Title :
Petro-data cluster mining - knowledge building analysis of complex petroleum systems
Author :
Nimmagadda, Shastri L. ; Dreher, Heinz
Author_Institution :
Schlumberger, East Ahmadi
Abstract :
Large volumes of historical petroleum data are available and presently unused primarily because of lack of knowledge. Initially, conceptual data models are derived and warehoused for data mining. For this purpose, petroleum system analysis and knowledge mapping of geological structure, reservoir, well and oil/gas production data are done concentrating on the key issues of geological-structure, reservoir and production data dimensions. Clustering is a data-mining tool for categorizing and analyzing groups of these data dimensions having similar attribute characteristics or properties. Using data warehousing, mining and interpretation strategies, petro-clustering is designed for understanding petroleum systems. Knowledge acquired on petroleum data clusters enhances understanding of relationships among petroleum data attributes, which can optimize economics of oil and gas exploration and development in the petroleum bearing basins.
Keywords :
data mining; data warehouses; petroleum industry; production engineering computing; complex petroleum systems; conceptual data models; data warehousing; knowledge building analysis; knowledge mapping; petro-data cluster mining; petroleum data; production data dimensions; Australia; Data mining; Drilling; Geology; Hydrocarbon reservoirs; Information analysis; Information systems; Large-scale systems; Petroleum industry; Production systems;
Conference_Titel :
Industrial Technology, 2009. ICIT 2009. IEEE International Conference on
Conference_Location :
Gippsland, VIC
Print_ISBN :
978-1-4244-3506-7
Electronic_ISBN :
978-1-4244-3507-4
DOI :
10.1109/ICIT.2009.4939729