Sharif Digital Repository

In this study, the ensemble Kalman filter is used to characterize threephase flow in porous media through simultaneous estimation of three-phase relative permeabilities and capillary pressures from production data. Power-law models of relative permeability and capillary pressure curves are used and the associated unknown parameters are estimated by assimilating the measured historical data. The estimation procedure is demonstrated on a twin numerical setup with two different scenarios, in which a synthetic 2D reservoir under three-phase flow is considered. In the first scenario, all the endpoints are assumed to be known and only the shape factors are estimated during the assimilation... 

In this study, impact of initial ensembles on posterior distribution of ensemble-based assimilation methods is statistically analyzed. Along with, sampling performance as well as uncertainty quantification of these methods are compared in terms of their ability to accurately and consistently evaluate unknown reservoir model parameters and reservoir future performance. For this purpose, a synthetic test problem, which is a small but highly nonlinear reservoir model under two-phase flow, is utilized. Subsequently, different initial ensemble sets are considered and are updated through the assimilation process performed on the test problem using ensemble-based assimilation methods. Afterwards,... 

In this study, ensemble Kalman filter (EnKF) is first applied to estimate absolute and relative permeabilities jointly under three-phase flow condition in the porous media. By assimilating historical data, absolute permeability field is adjusted progressively towards its reference. However, assimilation process does not improve the estimation of all relative permeability parameters, and some of them are poorly estimated at the end of assimilation. To improve the estimation of the relative permeability curves, we propose a new hybrid approach in which the estimation process of the absolute and relative permeabilities is separated. In this approach, gridblock permeabilities are again estimated... 

A stochastic real time optimization (SRTO) which has an efficient result has been implemented on the Tennessee Eastman (TE) challenging problem. In this article a novel stochastic optimization method, the so-called heuristic random optimization (HRO) proposed by Li & Rhinehart is used which attempts to rationally combine features of both deterministic and random (stochastic) methods. Further, an on-line nonlinear identifier via extended Kalman filter (EKF) is used to supply the plant model for model-based optimization algorithm. Using the information obtained from EKF an on-line HRO is accomplished by a random search method whose search directions and steps are considerably reduced by some... 

Perturbed-Chain Statistical Associating Fluid Theory Equation of State (PC-SAFT EoS) requires cross interaction parameter for each binary pair in the mixture. For real mixtures, these parameters should be corrected by binary interaction coefficients (kij's). The values of kij's are tuned by an optimization method in order to minimize the deviation from equilibrium data. The Particle Swarm Optimization (PSO) algorithm is employed for optimization of kij's due to the continuous nature of kij and highly nonlinear nature of PC-SAFT EoS. Although kij can be adjusted using the mentioned algorithm, it is cumbersome and highly time-consuming because the optimization should be performed for each pair... 

A continuous-discrete Distributed and Decentralized Switching Kalman Filter (DDSKF) is designed for estimation of spatial profiles in Pressure Swing Adsorption (PSA) processes. The introduced observer is an integral part of the control strategy of hybrid systems in general and PSA systems in particular. A reduced order model is developed based on the mechanistic model of the process. The sensors are optimally located and observability of the process is studied. The proposed observer is used to estimate the spatial profiles of various states of a two-bed, six-step PSA system used for production of pure H2 from a H2–CH4 gas mixture. The spatial profiles of the system have been estimated using... 

As the oil production from conventional oil reservoirs is decreasing, oil production through Enhanced Oil Recovery (EOR) processes is supposed to compensate for both the oil production reduction in matured oil reservoirs and the worldwide dramatic increase in oil demand. Therefore, developing a strategy to choose an optimized EOR technique is crucial to find a resolution for production decline in oil reservoirs. A screening tool recommending the most appropriate EOR method is proposed in this study. An expert fuzzy logic system is employed to screen four well-known EOR methods including miscible CO2 injection, miscible HC gas injection, polymer flooding and steam injection based on the... 

In this paper, the fuzzy system has been used for fault detection and diagnosis of a yeast fermentation bioreactor based on measurements corrupted by noise. In one case, parameters of membership functions are selected in a conventional manner. In another case, using certainty factors between normal and faulty conditions the optimal values of these parameters have been obtained through the genetic algorithm. These two cases are compared based on their performances in fault diagnosis of a yeast fermentation bioreactor for three different conditions. The simulation results indicate that the fuzzy-genetic system is superior in multiple fault detection for the conditions where the minimum and... 

As the oil production from conventional oil reservoirs is decreasing, oil production through Enhanced Oil Recovery (EOR) processes is supposed to compensate for both the oil production reduction in matured oil reservoirs and the worldwide dramatic increase in oil demand. Therefore, developing a strategy to choose an optimized EOR technique is crucial to find a resolution for production decline in oil reservoirs. A screening tool recommending the most appropriate EOR method is proposed in this study. An expert fuzzy logic system is employed to screen four well-known EOR methods including miscible CO2 injection, miscible HC gas injection, polymer flooding and steam injection based on the... 

