Abstract-Dynamic and mechanistic models of an Electrical Submersible Pump (ESP) lifted oil field are frequently used for understanding the process dynamics and the interactions occurring between the oil wells under varying operating conditions. They are also used to calculate the total fluid produced from the oil field. In this article, uncertainty and sensitivity analysis of such a model is studied. If the model is used for control and optimization of an oil field, it is important to see if the output calculated using the mathematical model lies within a confidence interval under the presence of uncertainties. It is also important to understand which parameters have strong/weak influence on the model output. The uncertainty analysis is performed by using the Monte Carlo simulation method. Morris method of elementary effect is used for input factor screening. To quantify the effect of the input factors on the model output, a variance based method of sensitivity analysis is used. Index Terms-Model uncertainty, ESP lifted oil field, morris method, variance based sensitivity analysis

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In this paper, a dynamic model for a rotary drum granulation loop with external product separator is developed. A population balance is used to capture dynamic particle size distribution in the 3-compartment rotary drum granulator model. Particle agglomeration along with particle growth due to layering are assumed as granulation mechanisms in the rotary drum. The model of the granulation loop includes models of the drum, screens and a crusher. Simulations using the developed model provide valuable data on dynamic fluctuations in the inlet and the outlet particle size distribution for the rotary drum. Simulation results showed that at smaller crusher gap spacings, the instabilities of the drum granulation loop occur, and damped oscillations are observed. Above the critical crusher gap spacing, sustained periodic oscillations are observed. The reason for oscillations is the off-spec particle flow that is recycled back to the granulator.

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Few granulation plants are operated optimally. It is common to operate granulation plants below their maximum design capacity, and in many cases, periodic instabilities may also occur. From a process control and optimization point of view, it is desirable to develop a dynamic model that can show the dominating dynamics of a granulation process and can be used for design of optimal operation of the granulation plant. In this paper, a dynamic model of a drum granulator is developed using population balance (PB). Different simulation scenarios are used to analyze various granulation mechanisms that are characteristic to drum granulators. Simulation results show that for the drum granulator, the particle agglomeration has a greater impact on the change in particle size distribution (PSD) compared to the particle growth due to layering. In addition, coarser particles are produced when a sizedependent agglomeration kernel is used in the granulator model. For combined processes, i.e., processes where the particle growth due to layering and agglomeration are considered simultaneously, coarser particles with a wider PSD are obtained with the size-dependent agglomeration kernel.

In an Electric Submersible Pump (ESP) lifted oil field, the ESP of each oil well should be operated inside its operating window. The total power consumed by the ESPs in the oil field should be minimized. The speed of the ESPs and the production choke valve opening should be optimally chosen for maximizing the total oil produced from the oil field. At the same time, the capacity of the separator should not be exceeded. In this paper, nonlinear steady state optimization based on Sequential Quadratic Programming (SQP) is developed. Two optimal control structures are proposed in this paper. In the first case, the optimal pump speed is controlled by a PI controller by varying the electrical excitation signal to the motors. The optimal fluid flow rate through each oil well is controlled by another PI controller by varying the production choke valve opening. The paper shows that the production choke valve for each oil well has to be always 100% open to maintain the optimal fluid flow rate. In the second case, the production choke valves are considered to be always 100% open as hard constraints. The optimal fluid flow rate through each oil well is controlled by a PI controller by varying the pump speed. It is shown that when the optimal fluid flow rate is tracked by the controller, the speed of each of the pumps is equal to the optimal pump speed calculated by the optimizer. This basically means that we can achieve the optimization objective with the same optimal results as in the first case by using only a single PI controller. The limitations of these two optimal control structures for very low values and for very high values of the separator capacity are discussed. For the feasible range of separator capacities, the optimal locus of the fluid flow rate and the pump speed are shown in this paper.

Proper allocation and distribution of lift gas is necessary for maximizing total oil production from a eld with gas lifted oil wells. When the supply of the lift gas is limited, the total available gas should be optimally distributed among the oil wells of the eld such that the total production of oil from the eld is maximized. This paper describes a non-linear optimization problem with constraints associated with the optimal distribution of the lift gas. A non-linear objective function is developed using a simple dynamic model of the oil eld where the decision variables represent the lift gas ow rate set points of each oil well of the eld. The lift gas optimization problem is solved using the 'fmincon' solver found in MATLAB. As an alternative and for verication, hill climbing method is utilized for solving the optimization problem. Using both of these methods, it has been shown that after optimization, the total oil production is increased by about 4%. For multiple oil wells sharing lift gas from a common source, a cascade control strategy along with a nonlinear steady state optimizer behaves as a self-optimizing control structure when the total supply of lift gas is assumed to be the only input disturbance present in the process. Simulation results show that repeated optimization performed after the rst time optimization under the presence of the input disturbance has no eect in the total oil production.

The operation of granulation plants on an industrial scale is challenging. Periodic instability associated with the operation of the granulation loop causes the particle size distribution of the particles flowing out from the granulator to oscillate, thus making it difficult to maintain the desired product quality. To address this problem, two control strategies are proposed in this paper, including a novel approach, where product-sized particles are recycled back to maintain a stable granulation loop process. A dynamic model of the process that is based on a population balance equation is used to represent the process dynamics. Both of the control strategies utilize a double-loop control structure that is suitable for highly oscillatory systems. The simulation results show that both control strategies, including the novel approach, are able to remove the oscillating behaviour and stabilize the granulation plant loop.