One of the strategies to achieve Indonesia's main goal to produce one million barrels oil per day in 2030 is to maintain existing production volume. The key of maintain the existing production is to optimize artificial lift performance used in oil wells, because 96% of oil wells in Indonesia had installed artificial lifts and their performance will significantly affect the production decline rate. This approach aims to create a simple data visualization from macro perspective, to evaluate the artificial lift performance of all oil wells in Indonesia and to find a solution to optimize their performance.

This method is started by collecting the main parameters that describes the artificial lift performance such as artificial lift type, historical run life, historical operating cost, production rate, reservoir depth, type of fluid as well as additional issues from each field in Indonesia. After the data is gathered, the next step is to cluster the usage of various artificial lifts in Indonesia, which have similarities such as area, crude type, depth, rate, and operational problems, in terms of comparison between the optimum case and non-optimum one. Finally, from the non-optimum one, it will be evaluated on more detailed programs for further optimization. This evaluation process is carried out by visualizing all the data gathered using some informative dashboards. The digitalization is expected to help the improvement of evaluation time and to support decision processes.

By implementing this method, several success cases were demonstrated in 2020, like optimizing Sucker Rod Pump (SRP) component in one of the fields in Sumatra, with the gain around 120 BOPD, Gas lift and SRP to Electric Submersible Pump (ESP) conversion in one of the fields in Kalimantan with 160 BOPD production outcome, switching normal ESP rate to lower rate ESP which resulted from double run life compared with the previous one, and also conversion from SRP to HPU that can extend its run life, while creating cost efficiency. From those results, it shows the benefit of the dashboards created for artificial lift optimization, especially from Government point of view. Furthermore, there are around 50 wells that will be evaluated in detail for optimization program.

The visual analytics of the dashboards, for example, will help the evaluation process all at once providing positive impacts on artificial lift optimalization program. In the future, we hope that these dashboards could be developed further, by combining the implementation of machine learning, like fuzzy logic methods or neural network, to enhance the operator performance and to improve production efficiency toward the achievement of one million barrels oil per day in 2030.

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