This study is focused on the development of a systematic computational approach which implements Genetic Algorithm (GA) to find the optimal rigorous kinetic models. A general Kinetic model for hydrogenolysis of dibenzothiophene (DBT) based on Langmuir Hinshelwood type has been obtained from open literature. This model consists of eight continuous parameters(e.g., Arrhenus and Van't Hoff parameters) and six discrete parameters representing the order of the reaction with respect to each concentration. The optimal value of these parameters have been obtained based on Genetic Algorithm. Furthermore, the best type of Genetic operators and their corresponding parameters for this type of problems... 

Extended Kalman Filtering (EKF) is a nonlinear dynamic data reconciliation (NDDR) method. One of its main advantages is its suitability for on-line applications. This paper presents an on-line NDDR method using EKF. It is implemented for two case studies, temperature measurements of a distillation column and concentration measurements of a CSTR. In each time step, random numbers with zero mean and specified variance were added to simulated results by a random number generator. The generated data are transferred on-line to a developed data reconciliation software. The software performs NDDR on received data using EKF method. Comparison of data reconciliation results with simulated... 

A new approach for static state feedback linearization of single input and multi-input systems is put forward. The approach can be applied to wider class of systems than the classical feedback linearization. The main idea is to transform the nonlinear system via a change of coordinates and static state feedback into a linear system with desired dynamics. This circumvents the need to transform the input vector fields into constant vectors in the new coordinates. An amine gas sweetening process has been simulated as a nonlinear system, then the system is linearized using the approximate fuzzy feedback linearization method. The rules of the fuzzy system have been optimized by genetic algorithm... 

Clustering is the term used in several research communities to describe methods for grouping of unlabeled data. The objective in clustering is to divide the data set in such a way that the intra-class similarity is high and inter-class similarity is low. It is very helpful to find a system's states distributions, but when the system's scale is large, e.g., considering a distillation column that has crude oil as its feed, if one of desired is finding concentration of most popular hydrocarbons (ethane, propane, octane, etc.) on each tray, it should be solve species continuity for each component. A mixture of 100 hydrocarbons was classified into 10 clusters by different clustering methods,... 

Combined cycle power plants (CCPPs) play an important role in electricity production throughout the world. Their energy efficiency is relatively high and their production rates of greenhouse gases are considerably low. In a country like Iran with huge oil and gas resources, most CCPP's use natural gas as primary fuel and diesel as secondary fuel. In this study, effect of using diesel instead of natural gas for a selected power plant will be investigated in terms of exergy, economic and environmental impacts. The environmental evaluation is performed using life cycle assessment (LCA). In the second step, the operation of the plant will be optimized using exergy and economic objective... 

The effect of operation variables, such as the H2/CO molar feed ratio, gas hourly space velocity (GHSV), temperature, and pressure, on the catalytic performance of the Co/Mn/TiO2 catalyst prepared at the Research Institute of the Petroleum Industry (RIPI) was investigated, and optimum reactor conditions were obtained to produce the maximum amount of light olefins. The catalyst was prepared by co-precipitation of Co and Mn phases in the presence of commercial TiO2 with maximum selectivity for ethylene and propylene production. It was found that the [H2]/[CO]=2/1, space velocity (GHSV) of 1800 h-1, 280°C temperature and 4 bar pressure were optimum operating conditions for the modified catalyst... 

In order to safe design and optimize performance of some industrial systems, it's often needed to categorize two-phase flow into different regimes. In each flow regime, flow conditions have similar geometric and hydrodynamic characteristics. Traditionally, flow regime identification was carried out by flow visualization or instrumental indicators. In this research3 kind of neural networks have been used to predict system characteristic and flow regime, and results of them were compared: radial basis function neural networks, self organized and Multilayer perceptrons (supervised) neural networks. The data bank contains experimental pressure signalfor a wide range of operational conditions in... 

Thermal cracking of hydrocarbons converts them into valuable materials in the petrochemical industries. Multiplicity of the reaction routes and complexity of the mathematical approach has led us use a kind of black-box modelingsartificial neural networks. Reactor feed type plays an essential role on the product qualities. Feed type is a qualitative character. In this paper, a method is presented to introduce a range of petroleum fractions to the neural network. To introduce petroleum cuts with final boiling points of 865 °F maximum to the neural network, a real component substitute mixture is made from the original mixture. Such substitute mixture is fully defined, it has a chemical... 

A coupled statistical and conditional generative adversarial neural network is used for 3D reconstruction of both homogeneous and heterogeneous porous media from a single two-dimensional image. A statistical approach feeds the deep network with conditional data, and then the reconstruction is trained on a deep generative network. The conditional nature of the generative model helps in network stability and convergence which has been optimized through a gradient-descent-based optimization method. Moreover, this coupled approach allows the reconstruction of heterogeneous samples, a critical and serious challenge in conventional reconstruction methods. The main contribution of this work is to...