Optimization of Plunger-Lift Performance in Liquid Loading Gas Wells

Özkan, Erdal; Keefer, Brian; Miller, M.G.

doi:10.2118/84135-ms

Cited by 3 publications

References 10 publications

(11 reference statements)

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Enhanced Plunger Lift Performance Utilizing Reservoir Modeling

et al. 2013

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Liquid loading is an important determinant for the performance of tight, unconventional gas wells. Although plunger lift is one of the best solutions when applicable, marginal economics of unconventional gas production does not tolerate un-optimized plunger lift performance. In this paper a reservoir-performance based algorithm proposed by Ozkan et al. (2003) is applied to optimize the gas production and shut-in periods of plunger lift operation. The objective of the optimization is to maximize gas production with the condition that the liquid loaded during production can be lifted to the surface by the pressure that builds up during the following shut-in period. Unlike the current plunger lifts that work in time mode (like timer clock), this algorithm is based on pressure (pressure mode). The optimization algorithm combines the conventional plunger-lift theory with an analytical description of the reservoir performance. The conventional plunger lift theory provides the pressure required for lifting the plunger with a liquid column on top of it, and the analytical reservoir model is used to optimize the sequence of production and shut-in times. The optimization algorithm presented in this paper improves the economics of unconventional gas production not only by increasing gas production but also automating the process by which optimization is achieved. Another advantage of the proposed method is the ability to automatically adjust to changes in the line pressure. A case study is presented to demonstrate that the proposed automated model can achieve the same optimization performance as that implemented by expert technicians.

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Enhanced Plunger Lift Performance Utilizing Reservoir Modeling

et al. 2013

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Root-Cause Identification and Production Diagnostic for Gas Wells with Plunger Lift

Singh

2015

Day 2 Tue, September 15, 2015

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The paper presents an application of the regression tree technique as a root-cause identification and production diagnostic tool, and presents case studies for gas wells using the plunger-lift system. The regression tree model is data-driven and easy to construct, and does not require all of the parameters that a first principle-based model often requires. Thus, these models are quite useful in cases where real-time data or the required data for a detailed analysis are unavailable. To improve prediction capability, a cross-validation technique was used to optimize tree size. A total of 8–10 variables that could potentially impact the gas production rate were considered, with case studies conducted on actual field data. Questions, such as which group of wells are performing well or poorly, are quickly answered. Regression tree analysis, when applied at the field level, can identify a group of wells that underperform compared to other wells. As a result, an asset team can prioritize wells identified as poor performers. Further, a statistical analysis helps to gain insight into understanding the causal relationship between the gas production rate and various operational variables. Based on this analysis, recommendations are also provided to improve the gas production rate of poorly performing wells. Individual well models were also constructed to identify the root causes for high or low gas production based on operational changes made over a certain period of time. Only a few of the variables were found to have a significant impact on gas production. The interpretation of the decision tree indicated that additional operational variables, such as production time and pressure build-up time, should be included in the regression tree analysis to improve the diagnostic process. This paper summarizes these conclusions and recommends future work to improve the prediction capability of the regression tree analysis.

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Application of Data Mining for Quick Root-Cause Identification and Automated Production Diagnostic of Gas Wells With Plunger Lift

Singh

2017

SPE Production &Amp; Operations

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Summary The paper presents an application of the classification-and-regression-tree (CART) technique as a root-cause identification and production diagnostic tool, and presents case studies from real field data for gas wells using the plunger-lift system. Specifically, regression-tree analysis was performed on the basis of the available operation data. The regression-tree model is data-driven and easy to construct, and does not require all the parameters that a first-principles-based model often requires. Thus, these models are quite useful in cases where real-time data or the required data for a detailed analysis are unavailable. To improve prediction capability, a cross-validation technique was used to optimize tree size. A total of 8 to 10 variables that could potentially affect the gas-production rate was considered, with case studies conducted on actual field data. Questions, such as which group of wells is performing well or poorly, are quickly answered. Regression-tree analysis, when applied at the field level, can identify a group of wells that underperform compared with other wells. As a result, an asset team can prioritize wells identified as poor performers. Further, a statistical analysis helps to gain insight into understanding the causal relationship between the gas-production rate and various operational variables. On the basis of this analysis, recommendations are also provided to improve the gas-production rate of poorly performing wells. Individual-well models were also constructed to identify the root causes for high or low gas production on the basis of operational changes made during a certain period of time. Only a few of the variables were found to have a significant impact on gas production. The interpretation of the decision tree indicated that additional operational variables, such as production time and pressure-buildup time, should be included in the regression-tree analysis to improve the diagnostic process. This paper summarizes these conclusions and recommends future work to improve the prediction capability of the regression-tree analysis.

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Optimization of Plunger-Lift Performance in Liquid Loading Gas Wells

Cited by 3 publications

References 10 publications

Enhanced Plunger Lift Performance Utilizing Reservoir Modeling

Enhanced Plunger Lift Performance Utilizing Reservoir Modeling

Root-Cause Identification and Production Diagnostic for Gas Wells with Plunger Lift

Application of Data Mining for Quick Root-Cause Identification and Automated Production Diagnostic of Gas Wells With Plunger Lift

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