PREDIKSI PENYEBARAN HIDROKARBON MENGGUNAKAN ARTIFICIAL NEURAL NETWORK (ANN) DI FORMASI GUMAI, JAMBI

Authors

Keywords:

akustik impedansi, porositas, artificial neural network,

Abstract

Penelitian ini memaparkan karakteristik reservoir batugamping penghasil hidrokarbon menggunakan
artificial neural network di lapangan “X” Formasi Gumai, Cekungan Jambi. Lapangan ini menggunakan data
seismik 3D poststack, 2 buah sumur eksplorasi, 3 buah horizon, dan 3 buah marker. Untuk mengetahui potensi
sumur geotermal dilakukan prediksi temperatur dan tekanan dengan parameter lokasi, laju aliran injeksi dan
temperatur injeksi dengan menggunakan metode Artificial Neural Network (ANN). Yang pertama dilakukan
adalah integrasi data model produksi sumur sebanyak 2 buah sumur selama satu tahun dan dilakukan pemisahan
data yaitu data selama 11 bulan digunakan sebagai data pelatihan ANN dan data selama 1 bulan terakhir
digunakan sebagai data pengujian. Hasil prediksi dengan ANN akan dibandingkan dengan data pengujian.
Terlihat daerah yang porous berpotensi sebagai reservoir hidrokarbon karbonat di sekitar Gumai berkisar 27543-
28113 (m/s)*(g/cc), sedangkan di sekitar Talang Akar berkisar 26973-28683 (m/s)*(g/cc). Perhitungan nilai eror
antara hasil prediksi dengan data pengujian adalah berkisar 0.15% pada temperature (T) dan 0.25% pada tekanan
(P) dengan sumur-1 merupakan lokasi yang paling optimum. Hasil inversi penyebaran ini di-slice untuk
mendapatkan daerah porous yang berpotensi sebagai reservoir hidrokarbon pada lapangan “X” berdasarkan nilai
impedansi akustik dan porositas sumur di sekitarnya.

Author Biographies

  • Herman Santoso Pakpahan, Mulawarman University
    Teknik Informatika
  • Medi Taruk, Mulawarman University
    Teknik Informatika
  • Hario Jati Setyadi, Mulawarman University
    Teknik Informatika
  • Putut Pamilih Widagdo, Mulawarman University
    Bio Statement : 

     

  • Haviluddin Haviluddin, Mulawarman University

    Ilmu Komputer

  • Lilik Hendrajaya, ITB
    FMIPA ITB

References

Aanonsen, S.I., (2011): Optimizing Reservoir Performance Under Uncertainty with Application to Well Location. Paper SPE 30710.

Akin, S., Kok, M.V., Uraz, I. (2011): Optimization of Well Placement Geothermal

Reservoirs Using Artificial Intelligence. Computer Geoscience 36, 776-785.

Ali, J. K. (2013): Neural Networks: A New Tool for the Petroleum Industry. Paper SPE 27561, March 15-17.

Anderson, J. A. (2012): An Introduction to Neural Networks. Cambridge, MA: MIT

Press.

Axelsson, G., Zhilin, D. (2011): The Tanggu Geothermal Reservoir (Tianjin,

China). Geothermics, 27 (3), 271-294.

Beckner, B.L., et al. (2014): Field Development Planning Using SImulated

Annealing - Optimal Economic Well Scheduling and Placement. Paper SPE

Bodvarsson, G. (2011): Reinjection into Geothermal Reservoirs. Dordrecht:

Kluwer Academic.

Daniel, G. (2012): Principle Of Neural Network.

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Published

2017-09-16

Issue

Vol. 2 No. 2 (2017): Vol 2, No 2 (2017): Prosiding Seminar Nasional Ilmu Komputer dan Teknologi Informasi (SAKTI)

Section

Articles

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