Carbonate lithofacies and permeability Estimation Using Fuzzy Logic | Carbonate lithofacies and permeability Estimation Using Type-1 and Type-2 Fuzzy Logic Inference Systems: Methodology and Comparative Studies

Sunday O. Olatunji

ISBN 10: 3639257286 / ISBN 13: 9783639257281

Published by VDM Verlag Dr. Müller, 2010

About this Item

Carbonate lithofacies and permeability Estimation Using Fuzzy Logic | Carbonate lithofacies and permeability Estimation Using Type-1 and Type-2 Fuzzy Logic Inference Systems: Methodology and Comparative Studies | Sunday O. Olatunji | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639257281 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: Carbonate lithofacies and permeability Estimation Using Fuzzy Logic | Carbonate lithofacies and permeability Estimation Using Type-1 and Type-2 Fuzzy Logic Inference Systems: Methodology and Comparative Studies
• Publisher: VDM Verlag Dr. Müller
• Publication Date: 2010
• Language: English
• ISBN 10: 3639257286
• ISBN 13: 9783639257281
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 150W x 10H x 219D millimeters 150 millimeters width by 10 millimeters height by 219 millimeters depth
• Weight: 322 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

This work explores the capability of both Type-1 and Type-2 Fuzzy Inference Systems as novel approaches for predicting carbonate lithofacies and permeability from Well Logs. As expected, these new computational intelligence approaches overcome the weaknesses of the standard neural networks limitations. In addition, we carried out a comparative study to compare their performances with those of the conventional artificial neural network models and other popular techniques. Empirical results show that the performance of T1&T2 FIS novel approaches outperform most of the common existing approaches, particularly in the area of ability to handle data in uncertain situations, which are the common characteristics of well logs data. As an additional advantage of this work, a Type-2 fuzzy logic based model has been developed that will generate, not only the target forecast, but also prediction intervals without extra compuational cost. The methodology involved were described and We have developed smart simulation structure to attack such problem with real-industry data to prove our contribution.

About the Author

Sunday Olusanya Olatunji had B.Sc., M.Sc. in computer science, Master of Science (MS) in information and Computer Science. He is currently pursuing his PhD in Computer Science. He has participated in research projects making use of artificial intelligence based methods in solving real industrial problems. He is a member of ACM and IEEE.