Real-Time Optimization under Uncertainty Applied to a Gas Lifted Well Network

Krishnamoorthy, Dinesh; Foss, Bjarne A.; Skogestad, Sigurd

doi:10.3390/pr4040052

Cited by 59 publications

(30 citation statements)

References 25 publications

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“…Decision trees, which are a strong regression method, have a clear concept description for a dataset. The decision tree learning method is a popular method because of its fast data processing capability and because it produces successful performance predictive models [42,43]. The alternative decision tree (ADTree) method consists of decision nodes and prediction nodes.…”

Section: Alternating Decision Treementioning

confidence: 99%

Heat Flux Estimation at Pool Boiling Processes with Computational Intelligence Methods

2019

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It is difficult to manually process and analyze large amounts of data. Therefore, to solve a given problem, it is easier to reach the solution by studying the data obtained from the environment of the problem with computational intelligence methods. In this study, pool boiling heat flux was estimated in the isolated bubble regime using two optimization methods (genetic and artificial bee colony algorithm) and three machine learning algorithms (decision tree, artificial neural network, and support vector machine). Six boiling mechanisms containing eighteen different parameters in the genetic and the artificial bee colony (ABC) algorithms were used to calculate overall heat flux of the isolated bubble regime. Support vector machine regression (SVMReg), alternating model tree (ADTree), and multilayer perceptron (MLP) regression only used the heat transfer equation input parameters without heat transfer equations for prediction of pool boiling heat transfer over a horizontal tube. The performance of computational intelligence methods were determined according to the results of error analysis. Mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) error were used to calculate the validity of the predictive model in genetic algorithm, ABC algorithm, SVMReg, MLP regression, and alternating model tree. According to the MAPE error analysis, the accuracy values of MLP regression (0.23) and alternating model tree (0.22) methods were the same. The SVMReg method used for pool boiling heat flux estimation performed better than the other methods, with 0.17 validation error rate of MAPE.

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Section: Alternating Decision Treementioning

confidence: 99%

Heat Flux Estimation at Pool Boiling Processes with Computational Intelligence Methods

2019

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“…The online model-based approach relies on measurements collected online together with the process model. Depending on the type of optimization problem, the online model-based approach can be further classified as steady-state real-time optimization (SRTO) [15] and dynamic real-time optimization (DRTO) [16] or economic model predictive control (EMPC) [17]. Due to the steady-state wait time for SRTO and computation complexity of DRTO and EMPC, the convergence rate for the online model-based approach is also slow.…”

Section: Introductionmentioning

confidence: 99%

A Retrofit Hierarchical Architecture for Real-Time Optimization and Control Integration

Xie

et al. 2020

Processes

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To achieve the optimal operation of chemical processes in the presence of disturbances and uncertainty, a retrofit hierarchical architecture (HA) integrating real-time optimization (RTO) and control was proposed. The proposed architecture features two main components. The first is a fast extremum-seeking control (ESC) approach using transient measurements that is employed in the upper RTO layer. The fast ESC approach can effectively suppress the impact of plant-model mismatch and steady-state wait time. The second is a global self-optimizing control (SOC) scheme that is introduced to integrate the RTO and control layers. The proposed SOC scheme minimizes the global average loss based on the approximation of necessary conditions of optimality (NCO) over the entire operating region. A least-squares regression technique was adopted to select the controlled variables (CVs) as linear combinations of measurements. The proposed method does not require the second order derivative information, therefore, it is numerically more reliable and robust. An exothermic reaction process is presented to illustrate the effectiveness of the proposed method.

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“…Currently, there are many different ways to increase the reservoir productivity which is at the third stage of development. Among the main methods, the following ones can be singled out (Krishnamoorthy et al 2016;Feng et al 2012;Xiaogang et al 2010;Ren et al 2007;Zhu et al 2009;Huang et al 2012;Liao et al 2003;Biagi et al 2014):…”

Section: Introductionmentioning

confidence: 99%

Mathematical modelling of enhanced gas production

Yakovlev

Kalugin

et al. 2018

J Petrol Explor Prod Technol

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Based on a mathematical model for 2D filtration of a two-phase multicomponent hydrocarbon mixture, the boundary problem of optimizing distribution of well flow rates was for the first time composed and solved so as to maximize the gas recovery from a natural gas reservoir at its final stage of development. The input data used in the model are the planned gas production in the reservoir and reservoir characteristics (its thickness, porosity, and permeability). The flow rate optimization in individual wells is carried out in such a way that the pressure drop is kept constant for the recharge area of each well. The proposed optimization algorithm takes into account both the restriction on the flow rates in certain wells and their shutoffs for remedial maintenance. The own computer program has been developed to solve the problem. The efficiency of this approach is illustrated by the example of the natural gas reservoir Kotelevske (Ukraine). It is shown that the control of the pressure equalizing process in the recharge area of each well leads to the decrease in differential pressure inside the whole reservoir. As a result, the 6% increase in gas production was possible for the optimum alternative in a shorter period of time. Keywords Natural gas reservoir • Enhanced gas production • Optimization of reservoir development • Mathematical model n , n Coordinates of the n th well, m g , w Gas and liquid phase viscosities, respectively, kg/m c g , w Gas and liquid phase densities, respectively, g/m 3 std g,k Density of the k th component under standard conditions, g/m 3 std g

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Real-Time Optimization under Uncertainty Applied to a Gas Lifted Well Network

Cited by 59 publications

References 25 publications

Heat Flux Estimation at Pool Boiling Processes with Computational Intelligence Methods

Heat Flux Estimation at Pool Boiling Processes with Computational Intelligence Methods

A Retrofit Hierarchical Architecture for Real-Time Optimization and Control Integration

Mathematical modelling of enhanced gas production

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