Sharif Digital Repository

Management decisions, enhanced oil recovery, and reservoir development plans in petroleum industries are based on predictions by reservoir simulation. Due to uncertainties in model parameters or engineering assumptions, the simulation results are not accurate, while they are correct. For more accurate estimation of unknown production quantities, it is required to characterize the unknown parameters and its uncertainty. By using static data alone the result of characterization is unreliable and unsure, therefore dynamic data use practically. In reservoir engineering literature, this is called “History Matching”.The ensemble Kalman filter is an optimal recursive data processing algorithm based... 

Terrestrial water storage change is an important factor in a region ecosystem studies and climate variability effects. Terrestrial water storage is composed of water on vegetation surfaces, in the biomass, in the unsaturated soil or rock zone, as groundwater, snow and ice, and as surface water in rivers, lakes, reservoirs and wetlands. There was no possibility to monitor and estimate this volume of water and it's changes during past decade because of lack of global processing system. After a successful launch, the GRACE mission developed by NASA is currently mapping the variations of the gravity field of the Earth over its lifetime.
The aim of this study is to investigate Terrestrial... 

A typical problem in applied mathematics and science is to estimate the future state of a dynamical system given its current state. One approach aimed at understanding one or more aspects determining the behavior of the system is mathematical modeling. This method frequently entails formulation of a set of equations, usually a system of partial or ordinary differential equations. Model parameters are then measured from experimental data or estimated from computer simulation or other methods. Solutions to the model are then studied through mathematical analysis and numerical simulation usually for qualitative fit to the dynamical system of interest and any relative time-series data that is... 

Hydrocarbon reservoirs development and management is based on their dynamic models. To encounter various types of error during model building, model parameters are adjusted to produce reservoir historical data by assimilation (history matching) of reservoir production or 4D seismic data. Among the existing sequential methods for automatic history matching, ensemble Kalman filter and its variants have displayed promising results. The innovations of this thesis for ensemble Kalman filter (EnKF) are presented into three major orients; these includes adaptive localization/regularization, characterization of original PUNQ test model and characterization of channelized reservoir.
To mitigate... 

Hydrocarbon reservoir management and development as well as planning of enhanced oil recovery (EOR) processes are based on the reservoir dynamic model. Thus, successful implementation of EOR scenarios greatly depends on the quality of the dynamic model and accuracy of the associated parameters in order to correctly describe fluid flow through porous media. First, a dynamic model is constructed based on the prior knowledge. However, because of the various types of error during model building, the prior model is not so accurate and perfect. Accordingly, new observation data, such as production and 4D seismic data, are utilized to calibrate the prior model and characterize the reservoir under a... 

In this research, the aim is to investigate the use of data assimilation method for better reservoir model updating in the reservoir management. In addition, multi-objective optimization concept is studied for the production optimization. The application of these methods is applied for reservoir management in the naturally fractured reservoirs. To update the reservoir, ensemble based methods, especially ensemble Kalman filter and ensemble smoother, are used. To tackle the challenges encountered with these methods, modifications are proposed to obtain a better history matching and more accurate reservoir characterization. The proposed framework for history matching is implemented for the... 

In atmospheric studies, chemical transport models are formulated to simulate the spatial and temporal distribution of pollutant concentrations. However, the performance of these models is strongly dependent on the input parameters such as emissions. Inverse modeling is a widely used mathematical approach for estimating model parameters by minimizing the discrepancy between model output and observations. For air quality studies, inverse modeling is often used for emission inversion as emissions are associated with significant amount of uncertainties.This research aims to estimate optimal values for anthropogenic PM2.5 emission through a four-dimensional variational (4D-Var) inverse modeling... 

Having information about the amount of water storage is essential to estimate the amount of water available for use in agriculture, industrial, etc. The purpose of this study is to propose a method for estimating the water budget in large-scale basins using remote sensing data. Using ground-based measurements to obtain water budget components is a challenging issue. This may be due to the limited access to in-site data in temporal and spatial resolution, especially in large scale basins. In recent years, with the increasing growth of satellite data, the application of remotely sensed data to estimate changes in water storage is increasing. In this study, due to the limitation of access to...