Penentuan Model Total Organic Carbon dengan Menggunakan Metode Artificial Neural Network dan Adaptive Neuro Fuzzy Inference System untuk Estimasi Potensi Gas Serpih di Cekungan Jawa Barat Utara

Penulis

  • Muhammad Rizqy Septyandy Program Studi Teknik Geologi, Fakultas Teknik, Universitas Mulawarman http://orcid.org/0000-0002-4130-5949
  • Eddy Ariyono Subroto Program Studi Teknik Geologi, Fakultas Ilmu dan Teknologi Kebumian, Institut Teknologi Bandung
  • Aveliansyah Aveliansyah Pertamina Hulu Energi Offshore Southeast Sumatra (PHE OSES)

DOI:

https://doi.org/10.32815/jitika.v17i1.889

Kata Kunci:

Artificial Neural Network, Adaptive Neuro Fuzzy Inference System, Total Organic Carbon, Gas Serpih, Geokimia

Abstrak

Shale gas is a kind of natural gas that is generated and trapped in organic-rich shale rocks. The potential as a source rock for shale gas must, however, be demonstrated through geochemical investigation. The geochemical examination of hydrocarbons comprises a richness parameter/total organic carbon (TOC), maturity, and kind of kerogen. Richness (TOC) parameter is only available for four wells (SA-13, SA-11, E-5, and JB-7), hence synthetic TOC must be modeled. Artificial neural networks (ANN) and adaptive neuro fuzzy inference system (ANFIS) are components of artificial intelligence that provide the most effective way for determining the value of TOC in wells with no TOC Analysis Result data. With a correlation value of 0.98, the ANN approach is the best method in the South Arjuna, E15, and western Jatibarang Subbasins. With a correlation value of 0.88, ANFIS is the most effective approach in eastern Jatibarang.

Unduhan

Data unduhan belum tersedia.

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Unduhan

Diterbitkan

2023-01-25

Cara Mengutip

Septyandy, M. R., Subroto, E. A., & Aveliansyah, A. (2023). Penentuan Model Total Organic Carbon dengan Menggunakan Metode Artificial Neural Network dan Adaptive Neuro Fuzzy Inference System untuk Estimasi Potensi Gas Serpih di Cekungan Jawa Barat Utara. Jurnal Ilmiah Teknologi Informasi Asia, 17(1), 83–96. https://doi.org/10.32815/jitika.v17i1.889

Terbitan

Vol 17 No 1 (2023): Volume 17 Nomor 1 (8)

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