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Real‐time multivariable model predictive control for steam‐assisted gravity drainage
AIChE Journal ( IF 3.7 ) Pub Date : 2018-02-06 , DOI: 10.1002/aic.16098
Sagar N. Purkayastha 1 , Ian D. Gates 1 , Milana Trifkovic 1
Affiliation  

Thermal recovery techniques, such as steam‐assisted gravity drainage (SAGD), are used to recover the majority of the crude bitumen, in Western Canada. However, suboptimal production techniques have led to a large carbon footprint and a subsequent search for more efficient extraction techniques, than open loop manual control. This article summarizes research on the comparison of performance of a novel multi‐input multioutput (MIMO) model predictive controller (MPC) with steam trap and oil rate controls with a multi‐input single output (MISO) MPC with only steam trap control. An appropriate system identification technique was also used for periodic model update in compliance with changing system behavior. The real‐time control study was made possible by establishing a bidirectional communication between computer modeling group STARSTM (virtual reservoir) and MATLAB (onsite controller) software. The results show a 171% improvement in oil recovery for the novel MIMO MPC over the MISO MPC. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3034–3041, 2018

中文翻译:

蒸汽辅助重力排水的实时多变量模型预测控制

热回收技术,例如蒸汽辅助重力排水(SAGD),用于回收加拿大西部的大部分原油沥青。但是,次优生产技术导致了较大的碳足迹,并且随后寻求比开环手动控制更有效的提取技术。本文总结了比较新型带疏水阀的多输入多输出(MIMO)模型预测控制器(MPC)和仅带疏水阀控制的多输入单输出(MISO)MPC的油率控制的性能的研究。适当的系统识别技术还用于根据更改的系统行为定期更新模型。通过在计算机建模小组STARS之间建立双向通信,可以进行实时控制研究TM(虚拟水库)和MATLAB(现场控制器)软件。结果表明,与MISO MPC相比,新型MIMO MPC的采油量提高了171%。©2018美国化学工程师学会AIChE J,64:3034–3041,2018
更新日期:2018-02-06
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