Determination of lithology boundary of jahrom formation in hendijan field in persian gulf using fuzzy clustering
Paper Details
Determination of lithology boundary of jahrom formation in hendijan field in persian gulf using fuzzy clustering
Abstract
Clustering is one of the main tools in introduction of similar patterns recognition which makes the analysis of existing data more accurate and comfortable. Clustering is used in different branches. In the hydrocarbon exploration activities, determination of boundary between formations is an important factor of hydrocarbon fields. Therefore, in this research, the logs collected from the area under study, were clustered using fuzzy clustering methods and the results were compared with the results from determination of actual boundary. The dolomite formation of Jahrom which Pabde shale formation located at its lower boundary has been studied. The goal of the study is to recognition of the boundary between the two formations using fuzzy clustering. A thickness with 300 meters length has been studied. Input data are logs data including DT, RHOB, PE, FDC, CGR, SGR, GR, CNL, NPHI and PEF which are classified in six separate groups with 3 members and one group with 4 members. To determine the degree of success of clustering, the ratio of within cluster distance to between cluster distances has been used. Because used logs in this study are able to recognize lithology. Fuzzy clustering with 3 member was partly successful to recognition the number of lithology. In group of 4 members, clustering was able to recognition lithology with great successful. Since this study has done on the one formation, obviously the logs group presented is valid for similar lithology. But it proves fuzzy clustering is useful and efficient for lithology determination in hydrocarbon field.
De Carvalho FAT, Teorio CP, Cavalcanti Junior NL. 2006. Partitional fuzzy clustering methods based on adaptive quadratic distances. Fuzzy Sets and Systems 157, 2833 – 2857.
Dong Y, Zhuang Y, Chen K, Tai X. 2006. A hierarchical clustering algorithm based on fuzzy graph connectedness. Fuzzy Sets and Systems 157, 1760 – 1774.
Feng Z, Zhou B, Shen J. 2007. A parallel hierarchical clustering algorithm for PCs cluster system. Neurocomputing 70, 809–818.
Lee M, Pedrycz W. 2010. Adaptivelearningo fordinal–numericalmappingsthrou ghfuzzy clustering fortheobjectsofmixedfeatures. Fuzzy Sets and Systems 161, 564–577.
Lucieer V, Lucieer A. 2009. Fuzzy clustering for seafloor classification. Marine Geology 264, 230– 241.
Pedrycz W, Hirota K. 2008. A consensus-driven fuzzy clustering. Pattern Recognition Letters 29, 1333–1343.
Salski A. 2007. Fuzzy clustering of fuzzy ecological data. Ecological informatics 2, 262 – 269.
Soto J, Flores-Sintas A, Palarea-Albaladejo J. 2008. Improving probabilities in a fuzzy clustering partition. Fuzzy Sets and Systems 159, 406 – 421.
Witold P, Kaoru H. 2008. A consensus-driven fuzzy clustering. Pattern Recognition Letters 29, 1333–1343.
Yang MS. 1993. A Survey of Fuzzy Clustering. Mathl. Comput. Modelling 18,11, 1-16.
Zhong C, Miao D, Wanga R, Zhou X. 2008. DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points. Pattern Recognition Letters 29, 2067–2077.
Zhu W, Jiang J, Song C, Bao L. 2011. Clustering Algorithm Based on Fuzzy C-means and Artificial Fish Swarm. Procedia Engineering 29, 3307–3311.
Seyede Tayebe Khalili, Arash Vakili , 2015. Determination of lithology boundary of jahrom formation in hendijan field in persian gulf using fuzzy clustering. J. Biodiv. Environ. Sci., 6(3), 489-495.
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