDC matrix has been transformed into maximally compact concept based on graph representation and rules based on possibilistic relational universal fuzzy-type II (pertaining to composition), one can use Z(n)-compact algorithm and transform the TDC into a decision-tree and hierarchical graph that will represents a Q&A model. Finally, the concept of semantic equivalence and semantic entailment based on possibilistic relational universal fuzzy will be used as a basis for question-answering (Q&A) and inference from fuzzy premises. This will provide a foundation for approximate reasoning, language for representation of imprecise knowledge, a meaning representation lang

关键词： semantic Web fuzzy query fuzzy search PNL NeuSearch Z(n)-compact bisc-DSS

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Evolution of fuzzy logic: from intelligent systems and computation to human mind

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SOFT COMPUTING 2008年第2期12卷 207-214页

作者： Nikravesh, Masoud Univ Calif Berkeley Div Comp Sci BISC Program EECS Dept Berkeley CA 94720 USA Univ Calif Berkeley Lawrence Berkeley Lab NERSC Berkeley CA 94720 USA

Inspired by human's remarkable capability to perform a wide variety of physical and mental tasks without any measurements and computations and dissatisfied with classical logic as a tool for modeling human reasoning in an imprecise environment, Lotfi A. Zadeh developed the theory and foundation of fuzzy logic with his 1965 paper "Fuzzy sets" (Zadeh in Inf Control 8:378-53, 1965) and extended his work with his 2005 paper "Toward a generalized theory of uncertainty (GTU)-an outline" (Zadeh in Inf Control, 2005). Fuzzy logic has at least two main sources over the past century. The first of these sources was initiated by Peirce in the form what he called a logic of vagueness in 1900s, and the second source is Lotfi's A. Zadeh work, fuzzy sets and fuzzy Logic in the 1960s and 1970s.

关键词： nature of mind Zadeh fuzzy sets logic vagueness

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A field theory of words and agents

A field theory of words and agents

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作者： Resconi, Germano Nikravesh, Masoud Catholic Univeristy Brescia Italy BISC Program Computer Sciences Division University of California Berkeley CA 94720 United States

In this paper we assume that computation by words is equivalent to computation by fields. Any field is a word and computation by fields means computation of the words as fuzzy fields. Values of the fuzzy fields give us the semantic value of the words. Geometric image of the context H and of the field X in the Zadeh [12] form "H is r X" is given. Superposition of words (fields) to generate other words and filter rules to decompose a word in other words are given. Any word or field can be a basis field in the weighted superposition process to generate other words. The computation of the weights (sources) for which basis words are included in a given word is a very important problem that we solve in this paper. A connection between Boolean fields (fields whose values are one or zero) and fuzzy fields (words) by the projection operator is given. In conclusion we establish a new computation of words as fuzzy fields with a suitable information system and its multidimensional geometric representation. © 2008 Old City Publishing, Inc.

关键词： Software agents

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Field-wise waterflood management in low permeability, fractured oil reservoirs: Neuro-fuzzy approach

Field-wise waterflood management in low permeability, fractu...

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International Thermal Operations and Heavy Oil Symposium 1997, ITOHOS 1997

作者： Nikravesh, Masoud Dobie, Chuck A. Patzek, Tad W. Lawrence Berkeley National Laboratory and BISC Program Computer Science Division University of California at Berkeley SPE United States Crutcher-Tufts SPE United States

ISBN: (纸本)9781555634179

Here we present the third generation of "intelligent" oil field surveillance and prediction software based on neural networks and fuzzy logic. Neural networks and fuzzy logic provide a way to incorporate disparate information because a structural relationship between input and output data is not required. The model helps to improve waterflood management and avoid reservoir damage. It can also be used to visualize the global trajectory of an entire project and allow engineers to recognize patterns of incipient reservoir damage and poor performance. The diatomaceous fields of California, which hold an estimated 10 billion bbl of oil-in-place, have been chosen to demonstrate the power of neural networks and fuzzy logic. However, this methodology is applicable directly to fluid injection into other tight fractured reservoirs such as the Austin Chalk and the West Texas Carbonates. © 1997 SPE All rights reserved.

关键词： Fuzzy inference

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Evolution of fuzzy logic: from intelligent systems and computation to human mind

Evolution of fuzzy logic: from intelligent systems and compu...

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bisc International Special Event on Forging the Frontiers (biscSE'05)

作者： Nikravesh, Masoud Univ Calif Berkeley Div Comp Sci BISC Program EECS Dept Berkeley CA 94720 USA Univ Calif Berkeley Lawrence Berkeley Lab NERSC Berkeley CA 94720 USA

ISBN: (纸本)9783540731818

关键词： nature of mind Zadeh fuzzy sets logic vagueness

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