Model of Machine Learning for Prediction and Optimization of Oil and Gas Operating Costs in Indonesia

Adhanto Bagaskoro; Ardian Nengkoda; Andy Noorsaman Sommeng

doi:10.55927/fjst.v3i6.9687

Authors

Adhanto Bagaskoro Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia
Ardian Nengkoda Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia
Andy Noorsaman Sommeng Department of Chemical Engineering, Faculty of Engineering, Universitas Indonesia

DOI:

https://doi.org/10.55927/fjst.v3i6.9687

Keywords:

Predictive Modeling, Cost Optimization, Machine Learning, Oil and Gas

Abstract

This study leverages machine learning techniques to predict and optimize operational expenditures (OPEX) in Indonesia's oil and gas industry. By analyzing historical data from Work Plan and Budget (WP&B) reports from 2017, the research identifies key factors influencing OPEX, such as production location, oil characteristics, and development stages. The Random Forest model demonstrated the highest predictive accuracy with an R-squared value of 0.92 and Mean Squared Error (MSE) of 4.5. The findings highlight significant cost-saving opportunities, particularly in Kalimantan and Papua. These insights support strategic planning and decision-making, emphasizing the transformative potential of machine learning in enhancing operational efficiency and sustainability in the oil and gas sector.

Downloads

Download data is not yet available.

References

Ahmed, K., Zhang, Y., & Li, X. (2018). Predicting cost overruns in oil and gas projects using machine learning algorithms. Journal of Petroleum Science and Engineering, 170, 377-388. https://doi.org/10.1016/j.petrol.2018.06.024

Azizurrofi, A., Asnidar, A., Simanjuntak, J., & Firdaus, R. R. (2017). Statistical analysis and mapping of oil and gas operating costs based on field development plans in Indonesia. In SPE/IATMI Asia Pacific Oil & Gas Conference and Exhibition. https://doi.org/10.2118/186346-MS

Belch, G. E., & Belch, M. A. (2003). Advertising and promotion: An integrated marketing communications perspective (6th ed.). McGraw-Hill/Irwin.

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.

Berman, B., & Evans, J. R. (2013). Retail management: A strategic approach (12th ed.). Pearson.

Brown, J., & Davis, R. (2018). Cost management in the exploration and production sector: A machine learning approach. Energy Economics, 76, 50-60. https://doi.org/10.1016/j.eneco.2018.08.025

Chen, Q., Liu, Y., & Zhang, Z. (2020). Machine learning for cost optimization in drilling operations. Journal of Energy Resources Technology, 142(11), 1-10. https://doi.org/10.1115/1.4047995

Hennig-Thurau, T., & Klee, A. (1997). The impact of customer satisfaction and relationship quality on customer retention: A critical reassessment and model development. Psychology & Marketing, 14(8), 737-764. https://doi.org/10.1002/(SICI)1520-6793(199712)14:8<737:AID-MAR2>3.0.CO;2-F

Jones, T. O., & Sasser, W. E. (1995). Why satisfied customers defect. Harvard Business Review, 73(6), 88-99.

Kotler, P., & Keller, K. L. (2012). Marketing management (14th ed.). Pearson Education.

Monroe, K. B. (1973). Buyers' subjective perceptions of price. Journal of Marketing Research, 10(1), 70-80. https://doi.org/10.2307/3149411

Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41-50. https://doi.org/10.1177/002224298504900403

Smith, R., Johnson, P., & Walker, T. (2019). Impact of oil characteristics on production costs: A machine learning perspective. Fuel, 236, 143-150. https://doi.org/10.1016/j.fuel.2018.08.121

Varki, S., & Colgate, M. (2001). The role of price perceptions in an integrated model of behavioral intentions. Journal of Service Research, 3(3), 232-240. https://doi.org/10.1177/109467050133004

Xia, L., Monroe, K. B., & Cox, J. L. (2004). The price is unfair! A conceptual framework of price fairness perceptions. Journal of Marketing, 68(4), 1-15. https://doi.org/10.1509/jmkg.68.4.1.42733

Zhang, Y., Wang, H., & Li, X. (2020). Machine learning-based prediction of oil and gas project costs. Energy, 200, 117548. https://doi.org/10.1016/j.energy.2020.117548