ptimization of lift gas allocation in a gas lifted oil field as non-linear optimization problem

Abstract

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.

Simulation and Optimization Study of Ultra-Precision Turning Process

“…Table (3) and Table (4) illustrate the optimum cutting conditions at different cutting edge radii, particularly in additions to 5 µm edge radius too, cutting edge radii of 20 µm and 40 µm were also studied. Table (3) and Table ( 4) show that minimum roughness obtained by GA is more accurate than the results achieved by SQP in case of cutting edge radius of 20 µm and 40 µm. In Table (3), for Brass 6040 the minimum surface roughness obtained by GA when using cutting tool with nose radius 200 µm and 400 µm are less than simulation results, which can be explained by the large increment, 5 µm, that was used to carry out the simulation.…”

“…The algorithms are Active-set (AS), (SQP), and Interior-point (IP). Also, fmincon provides to solve functions linear or nonlinear, constrained or unconstrained problems [4,5]. Another non-conventional optimization technique is Genetic Algorithm (GA).…”

“…Many researchers have studied the micro-cutting operations experimentally and theoretically in order to assess the process behavior [1][2][3][4][5][6][7][8][9][10][11]. The goal of these studies was to identify and control the dominating parameters for the best possible performance of the process.…”

Oil Well Characterization and Artificial Gas Lift Optimization Using Neural Networks Combined with Genetic Algorithm

“…We seek to characterize the oil wells and get maximum produced oil using limited available gas which indirectly results in increased economic returns. Further details and description of the gas lift problem can be found in [2][3][4][6][7][8].…”

The Framework Dedicated to Three Phase Flows Wellbore Modelling

“…Optimisation of gas lift is a well known problem [3,4], but we are concentrating on the gas injection influence on the final results of simulation. Unfortunately, the authors do not have any possibility to verify the simulation results by the comparison to the real data.…”