5 research outputs found
On Rough Sets and Hyperlattices
In this paper, we introduce the concepts of upper and lower rough hyper fuzzy ideals (filters) in a hyperlattice and their basic properties are discussed. Let be a hyper congruence relation on . We show that if is a fuzzy subset of , then and , where is the least hyper fuzzy ideal of $L$ containing $\mu$ and \mu^*(x) = sup\{\alpha \in [0, 1]: x \in I( \mu_{\alpha} )\} for all . Next, we prove that if is a hyper fuzzy ideal of , then is an upper rough fuzzy ideal. Also, if is a complete on and is a hyper fuzzy prime ideal of such that is a proper fuzzy subset of , then is an upper rough fuzzy prime ideal. Furthermore, let be a -complete congruence relation on . If is a hyper fuzzy ideal, then is a lower rough fuzzy ideal and if is a hyper fuzzy prime ideal such that is a proper fuzzy subset of , then is a lower rough fuzzy prime ideal
Development of memory-based models for reservoir fluid characterization
The petroleum industry play an important role in supplying required energy all over the
world. Effective methods are required to stimulate the process. Petroleum fluids are the
mixture of complex hydrocarbons. Several techniques being used to predict reserve
estimation, recovery, production, enhanced oil recovery, etc. Despite of modern
engineering advancement, still, there are some drawbacks, such as, conventional models,
linearized rock-fluid properties models, inaccurate risk assessment, and inappropriate
descriptions of thermal effects. In this research, new mathematical models for petroleum
fluids (non-Newtonian) regarding various degree of complexities will be developed. The
most significant component will be the continuous time function introduced to the
rheology. Previous attempts are addressed in this modeling, and those models were limited
for some specific cases and fluids. The current proposal will develop a comprehensive
model that can be applied to different reservoir fluids irrespective to fluid origin. In
addition, the proposed models will also be adjusted for a complex mixture of reservoir
fluids. The model equations will be solved numerically and validated using field data and
data gathered from experimental tasks available in the literature. The proposed models will
be developed focusing light crude oil for reservoir conditions. The role of various factors,
such as crude oil density, viscosity, compressibility, surface tension, ambient temperature,
and temperature will be included in the predictive models. Model equations will be solved
with non-linear solvers, as outlined earlier. This will generate a range of solutions, rather
than a line of unique solutions. This analysis will increase an accuracy of the predictive
tool and will enable one to assess the uncertainty with greater confidence