Predicting Injection Profiles Using ANFIS
Decision making pertaining to injection profiles during oilfield development is one of the most important factors that affect the oilfield's performance. Since injection profiles are affected by multiple geological and development factors, it is difficult to model their complicated, non-linear relationships using conventional approaches. In this paper, two adaptive-network-based fuzzy inference systems (ANFIS) based neuro-fuzzy systems are presented. The two neuro-fuzzy systems are: (1) grid partition based fuzzy inference system (FIS), named ANFIS-GRID, and (2) subtractive clustering based FIS, named ANFIS-SUB. We compare the performance of resultant FIS and study the effect of parameters. A real-world injection profile data set from the Daqing Oilfield, China is used. FIS are generated and tested using training and testing data from that data set. The impact of data quality on the performance of FIS is also studied. Experiments demonstrate that although soft computing methods are somewhat of tolerant of inaccurate inputs, cleaned data results in more robust models for practical problems. ANFIS-GRID outperforms ANFIS-SUB due to its simplicity in parameter selection and its fitness in the target problem.
M. Wei et al., "Predicting Injection Profiles Using ANFIS," Information Sciences, vol. 177, no. 20, pp. 4445-4461, Elsevier, Oct 2007.
The definitive version is available at https://doi.org/10.1016/j.ins.2007.03.021
Geosciences and Geological and Petroleum Engineering
New Mexico Petroleum Recovery Research Center
Keywords and Phrases
ANFIS; Data Quality Problems; Grid Partition; Petroleum Industry; Subtractive Clustering; Decision making; Fuzzy inference; Mathematical models; Parameter estimation; Problem solving; Robust control
International Standard Serial Number (ISSN)
Article - Journal
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