Oil and Gas Production Forecasting Based on LSTM Model: A Case Study of PT Pertamina Hulu Rokan Zone 4

  • Angel Caroline Billan Bina Darma University, Indonesia
  • Usman Ependi Bina Darma University, Indonesia
DOI: 10.63158/journalisi.v7i4.1285
Keywords: Attention Mechanism, LSTM, MAPE, Oil and Gas Production Forecasting, RMSE

Abstract

This study addresses the critical need for accurate oil and gas production forecasting to support strategic decision-making in Indonesia’s energy sector. PT Pertamina Hulu Rokan Zone 4 (PHR Zona 4), a key player in national energy production, frequently encounters technical and external operational challenges. To tackle these issues, this research proposes a deep learning-based predictive model using the Long Short-Term Memory (LSTM) architecture, structured in an encoder-decoder format and enhanced with an attention mechanism. The model was trained and tested on historical oil and gas production data from PHR Zona 4, evaluated under two data-splitting scenarios: 80:20 and 90:10. Model performance was assessed using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Results from the 80:20 scenario showed RMSE of 5.83, MAE of 5.54, MAPE of 1.71%, and R² of -1.97, suggesting difficulties in capturing extreme data fluctuations. However, the 90:10 scenario demonstrated significantly improved performance with RMSE of 0.42, MAE of 0.36, MAPE of 0.11%, and R² of 0.00, indicating better trend prediction stability. The novelty of this study lies in the integration of attention mechanisms within the LSTM encoder-decoder framework for oil and gas time series forecasting, offering enhanced accuracy and robustness. This research provides a valuable foundation for future improvements in predictive analytics and operational efficiency in the oil and gas industry.

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References

J. Elvis, E. Suparman, I. I.-I. L. Review, and undefined 2023, “Hukum Pengelolaan Sumber Daya Minyak Dan Gas Bumi Menurut Pasal 33 Undang-Undang Dasar Tahun 1945,” Iblam Law Review, vol. 3, no. 3, pp. 14–29, 2023, doi: 10.52249.

A. Almas, “Analisis Kinerja Algoritma Deep Neural Network dalam Melakukan Prediksi Laju Alir Produksi Minyak pada Lapangan X PT. Pertamina Hulu Rokan,” 2024.

M. Faiz Haikal et al., “Dampak Fluktuasi Pendapatan pada Tingkat Penjualan di Perusahaan BTC Watampone dalam Perspektif Ekonomi Mikro Islam,” Jurnal Inovasi Ekonomi Syariah dan Akuntansi, vol. 1, no. 4, pp. 24–33, Jun. 2024, doi: 10.61132/JIESA.V1I4.231.

P. N. Yulisa, M. Al Haris, and P. R. Arum, “Peramalan Nilai Ekspor Migas di Indonesia dengan Model Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU),” J Statistika: Jurnal Ilmiah Teori dan Aplikasi Statistika, vol. 16, no. 1, pp. 328–341, Jul. 2023, doi: 10.36456/JSTAT.VOL16.NO1.A6121.

K. A. Rijal, A. V. Vitianingsih, Y. Kristyawan, A. L. Maukar, and S. F. A. Wati, “Forecasting Model of Indonesia’s Oil & Gas and Non-Oil & Gas Export Value using Var and LSTM Methods,” Jurnal Teknologi dan Manajemen Informatika, vol. 10, no. 1, pp. 59–69, Jun. 2024, doi: 10.26905/JTMI.V10I1.13127.

A. Arfan and ; Lussiana, “Perbandingan algoritma long short-term memory dengan SVR pada prediksi harga saham di Indonesia,” PETIR: Jurnal Pengkajian dan Penerapan Teknik Informatika, vol. 13, no. 1, pp. 33–43, 2020, doi: 10.33322/petir.v13i1.858.

A. Alqahtani, M. Ali, X. Xie, and M. W. Jones, “Deep Time-Series Clustering: A Review,” Electronics (Basel), vol. 10, no. 23, p. 3001, Dec. 2021, doi: 10.3390/ELECTRONICS10233001.

J. Nurhakiki et al., “Studi Kepustakaan: Pengenalan 4 Algoritma Pada Pembelajaran Deep Learning Beserta Implikasinya,” Pendekar: Jurnal Pendidikan Berkarakter, vol. 2, no. 1, pp. 270–281, 2024, doi: 10.51903/pendekar.v2i1.598.

B. Penelitian et al., “Penentuan Nilai Terkecil Root Mean Squared Error (RMSE) Metode Holt-Winters Exponential Smoothing Pada Ekspor Kopi Tujuan Jerman,” MathVision: Jurnal Matematika, vol. 6, no. 1, pp. 37–42, 2024.

I. Nabillah, I. R.-J. of I. System, and undefined 2020, “Mean absolute percentage error untuk evaluasi hasil prediksi komoditas laut,” Journal of Information System, vol. 5, no. 2, pp. 250–255.

Marie Luthfi Ashari and Mujiono Sadikin, “Prediksi Data Transaksi Penjualan Time Series Menggunakan Regresi Lstm,” Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI, vol. 9, no. 1, pp. 1–10.

L. Wiranda and M. Sadikin, “Penerapan Long Short Term Memory Pada Data Time Series Untuk Memprediksi Penjualan Produk PT. Metiska Farma,” Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI, vol. 8, no. 3, pp. 184–196, 2019, doi: 10.23887/JANAPATI.V8I3.19139.

M. Hussein and Y. Azhar, “Prediksi Harga Minyak Dunia Dengan Metode Deep Learning,” Fountain of Informatics Journal, vol. 6, no. 1, pp. 29–34, 2021, doi: 10.21111/FIJ.V6I1.4446.

Ferdiansyah, S. H. Othman, R. Zahilah Raja Md Radzi, D. Stiawan, Y. Sazaki, and U. Ependi, “A LSTM-Method for Bitcoin Price Prediction: A Case Study Yahoo Finance Stock Market,” 2019 International Conference on Electrical Engineering and Computer Science (ICECOS), pp. 206–210, Oct. 2019, doi: 10.1109/ICECOS47637.2019.8984499.

U. Ependi;, A. F. Rochim, and A. Wibowo, “A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods,” International Journal of Intelligent Engineering and Systems, vol. 16, no. 3, pp. 345–361, 2023, doi: 10.22266/ijies2023.0630.28.

A. Ayub, “Prediksi Kinerja Time Series Well Performance Berdasarkan Long Short-Term Memory (LSTM),” Makassar, 2024.

M. Naufal, “Rendering Dinamis dan Interaksi Pengguna untuk Visualisasi Data Deret Waktu di Realitas X,” Jurnal Teknik ITS, vol. 13, no. 3, pp. 230–235, 2024.

Published
2025-12-15
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How to Cite
Billan, A., & Ependi, U. (2025). Oil and Gas Production Forecasting Based on LSTM Model: A Case Study of PT Pertamina Hulu Rokan Zone 4. Journal of Information Systems and Informatics, 7(4), 3903-3923. https://doi.org/10.63158/journalisi.v7i4.1285
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Vol 7 No 4 (2025): December
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