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Artificial gas lift technology is used in the oil industry to enhance oil recovery. Controlling this process is considered as a vital task by many researchers in the field. Solution of this problem would improve process performance and stabilize system operation. However, most of the control methodologies found in the literature and oil-field practice use only such process variables as on-surface flow and pressure measurements. The proposed approach offers soft sensing of process variables in the well that are unavailable for measurement due to technical constraints. For instance, measuring the flow rate of gas or liquid at the depth of a few kilometres would be unfeasible, given that the existing instruments cannot endure huge pressures and high temperatures at this depth. The proposed methodology provides soft sensing of these variables by utilizing a sliding mode observer (state estimator). Sliding mode observers are widely used for acquiring good estimates of unmeasured process variables in a dynamical system. This proposed soft sensing is primarily intended for control purpose. For that reason the estimator must provide computational efficiency with acceptable rate of accuracy, since the observer must be working in real-time to be a part of the control system. By linearizing the nonlinear model of the artificial gas lift dynamics, we reduce the complexity of implementation to simple arithmetic operations that must be realized within an estimator. In this case, computational efficiency of these operations would be higher than that involving solution of complex functions and algebraic equations that are involved with the nonlinear model of the gas lift. Therefore, the proposed linear estimator is simple, higly efficient in term of computation, and it satisfies the requirement for real-time operation. We developed a simple version of sliding mode observer through linearization principle applied in a number of points. The highly nonlinear gas lift system was linearized at different points which were selected as corresponding to different opening of the valve. This method is applicable for this scenario as the linearization of the model is valid around each point. Incorporating multiple sliding mode observers was achieved by utilizing the interpolation principle. This novel approach was tested with the nonlinear gas lift model and simulation results was obtained for 10-point and 20-point observers. The resulting estimates tracks the actual values with insignificant error. By checking the percentage error of our estimates, we conclude that the designed observer provides good and reliable results. This would permit further enhancements in the gas lift technology with less cost, by utilizing these estimates to control the nonlinear process using some advanced control techniques such as model predictive control.

An Artificial gas lift system is an existing technology in the oil sector; which utilizes the fact that a pressure differential exists in the reservoir's tubing leading to enhanced oil recovery from the reservoir. Studies were conducted to control this process as it improves the stability and performance of gas lift. The current industrial practice depends on flow measurement as a process variable, yet, it does not depend on the measurements which would be obtained several kilometers below the ground that are technically difficult to approach due to several limitations such as huge pressures. Proper knowledge of the states would leads to a better controller design for this system. In this paper, a methodology towards the design of a sliding mode observer is investigated. The purpose of the observer is to acquire the states of a nonlinear system representing the physical system of a gas lift process in oil wells. The proposed design of an observer is based only on measurements taken above the surface.

Artificial gas lift is widely used in the oil industry to enhance oil recovery. Active feedback control of this process would lead to its stabilization, that in some operating modes may otherwise be unstable, and to the increase of oil production. However, the control strategies are normally constrained to the use of the surface-measured process variables. The use of down-hole measurements would improve the performance of the control system but is technically hardly feasible due to the necessity of placing instruments in harsh conditions. The use of state observation might be a feasible alternative to the down-hole measurements. Recent development of a new accurate model of the artificial gas lift process enables us to increase accuracy of observation due to the account of pressure and density distribution along the well depth. Besides, a new approach to a nonlinear model treatment proposed in the present paper leads to a high computational efficiency of the observer for the gas lift. The paper presents an approach to the design of a novel sliding mode observer for the gas lift process.The observer uses multiple linearized models representing deviations from a set of equilibrium points. These models are then incorporated to produce estimates for the gas lift process variables. The approach is supported by simulations.

Compressors operating in parallel are widely used in compressor stations on natural gas pipelines to address the required flow demands. This paper presents a design of a new control structure and a load sharing optimal adaptive controller for multiple compressors connected in parallel and equipped with variable speed drives. The load sharing optimization (LSO) controller computes the split factor to distribute the flow among the compressors which depends on the current operating conditions, with the optimization's objective being to minimize the total energy consumption. In addition, the compressor maps are continuously updated to account for any changes due to external and untraceable factors resulting in an enhancement of the LSO. The presented control structure includes a common single controller for parallel compressors, which eliminates the need for loop-decoupling. Thus, ensuring a better stability and a faster dynamics with respect to the flow or pressure process variable. The proposed control structure and the adaptive LSO performance is evaluated through simulations and a lab hardware setup. The results show an improvement of more than 4% in the total energy consumption compared to an equal load sharing scheme and more than 2.5% compared to the equal distance to surge industrial scheme. This efficiency improvement leads to significant energy cost saving over large periods of time.