Gas-lift Optimization and Control with Nonlinear MPC

Plucenio, Agustinho; Pagano, Daniel J.; Camponogara, Eduardo; Traple, A.; Teixeira, Anne Kayline Soares

doi:10.3182/20090712-4-tr-2008.00148

Cited by 16 publications

(6 citation statements)

References 14 publications

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“…Several papers have been published about gas-lift wells modeling and control as in Eikrem et al (2004), Hu (2004), Imsland et al (2003) and Plucenio et al (2009). In Camponogara et al (2010) an optimization and control strategy was developed to distribute the gas arriving in a gas-lift manifold (GLM) shown in figure 1 following a set of objectives:…”

Section: Controlling Gas-lift Well Dynamicsmentioning

confidence: 99%

Stabilizing gas-lift well dynamics with free operating point

Plucenio

Ganzaroli

Pagano

2012

IFAC Proceedings Volumes

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Section: Controlling Gas-lift Well Dynamicsmentioning

confidence: 99%

Stabilizing gas-lift well dynamics with free operating point

Plucenio

Ganzaroli

Pagano

2012

IFAC Proceedings Volumes

Self Cite

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“…3 The gas lift optimization problem for oil production is an example. 4,5 Determining the optimal amount of gas lift typically requires a rigorous process model to account for the production rate changes based on the amount of gas-lift injection. Thus, such a model must capture the system's complexity and the problem's nature, as shown in Figure 1, where increases in the gas-lift injection rate are economically attractive until friction pressure rises and production decreases.…”

Section: Introductionmentioning

confidence: 99%

Gas-Lift Optimization Using Physics-Informed Deep Reinforcement Learning

de Rezende Faria,

Capron,

Secchi

et al. 2024

Ind. Eng. Chem. Res.

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Real-time optimization (RTO) methodologies have become essential for optimal process operation in the oil and gas industries. Typically, RTO is based on a steady-state model (steady-state real-time optimization�SSRTO) and operates as a closed-loop optimizer. However, this technique can result in suboptimal policies due to steady-state waiting and plant-model mismatch issues. To alleviate these problems, we combine this closed-loop optimizer with a data-driven residual optimizer based on deep reinforcement learning (DRL). The idea is to use the SSRTO solution's stability and convergence properties while reducing plant-model mismatch through a residual DRL controller that can tune the optimal inputs calculated by the SSRTO based on transient measurements (i.e., real data). Our proposed methodology delivered robust online control policies compared to dynamic real-time optimization (DRTO) for closed-loop control, even when the plant-model mismatch was simulated by introducing parameter uncertainty and noise in the plant and using an SSRTO model with structural uncertainty. As a result, its economic gain was improved compared to the SSRTO solution, with an average computational cost 11 times lower than DRTO, making it suitable for online applications.

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“…Willersrud et al (2012) maximizaram a produção de óleo por uma estratégia de controle em duas camadas, um MPC não linear (NMPC) e uma camada de otimização. Plucenio et al (2009) utilizaram um NMPC para controlar um manifold de gás de elevação e os poços a ele conectados. O uso de técnicas de controle avançado em processos de compressão de gás também se mostra promissor, uma vez que esses processos são intrinsecamente multivariáveis e possuem restrições de operação para se evitar, por exemplo, o surge (Budnis e Thornhill, 2015).…”

Section: Introductionunclassified

Estratégia de Controle Preditivo Multivariável para um Sistema de Compressão de Gás de Plataforma do Pré-Sal

Thomaz¹,

Secchi²,

Souza³

2018

Blucher Chemical Engineering Proceedings

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RESUMO -No presente trabalho, construiu-se um modelo matemático de uma planta de compressão de gás offshore, com destino de exportação via gasodutos, para avaliar estratégias de controle e otimização do consumo energético do sistema respeitando as restrições do mesmo. Uma combinação do controle clássico, feito por PIDs, com controle avançado, MPC (Model Predictive Control), foi proposta e os modelos internos do MPC e aqueles aplicados à sintonia dos PID foram obtidos via identificação de formas lineares. Obteve-se um MPC capaz de manter a estabilidade do processo e respeitar suas restrições, sem, contudo, utilizar ações de controles bruscas e indesejadas.

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Gas-lift Optimization and Control with Nonlinear MPC

Cited by 16 publications

References 14 publications

Stabilizing gas-lift well dynamics with free operating point

Stabilizing gas-lift well dynamics with free operating point

Gas-Lift Optimization Using Physics-Informed Deep Reinforcement Learning

Estratégia de Controle Preditivo Multivariável para um Sistema de Compressão de Gás de Plataforma do Pré-Sal

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