Modelling and Extremum Seeking Control of Gas Lifted Oil Wells

Peixoto, Alessandro Jacoud; Pereira-Dias, Diego; Xaud, Arthur F.S.; Secchi, Argimiro Resende

doi:10.1016/j.ifacol.2015.08.004

Cited by 23 publications

(17 citation statements)

References 10 publications

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“…The considered operational conditions for all Ribeiro' model [35] simulations were the same ones described by Peixoto et al [41], while simulations performed with the Rojas Soares et al model [36] were the same described by Jahanshahi et al [32]. The well and reservoir parameters used in all simulations are shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Extracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Process

Dias¹,

Soares

Jäschke

et al. 2019

Processes

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The present work investigated the use of an echo state network for a gas lift oil well. The main contribution is the evaluation of the network performance under conditions normally faced in a real production system: noisy measurements, unmeasurable disturbances, sluggish behavior and model mismatch. The main pursued objective was to verify if this tool is suitable to compose a predictive control scheme for the analyzed operation. A simpler model was used to train the neural network and a more accurate process model was used to generate time series for validation. The system performance was investigated with distinct sample sizes for training, test and validation procedures and prediction horizons. The performance of the designed ESN was characterized in terms of slugging, setpoint changes and unmeasurable disturbances. It was observed that the size and the dynamic content of the training set tightly affected the network performance. However, for data sets with reasonable information contents, the obtained ESN performance could be regarded as very good, even when longer prediction horizons were proposed.

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Section: Resultsmentioning

confidence: 99%

Extracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Process

Dias¹,

Soares

Jäschke

et al. 2019

Processes

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“…In terms of modelling, gas lifted well models were developed and studied in [18][19][20] to name a few. The dynamic models used in all these works are based on the mass balance between the different phases in the well tubing and annulus.…”

Section: Previous Workmentioning

confidence: 99%

“…For example, the frictional pressure drop in pipes have been assumed to be negligible in [18], whereas some other works explicitly include the frictional pressure drop term. Some works consider simple linear reservoir inflow models such as in [12,20], whereas nonlinear reservoir inflow models have been used in [19]. Some works, such as [22,23], consider partial differential equations for the pressure and flow dynamics in the pipe, which are discretized and solved, whereas ordinary differential equations for mass balance have been used in many other works.…”

Section: Previous Workmentioning

confidence: 99%

“…For the application considered in the paper, we know that the worst case scenario occurs when the GOR of all the wells takes its maximum realization simultaneously. Therefore, we simply choose the worst case GOR as shown in Equation (19). To avoid further confusion, we call this approach "worst case optimization" instead of "robust optimization".…”

Section: Optimization Under Uncertaintymentioning

confidence: 99%

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

2016

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Abstract:In this work, we consider the problem of daily production optimization in the upstream oil and gas domain. The objective is to find the optimal decision variables that utilize the production systems efficiently and maximize the revenue. Typically, mathematical models are used to find the optimal operation in such processes. However, such prediction models are subject to uncertainty that has been often overlooked, and the optimal solution based on nominal models can thus render the solution useless and may lead to infeasibility when implemented. To ensure robust feasibility, worst case optimization may be employed; however, the solution may be rather conservative. Alternatively, we propose the use of scenario-based optimization to reduce the conservativeness. The results of the nominal, worst case and scenario-based optimization are compared and discussed.

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“…The unique optimum point and the uncertainty in the production curves makes this problem an ideal candidate for extremum seeking methods. In fact, using extremum seeking optimization for individual wells was proposed in [13], while some practical issues related to this application were addressed in [8].…”

Section: Introductionmentioning

confidence: 99%

Practical extremum-seeking control for gas-lifted oil production

Павлов

Haring

Fjalestad

2017

2017 IEEE 56th Annual Conference on Decision and Control (CDC)

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We present a distributed extremum seeking algorithm for the problem of production optimization of multiple gas lifted wells. The algorithm is based on "synchronization" of production performance gradients for all individual wells. It mimics the manual optimization method employed by production engineers in industry. Thus due to better understanding by industrial specialists, this method may have higher chances of being accepted in the oil and gas industry compared to other data-driven optimization methods. Performance of the proposed algorithm is illustrated by simulations.

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Modelling and Extremum Seeking Control of Gas Lifted Oil Wells

Cited by 23 publications

References 10 publications

Extracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Process

Extracting Valuable Information from Big Data for Machine Learning Control: An Application for a Gas Lift Process

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

Practical extremum-seeking control for gas-lifted oil production

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