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

Abstract

Abstract 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.