Real-time adaptive observer technology of an oil-well hydrodynamic deep states

GOVORKOV, Denis A.; Solovyev, Iliya; Fomin, Vitaliy

doi:10.1109/spcmtt.2008.4897510

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…The adaptive observer has a better performance when the measurement noise covariance is high. Prior art also includes a real‐time adaptive nonlinear observer for the estimation and control of an oil well hydrodynamic deep states , a fuzzy observer and controller in gas‐lifted oil well , a high gain observer , and an estimator for gas lift parameters using artificial neural network (ANN) model . The observers presented in the above‐mentioned publications include models ranging from very complex to very simple.…”

Section: Introductionmentioning

confidence: 99%

Multiple‐model sliding mode observer for an artificial gas lift system

Hammadih

Hosani

Boiko

2018

Asian Journal of Control

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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.

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Section: Introductionmentioning

confidence: 99%

Multiple‐model sliding mode observer for an artificial gas lift system

Hammadih

Hosani

Boiko

2018

Asian Journal of Control

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Soft Sensing in Deep Wells Within Artificial Gas Lift Technology

Hammadih

Hosani

Boiko

2015

Day 4 Thu, November 12, 2015

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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.

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Real-time adaptive observer technology of an oil-well hydrodynamic deep states

Cited by 2 publications

References 4 publications

Multiple‐model sliding mode observer for an artificial gas lift system

Multiple‐model sliding mode observer for an artificial gas lift system

Soft Sensing in Deep Wells Within Artificial Gas Lift Technology

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