Application of Data Mining for Quick Root-Cause Identification and Automated Production Diagnostic of Gas Wells With Plunger Lift
- Ajay Singh (Halliburton)
- Document ID
- Society of Petroleum Engineers
- SPE Production & Operations
- Publication Date
- August 2017
- Document Type
- Journal Paper
- 279 - 293
- 2017.Society of Petroleum Engineers
- Data mining, regression tree analysis, plunger lift, production diagnostic, cross-validation
- 0 in the last 30 days
- 357 since 2007
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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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