| This thesis addresses two problems about water-flood management: (1) infer reservoir heterogeneity using measured injection and production rates, and (2) construct a decision support system to optimize oil production using computing with words (CWW) and the inferred reservoir heterogeneity.;To infer reservoir heterogeneity, we first present an adaptive method using an Extended Kalman Filter (EKF) for the case of multiple injectors and a single producer, and then present a pseudo-virtual reservoir method for the case of multiple injectors and multiple producers, respectively. In the EKF method, a very simple parametric model, one with two parameters per injector, is used so that if a producer depends upon N injectors our model contains exactly 2N parameters; and the EKF is used to adaptively estimate the 2N parameters, from which the injector-producer relationship (IPR) between each injector and a producer is then estimated. In the pseudo-virtual reservoir method, a virtual reservoir model is used to model the reservoir, and a pseudo-virtual reservoir model is used to estimate the regional impact from each injector, so that the problem for this case reduces to the problem for the case of multiple injectors and a single producer.;The study for decision support system using CWW focuses on the encoder component (called type-2 fuzzistics) of a perceptual computer (Per-C), a specific architecture for CWW using interval type-2 fuzzy sets, and our work so far has been to transform linguistic perceptions, words, into interval type-2 fuzzy sets (IT2 FS) that activate a CWW engine. We have proposed a new and simple approach, called the Interval Approach ( IA), to type-2 fuzzistics, one that captures the strong points of both the person membership function and interval end-points approaches. It collects interval end-point data from subjects, does not require subjects to be knowledgeable about fuzzy sets, has a straightforward mapping from data to footprint of uncertainty (FOU), does not require an a priori assumption about whether or not a FOU is symmetric or non-symmetric, and leads to an IT2 FS word model that reduces to a T1 FS word model automatically if all subjects provide the same intervals. |
