Fuzzy representation and reasoning in geotechnical site characterization (original) (raw)
Related papers
2015
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Application of Fuzzy Inference System to Estimating Rock Properties from Well Logs and Seismic Data
2015
All book chapters are Open Access distributed under the Creative Commons Attribution 3.0 license, which allows users to download, copy and build upon published articles even for commercial purposes, as long as the author and publisher are properly credited, which ensures maximum dissemination and a wider impact of our publications. However, users who aim to disseminate and distribute copies of this book as a whole must not seek monetary compensation for such service (excluded OMICS Group representatives and agreed collaborations). After this work has been published by OMICS Group, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Notice: Statements and opinions expressed in the book are those of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published chapters. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book.
3D Geological Modeling of Subsurface for Drilling Purposes Using Neural Networks and Fuzzy Logic
SUMMARY: 3D geological modeling is an important part of geomechanical models. Therefore, the knowledge of spatial distribution algorithms of geological properties is essential for the proper characterization of the subsurface. Generally, geostatistical and neural networks can be used as forecasting strategies of geological characteristics. One problem in forecasting logs and geomechanical properties is that they are not linearly related to several decision variables. Through neural networks it is possible to describe the behavior of complex systems and to estimate relevant correlations between a given set of inputs and another of outputs using a nonlinear function. Alternatively, fuzzy logic is a knowledge-based system that allows adding rules, which can be used to infer the result of a forecast. This work presents a methodology for 3D geological modeling of oil fields using artificial neural networks for forecasting geological properties, and a Fuzzy logic system – employed in order to improve the performance of neural network through the inclusion of geological rules. It was concluded that, using both computational intelligence techniques, it is possible to obtain reliable models based on numerical results.
Journal of Geophysics and Engineering, 2006
Permeability and rock type are the most important rock properties which can be used as input parameters to build 3D petrophysical models of hydrocarbon reservoirs. These parameters are derived from core samples which may not be available for all boreholes, whereas, almost all boreholes have well log data. In this study, the importance of the fuzzy logic approach for prediction of rock type from well log responses was shown by using an example of the Vp to Vs ratio for lithology determination from crisp and fuzzy logic approaches. A fuzzy c-means clustering technique was used for rock type classification using porosity and permeability data. Then, based on the fuzzy possibility concept, an algorithm was prepared to estimate clustering derived rock types from well log data. Permeability was modelled and predicted using a Takagi-Sugeno fuzzy inference system. Then a back propagation neural network was applied to verify fuzzy results for permeability modelling. For this purpose, three wells of the Iran offshore gas field were chosen for the construction of intelligent models of the reservoir, and a forth well was used as a test well to evaluate the reliability of the models. The results of this study show that fuzzy logic approach was successful for the prediction of permeability and rock types in the Iran offshore gas field.
EAGE Conference on Petroleum Geostatistics, 2007
The problem considered is a 3D Earth Model property prediction by well (and seismic) data. It is shown, that multimodal fuzzy presentation of the parameter (porosity) value in each elementary volume is quite adequate, when we deal with geological medium. After the region of parameter values is subdivided into n categories (n is no more than 20), the membership function f is represented by n number of discrete values f1, f2 ... fn. Presentation, when parameter value in each elementary volume is specified by a set of f1, f2 ... fn, is called a fuzzy model. Values f1, f2 ... fn are treated as evidences in favor of different parameter categories. They can be obtained using the next three procedures: (1) fuzzy transformation of well data; (2) fuzzy transformation of seismic attributes; (3) fuzzy evidence summation by Dempster’s rule. Analytical comparison with geostatistics is done in two point example. As shown, a fuzzy model uncertainty is dependent on data contrast. A case study with 93 wells is also considered. Thanks to economic uncertainty distribution, fuzzy model realizations look like a set of deterministic models.
GIS modeling using fuzzy logic approach in mineral prospecting based on geophysical data
The case study of geophysical prospective modelling for High Sulphidation Epithermal (HSE) Au deposit was taken over the Seruyung gold mine, located in the Nunukan Regency, North Kalimantan which is close to the Equator. Brown field exploration drilling is important to improve the mine life by adding the resources. Predicting realistic drill target is important to reduce the drill cost risk and loss opportunity to find the hidden target. The geophysical exploration method is the superior over the project area due to the dense of vegetation and thick soil so very limited geological outcrops. Due to between the ore body and host rock have contrast physical contents, geophysical survey could be used to assist to map the physical property bellow surface. Integrating the existing several layers is better than using a single layer of geophysical anomaly to predict the extension of ore body. Simplified Fuzzy method for mineral prospecting was implemented in this modelling and returned about 90% confident to delineate the existing ore body. MS Excel program was used to simplify the rules and the parameters of the modelling process.
This study explores the application of Takagi-Sugeno fuzzy inference system to predict reservoir permeability form depth and porosity measurements for Mashrif Formation in Nasyria Oil Field, south of Iraq. The models developed intend to describe the non-linear relationship between depth and porosity as inputs and permeability as output. A total of 206 core samples from three exploration wells (Ns-2, Ns-3, and Ns-5) were used to build a fuzzy model. Input data were divided into two groups including training set (170 data points) which represent the Ns-2 and Ns-3 wells; and testing set (36 data points which represent Ns-5). All membership functions and IF-THEN rules of the inference system were derived by using subtractive clustering technique. The performance of the model was measured by using degree of determination. The results of this study indicate that fuzzy logic technique is suitable to infer permeability from depth and porosity measurements alone without the need for the very expensive coring process. The calculated degree of determination was 0.98 for testing data set. A few core permeability and porosity measurements are required first to build fuzzy model and the fuzzy inference engine predict permeability for other sites of the field by knowing depth and porosity inputs which can be taken from conventional well logs data.
Rock Mechanics and Rock Engineering, 2010
The characterization of rock masses is one of the integral aspects of rock engineering. Over the years, many classification systems have been developed for characterization and design purposes in mining and civil engineering practices. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This results in subjective uncertainties, leading to the misuse of such classifications in practical applications. Fuzzy set theory is an effective tool to overcome such uncertainties by using membership functions and an inference system. This study illustrates the potential application of fuzzy set theory in assisting engineers in the rock engineering decision processes for which subjectivity plays an important role. So, the basic principles of fuzzy set theory are described and then it was applied to rock mass excavability (RME) classification to verify the applicability of fuzzy rock engineering classifications. It was concluded that fuzzy set theory has an acceptable reliability to be employed for all rock engineering classification systems.