PREDIKSI LAJU PENETRASI PENGEBORAN DENGAN MENGGUNAKAN PEMBELAJARAN MESIN PADA LAPANGAN GORGON DI AUSTRALIA

Authors

  • Muhammad Sayyid Anwar Al Mahbub UNIVERSITAS PERTAMINA
  • Dara Ayuda Maharsi Universitas Pertamina

Keywords:

Laju penetrasi (Rate of Penetration; ROP), machine learning, Multilayer Perceptron (MLP), Mean Absolute Percentage Error (MAPE), Generalisasi Model

Abstract

Studi ini menyelidiki prediksi laju penetrasi (Rate of Penetration; ROP) di Lapangan Gorgon, Australia, dengan menggunakan pendekatan machine learning berbasis Multilayer Perceptron Regressor (MLP). Tujuan penelitian ini adalah mengevaluasi pengaruh ukuran data pelatihan (training data size) dan pemilihan fitur (feature selection) terhadap kinerja model. Sebanyak tujuh dataset sumur (well datasets) digunakan dengan skenario eksperimental yang memvariasikan jumlah sumur pelatihan (dua dan tiga sumur) serta jumlah fitur input. Alur pengembangan model meliputi exploratory data analysis, data preprocessing, model training, hyperparameter tuning, dan evaluasi kinerja. Untuk merepresentasikan kondisi lapangan secara lebih realistis, diterapkan strategi well-based data splitting, di mana satu sumur diperlakukan sebagai blind data untuk menilai kemampuan generalisasi model. Evaluasi kinerja dilakukan menggunakan Mean Absolute Percentage Error (MAPE) dan koefisien determinasi (R2). Hasil menunjukkan bahwa peningkatan jumlah sumur pelatihan meningkatkan akurasi prediksi, sedangkan penambahan fitur tidak selalu meningkatkan kinerja dan dapat menurunkan generalisasi. Kinerja terbaik dicapai pada skenario tiga sumur dengan tiga fitur input (MAPE 25% dan R2 0,71), menegaskan pentingnya representativitas data dan pemilihan fitur dalam pengembangan model ROP yang robust.

Downloads

Published

28-02-2025

Issue

Vol. 3 No. 01 (2025): SUBSURFACE

Section

Articles

License

Copyright (c) 2026 SUBSURFACE

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

This is a human-readable summary of (and not a substitute for) the license.

You are free to:

  • Share — copy and redistribute the material in any medium or format
  • Adapt — remix, transform, and build upon the material

The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

  • NonCommercial — You may not use the material for commercial purposes.
  • ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

  • You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.
  • No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.