Increasing Production Using Microprocessors and Tracking Plunger-Lift Velocity

Morrow, Stanley J.; Rogers, Jeff

doi:10.2118/24296-ms

Cited by 4 publications

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

Singh

2015

Day 2 Tue, September 15, 2015

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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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Improved Data Mining for Production Diagnosis of Gas Wells with Plunger Lift through Dynamic Simulations

Zhu

Cao

Tian

et al. 2019

SPE Annual Technical Conference and Exhibition

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Plunger lift has been widely used in unconventional gas wells to remove liquid accumulation from the well.. Production surveillance provides large amount of data of production process and normal and abnormal operations, which can be used in machine learning (ML) and Artificial Intelligence (AI) to develop algorithms for anomaly diagnosis and operation optimization. However, in the surveillance data the majority is related to daily operation and the data of failure cases are rare. Also the failure cases may not be repeatable and many failure case signatures are not available until they happen. Large data size of anomaly cases are needed to improve the ML model accuracy. Dynamic simulation of the plunger lift process offers an alternative way to generate synthetic data on the specified anomalies to be used to train the ML model. It also helps better understand the trends reflected in the surveillance data and their root causes. From the available surveillance data of gas wells equipped with plunger lift, the simultaneous measurements of different parameters at different points in a production system with normal and abnormal occurrences can be analyzed and the correspondent trends/signatures can be identified. The typical signatures that conform to pre-determined anomalous patterns can be obtained. Using a commercial transient multiphase flow simulator, the actual field data of tubing/casing pressures can be matched through a tuning process. Trial-and-error is needed to improve the dynamic plunger lift model so that a good agreement with the production data can be achieved by adjusting the reservoir performance, plunger parameters or surface pipeline boundary conditions. Following the validation under different flow conditions, synthetic datasets for various operational and flow conditions can be generated by performing parametric studies. Unlike the field data, the synthetic data from the dynamic simulations mainly comprise anomaly signatures (e.g. tubing rupture, missed arrival of plunger, etc.), which can be added to the ML data pool to reduce the data covariance and increase independency.

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Increasing Production Using Microprocessors and Tracking Plunger-Lift Velocity

Cited by 4 publications

References 0 publications

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

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

Improved Data Mining for Production Diagnosis of Gas Wells with Plunger Lift through Dynamic Simulations

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