Research Article
Optimization of Oil Well Operations with Machine Learning Model to predict Well Production at Company "XYZ"
@INPROCEEDINGS{10.4108/eai.21-9-2023.2342918, author={Yasser Arapat and Juni Nurma Sari and Satria Perdana Arifin and Yohana Dewi Lulu Widyasari}, title={Optimization of Oil Well Operations with Machine Learning Model to predict Well Production at Company "XYZ"}, proceedings={Proceedings of the 11th International Applied Business and Engineering Conference, ABEC 2023, September 21st, 2023, Bengkalis, Riau, Indonesia}, publisher={EAI}, proceedings_a={ABEC}, year={2024}, month={2}, keywords={petroleum reservoir arima ann}, doi={10.4108/eai.21-9-2023.2342918} }- Yasser Arapat
Juni Nurma Sari
Satria Perdana Arifin
Yohana Dewi Lulu Widyasari
Year: 2024
Optimization of Oil Well Operations with Machine Learning Model to predict Well Production at Company "XYZ"
ABEC
EAI
DOI: 10.4108/eai.21-9-2023.2342918
Abstract
PT XYZ is a national oil company located in Minas, Siak Regency, Riau, Indonesia. Over time, the oil production process has decreased due to several reasons, one of which is the lack of pressure from the production well. So that at this time the wells that have decreased production are treated by injecting produced water into the reservoir using injection wells with the aim of increasing reservoir pressure so that the oil contained at the bottom of the earth will rise to the surface of the earth. Water that is produced with oil will be treated and injected back into the reservoir. Produced water is treated in accordance with the characteristics of the water contained in the reservoir, which, if different, will damage the reservoir. This research will discuss the application of data mining methods in the well production prediction process. The methods used are ARIMA and ANN, and the testing is done using RMSE and MAE. The results of this study show that the ANN method has higher accuracy results. This can be seen from the average accuracy results of each production with several treatments.