Online optimization for a plunger lift process in shale gas wells

•A computationally efficient algorithm for online optimization of plunger lift process in a shale gas well is presented.•The natural gas production is maximized while meeting the requirements on the average plunger rise velocity.•The binary manipulated variable is transformed into cycle-wise control...

Full description

Saved in:
Bibliographic Details
Published in:Computers & chemical engineering Vol. 108; pp. 89 - 97
Main Authors: Nandola, Naresh N., Kaisare, Niket S., Gupta, Arun
Format: Journal Article
Language:English
Published: Elsevier Ltd 04.01.2018
Subjects:
Hybrid system
Model predictive control
Multi-objective optimization
Plunger lift
Production optimization
Reduced order model
Plunger lift
Production optimization
Model predictive control
Multi-objective optimization
Reduced order model
Hybrid system
ISSN:0098-1354, 1873-4375
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•A computationally efficient algorithm for online optimization of plunger lift process in a shale gas well is presented.•The natural gas production is maximized while meeting the requirements on the average plunger rise velocity.•The binary manipulated variable is transformed into cycle-wise control threshold values and a reduced order model is identified between these transformed input variables and output objectives.•The proposed algorithm is implementable for both batch and periodic systems, with varying cycle time. This paper presents a method for efficient optimization of a plunger lift process in shale gas wells. Plunger lift is a cyclic process consisting binary decision as well as continuous and discrete state variables. The time-series data comprising of surface measurements are converted into cycle-wise process-relevant performance outputs, while the binary manipulated variable is transformed into continuous threshold values. These transformed variables are used to develop a reduced order cycle-to-cycle model and corresponding receding horizon optimization problem that maximizes daily production while meeting operational constraints. The efficacy of the proposed algorithm is demonstrated on a simulated plunger lift process.
ISSN:0098-1354
1873-4375
DOI:10.1016/j.compchemeng.2017.09.001