Aggregation and modeling using computational intelligence techniques
Minshen Hao · University of Southern California Digital Library · 2014
In waterflood management, there exists several models to describe a petroleum reservoir for predicting the future production rates using scheduled injection rates. Most of them have the ability to estimate how much the injectors impact some specific producers, namely, the interwell connectivities between the injectors and the producers. Knowing these values not only reduces the cost of water injection, but can also increase the oil production. ? In the first part of this thesis, we construct four different models for the interaction between a group of injectors and a producer and then dynamically estimate the parameters of these models, along with the interwell connectivities using an Iterated Extended Kalman Filter (IEKF) and Smoother (EKS). We then use the Weighted Average (WA) and Generalized Choquet Integral (GCI) to aggregate the estimated interwell connectivities. These two aggregation functions are optimized to minimize mean-square errors in future forecasted production rates. This is done by using Quantum Particle Swarm Optimization (QPSO) to search for the optimal set of weights which are required by both aggregation methods. Several experiments are conducted to show the improved average performance of our approach on a set of data from a real reservoir, and the performances of the above two aggregation methods are also compared and analyzed. ? A similarity measure between fuzzy sets is a very important concept in fuzzy set theory. There have been a lot of different similarity measures proposed in the literature, for both T1 FSs and IT2 FSs. The second part of this thesis presents theoretical studies that were performed for the most advanced fuzzy logic sets that are currently under research?general type?2 fuzzy sets. In our study, based on the ?-plane representation for a general type?2 (GT2) FS, the similarity measure is generalized to such T2 FSs. Some examples that demonstrate how to compute the similarity measures for different T2 FSs are given. ? Next, the third part of this thesis proposes a new method?the HM method?to model words by normal IT2 FSs, using data intervals that are collected from a group of subjects. The HM method uses the same bad data processing, outlier processing and tolerance limit processing to pre?process the data intervals, as is used in the Enhanced Interval Approach (EIA)