Title :
An Artificial Intelligent Analysis Method in Reservoir Development during High Water Cut Stage
Author :
Changjiang, Wang ; Hanqiao, Jiang ; Limin, Lian
Author_Institution :
Coll. of Pet. & Natural Gas Eng., China Univ. of Pet., Beijing, China
Abstract :
High water and low oil production has been one of the major features of high water cut stage reservoir. In order to find a practical scheme to develop this kind of reservoir, an artificial intelligent analysis method by using statistical learning and clustering theory to evaluate and recombine rhythmite formations. The evaluating model was constructed by selecting appropriate indexes including permeability, variation coefficient of permeability, storage ability, moving ability, remaining recoverable reserves, oil mobility, water mobility and coefficient of flooding. The model can carry out intelligent analysis to some extent by taking advantage of its special merit of searching inner optimization. Therefore, it can find some valuable information from huge superficial data which is beyond people´s analysis abilities. This action makes the result more pragmatic and valuable. Finally, application of the novel model on Z block in SL oilfield has been provided.
Keywords :
hydrocarbon reservoirs; learning (artificial intelligence); oil technology; pattern clustering; statistical analysis; artificial intelligent analysis method; clustering theory; high water cut stage reservoir; low oil production; oil mobility; reservoir development; rhythmite formation recombination; statistical learning; Artificial intelligence; Floods; Hydrocarbon reservoirs; Information analysis; Permeability; Petroleum; Production; Statistical learning; Water resources; Water storage; Artificial Intelligence; Clustering Theory; High-water-cut Reservoir; Reservoir Development; Rhythmite Formation; SVM;
Conference_Titel :
Intelligent Systems, 2009. GCIS '09. WRI Global Congress on
Conference_Location :
Xiamen
Print_ISBN :
978-0-7695-3571-5
DOI :
10.1109/GCIS.2009.261