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Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification,case study:Fars domain,Iran

引用

Geo-Spatial Information Science 2022年第3期25卷 399-412页

作者： Sahand Seraj Mahmoud Reza Delavar School of Surveying and Geospatial Engineering University of TehranTehranIran Center of Excellence in Geomatics Engineering in Disaster Management University of TehranTehranIran

It is well agreed that geologic risk occurs during hydrocarbon exploration because diverse uncertainties accompany the entire hydrocarbon system parameters such as the source rock,reservoir rock,trap and seal *** order to overcome such attributes with uncertainties,a number of soft computing methods are *** granules could be provided by the rough fuzzy set Granulation(RFSG)with a thorough quality *** is capable of attribute reduction that has been claimed to be essential in investigating the hydrocarbon *** paper is an endeavor to recommend a Geospatial Information System(GIS)-based model with the aim of categorizing the hydrocarbon structures map consistent with the uncertainty range concepts of geologic risk in the rough fuzzy sets and granular *** model used the RFSG for the attribute reduction by a Decision Logic language(DLlanguage).The RFSG was employed in order to classify hydrocarbon structures according to geological risk and extract the fuzzy rules with a predefined range of *** order to assess the precisions of the fuzzy decisions on the hydrocarbon structure classification,the fuzzy entropy and fuzzy cross-entropy are *** proposed RFSG model applied for 62 structures as the training data,average fuzzy entropy has been calculated as 0.85,whereas the average fuzzy cross-entropy has been calculated *** it can be discerned,just seven structures had cross-entropies greater than 0.1,while three structures were larger than *** is implied that the precision of the proposed model is about 89%.The results yielded two reductions for the condition attributes and 11 fuzzy rules being filtered by the granular computing values.

关键词： rough fuzzy set granular computing Geospatial Information Science(GIS) petroleum system hydrocarbon structure

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引用

Journal of Systems Engineering and Electronics 2008年第5期19卷 945-951页

作者： Li Jian Xu Xiaojing Shi Kaiquan School of Mathematics and System Science Shandong Univ. Jinan 250100 P. R. China School of Computer Science & Technology Shandong Jianzhu Univ. Jinan 250101 P. R. China

Based on rough similarity degree of rough sets and close degree of fuzzy sets, the definitions of rough similarity degree and rough close degree of rough fuzzy sets are given, which can be used to measure the similar degree between two rough fuzzy sets. The properties and theorems are listed. Using the two new measures, the method of clustering in the rough fuzzy system can be obtained. After clustering, the new fuzzy sample can be recognized by the principle of maximal similarity degree.

关键词： rough fuzzy set rough similarity degree rough close degree clustering recognition of rough pattern,maximal similarity degree principle.

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