Electrical Engineering – Control
Karim Salahshoor; Seyed morteza Hoseini
Abstract
The model-based optimization of the waterflooding process has found significant scope for improving the economic life-cycle performance of oil fields due to geological and economic uncertainties compared to conventional reactive strategies. This paper proposes a new frequency-based system identification ...
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The model-based optimization of the waterflooding process has found significant scope for improving the economic life-cycle performance of oil fields due to geological and economic uncertainties compared to conventional reactive strategies. This paper proposes a new frequency-based system identification method to identify a robust multi-input, multi-output (MIMO) surrogate model for an oil reservoir under waterflooding process so as to describe all the injector-producer relationships. In contrast to the conventional modeling methods, the proposed data-driven modeling approach uses the available injection and production rates as the reservoir input–output data. Meanwhile, it includes a structured-bounded uncertainty model in the form of norm-bounded state-space function blocks to account for uncertainties, facilitating the identified model employed in robust control methodology using linear matrix inequality (LMI) problem formulation so as to eliminate the effect of model uncertainty. The identified MIMO surrogate model is integrated with a desired nonlinear net present value (NPV) objective function in a multi-input, single-output (MISO) system configuration to synthesize a model-based optimization prediction for economical operation and production of oil from oil reservoirs under both geological and economic uncertainties. The introduced approach is implemented on the “EGG model” as a well-recognized three-dimensional synthetic oil reservoir with eight water injection wells and four oil production wells. The results demonstrate that economic performance prediction of the oil reservoir, having an uncertain permeability field, lies in the evaluated bound of the uncertainty model. Waterflooding is a well-known method for increasing oil production. A significant amount of time and effort is required even for high-performance processors to numerically simulate a reservoir with thousands of grid blocks. On the other hand, there is a high uncertainty level in oil reservoir model-based economic optimization due to limited information about geological model parameters. Employing robust control methods can provide robustness for the performance and stability of the control system against model norm-bounded uncertainty. However, in all standard identification methods, it is assumed that the uncertainties in the model can be accommodated in the form of noise. Therefore, the challenge of using the models estimated from the standard identification approach in robust control methods can mainly be considered an essential subject. This paper presents a new frequency-based modeling approach to identify a surrogate model and uncertainty modeling for the waterflooding process with the ease of being employed in robust control methods. A desirable relationship is obtained between the injection rate and the economic production function to model the dynamics of the reservoir using the identification of the surrogate model. Then, the concept of structure bounded uncertainty modeling is presented to describe the model geological uncertainty.
Karim Salahshoor; Babak Roshanipour; Iman Karimi
Abstract
The current paper investigates the influence of packet losses in network control systems (NCS’s) using the model predictive control (MPC) strategy. The study focuses on two main network packet losses due to sensor to controller and controller to actuator along the communication paths. A new Markov-based ...
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The current paper investigates the influence of packet losses in network control systems (NCS’s) using the model predictive control (MPC) strategy. The study focuses on two main network packet losses due to sensor to controller and controller to actuator along the communication paths. A new Markov-based method is employed to recursively estimate the probability of time delay in controller to actuator path and a generalized predictive control (GPC) method is proposed to compensate the effect of big network time-delay, which leads to packet loss. The proposed methods and algorithms have been evaluated using a practical Smar fieldbus pilot plant to judge the efficiency of the foregoing algorithms. The obtained results clearly demonstrate the superiorities of the proposed control scheme with respect to standard MPC algorithm.
Karim Salahshoor; Mohammad Ghesmat; Mohammad Reza Shishesaz
Abstract
This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance ...
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This paper presents a new multi-sensor data fusion method based on the combination of wavelet transform (WT) and extended Kalman filter (EKF). Input data are first filtered by a wavelet transform via Daubechies wavelet “db4” functions and the filtered data are then fused based on variance weights in terms of minimum mean square error. The fused data are finally treated by extended Kalman filter for the final state estimation. The recent data are recursively utilized to apply wavelet transform and extract the variance of the updated data, which makes it suitable to be applied to both static and dynamic systems corrupted by noisy environments. The method has suitable performance in state estimation in comparison with the other alternative algorithms. A three-tank benchmark system has been adopted to comparatively demonstrate the performance merits of the method compared to a known algorithm in terms of efficiently satisfying signal-tonoise (SNR) and minimum square error (MSE) criteria.
Karim Salahshoor; Behnam Lotfi
Abstract
Control of well drilling operations poses a challenging issue to be tackled. The loss of well control could lead to the occurrence of blowout as a severe threat, involving the risk of human lives and environmental and economic consequences. Conventional proportional-integral (PI) controller is a common ...
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Control of well drilling operations poses a challenging issue to be tackled. The loss of well control could lead to the occurrence of blowout as a severe threat, involving the risk of human lives and environmental and economic consequences. Conventional proportional-integral (PI) controller is a common practice in the well control operation. The small existing margin between pore pressure and fracture gradients jeopardizes the efficiency of this conventional method to exercise an accurate and precise pressure control. There is a significant incentive to develop more efficient control methodologies to precisely control the annular pressure profile throughout the well bore to ascertain the down-hole pressure environment limits. Adaptive control presents an attractive candidate approach to achieving these demanding goals through adjusting itself to the changes during well drilling operations. The current paper presents a set of adaptive control paradigms in the form of self-tuning control (STC). The developed STC’s are comparatively evaluated on a simulated well drilling benchmark case study for both regulatory and servo-tracking control objectives. The different sets of test scenarios are conducted to represent the superior performance of the developed STC methods compared to the conventional PI control approach.
Mohammad Khalili; Riyaz Kharrat; Karim Salahshoor; Morteza Haghighat sefat
Abstract
One of the mostly used enhanced oil recovery methods is the injection of water or gas under pressure to maintain or reverse the declining pressure in a reservoir. Several parameters should be optimized in a fluid injection process. The usual optimizing methods evaluate several scenarios to find the best ...
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One of the mostly used enhanced oil recovery methods is the injection of water or gas under pressure to maintain or reverse the declining pressure in a reservoir. Several parameters should be optimized in a fluid injection process. The usual optimizing methods evaluate several scenarios to find the best solution. Since it is required to run the reservoir simulator hundreds of times, the process is very time consuming and cumbersome. In this study a new intelligent method of optimization, called “global dynamic harmony search” is used with some modifications in combination with a commercial reservoir simulator (ECLIPSE®) to determine the optimum solution for fluid injection problem unknowns. Net present value (NPV) is used as objective function to be maximized. First a simple homogeneous reservoir model is used for validating the developed method and then the new optimization method is applied to a real model of one of the Iran oil reservoirs. Three strategies, including gas injection, water injection, and well placement are considered. Comparing the values of NPV and field oil efficiency (FOE) of gas injection and water injection strategies, it is concluded that water injection strategy surpasses its rival. Considering water injection to be the base case, a well placement optimization is also done and best locations for water injection wells are proposed. The results show the satisfying performance of the algorithm regarding its low iterations.
