Abstract

Though representing only a small percentage of the artificial lift systems in their fleet, electric submersible pump (ESP) repairs are an incredibly expensive issue at Devon Energy. In an effort to better understand ESP behavior and potentially delay these failures in the future, the Advanced Analytics team and the Production Operations team collaborated to statistically identify the key drivers behind ESP failures and determine if it was possible to accurately predict an ESP's lifespan using predictive model techniques. Continuous time series data from PI was summarized over ESP lifetime and combined with static descriptive data from Wellview in fifty-three ESPs across the Delaware Basin. Data exploration was performed in SAS Enterprise Guide before a number of predictive models were created in SAS Enterprise Miner.

Model competition was performed using a variety of modeling types such as linear regression, decision trees, and high performance random forests (HP Forest). The best model, the HP Forest model, was selected based on average square error. The HP Forest model predicted ESP lifespans which were, on average, within approximately five days of the true ESP lifespan. 90% of the model's predictive error were within +/- 30 days of the true ESP lifespan. The top three variables of importance when predicting ESP lifespan were metrics related to ESP shutdowns. Other notable variables included those related to proppant size and amount. These results prove that it is possible to create an appreciably accurate statistical model to predict ESP lifetime using static summarized data. After further standardization and optimization, this model may be operationalized in the future. This modeling process may also serve as the basis for future modeling exercises using unsummarized continuous time series data. Key driver analysis highlighted the influence that ESP shutdowns have on the lifespan on an ESP. Since ESPs can be shut down as a response to mechanical or human interference, analysis of ESP shutdowns using pattern recognition analysis and chaos theory may be performed in the future.

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