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
Abstract
Gas serpih adalah jenis gas alam yang dihasilkan dan terperangkap dalam batuan serpih yang kaya material organik. Potensi sebagai batuan induk gas serpih harus dibuktikan melalui analisis geokimia. Analisis geokimia hidrokarbon meliputi parameter kekayaan/total karbon organik (TOC), kematangan, dan jenis kerogen. Parameter kekayaan (TOC) hanya tersedia untuk empat sumur (SA-13, SA-11, E-5, dan JB-7), sehingga TOC sintetik harus dimodelkan. Artificial Neural Network (ANN) dan Adaptive Neuro Fuzzy Inference System (ANFIS) adalah jenis kecerdasan artifisial yang memberikan cara paling efektif untuk menentukan nilai TOC di sumur tanpa data hasil analisis laboratorium. Metode ANN adalah metode terbaik di Sub-cekungan Arjuna Selatan, E15, dan Jatibarang bagian barat dengan nilai korelasi 0,98. Metode ANFIS adalah metode terbaik di Jatibarang bagian timur dengan nilai korelasi mencapai 0,88.
Downloads
References
2. Waldo, D. (2012). A Review of Three North American Shale Plays: Learnings from Shale Gas Exploration in the Americas. Geoscience Technology Workshop (GTW) on Unconventional Hydrocarbons, 80214(March), 15–16.
3. Pertamina (2008). Laporan Geologi Regional. Asset 3 PT Pertamina EP. (Online).
4. Passey, Q. R., Creaney, S., Kulla, J. B., Moretti, F. J., & Stroud, J. D. (1990). A practical model for organic richness from porosity and resistivity logs. In American Association of Petroleum Geologists Bulletin (Vol. 74, Issue 12, pp. 1777–1794). https://doi.org/10.1306/0c9b25c9-1710-11d7-8645000102c1865d
5. Zhu, L., Zhang, C., Zhang, Z., Zhou, X., & Liu, W. (2019). An improved method for evaluating the TOC content of a shale formation using the dual-difference ΔlogR method. Marine and Petroleum Geology, 102(July 2018), 800–816. https://doi.org/10.1016/j.marpetgeo.2019.01.031
6. Kamali, M. R., & Mirshady, A. A. (2004). Total organic carbon content determined from well logs using ΔLogR and Neuro Fuzzy techniques. Journal of Petroleum Science and Engineering, 45(3–4), 141–148. https://doi.org/10.1016/j.petrol.2004.08.005
7. Bai, Y., & Tan, M. (2021). Dynamic committee machine with fuzzy-c-means clustering for total organic carbon content prediction from wireline logs. Computers and Geosciences, 146(October 2020), 104626. https://doi.org/10.1016/j.cageo.2020.104626
8. Ahangari, D., Daneshfar, R., Zakeri, M., Ashoori, S., & Soulgani, B. S. (2022). On the prediction of geochemical parameters (TOC, S1 and S2) by considering well log parameters using ANFIS and LSSVM strategies. Petroleum, 8(2), 174–184. https://doi.org/10.1016/j.petlm.2021.04.007
9. Kadkhodaie-Ilkhchi, A., Rahimpour-Bonab, H., & Rezaee, M. (2009). A committee machine with intelligent systems for estimation of total organic carbon content from petrophysical data: An example from Kangan and Dalan reservoirs in South Pars Gas Field, Iran. Computers and Geosciences, 35(3), 459–474. https://doi.org/10.1016/j.cageo.2007.12.007
10. Shalaby, M. R., Jumat, N., Lai, D., & Malik, O. (2019). Integrated TOC prediction and source rock characterization using machine learning, well logs and geochemical analysis: Case study from the Jurassic source rocks in Shams Field, NW Desert, Egypt. Journal of Petroleum Science and Engineering, 176(December 2018), 369–380. https://doi.org/10.1016/j.petrol.2019.01.055
11. Qian, K., Ning, J., Liu, X., & Zhang, Y. (2019). A rock physics driven Bayesian inversion for TOC in the Fuling shale gas reservoir. Marine and Petroleum Geology, 102(January), 886–898. https://doi.org/10.1016/j.marpetgeo.2019.01.011
12. Rui, J., Zhang, H., Zhang, D., Han, F., & Guo, Q. (2019). Total organic carbon content prediction based on support-vector-regression machine with particle swarm optimization. Journal of Petroleum Science and Engineering, 180(April), 699–706. https://doi.org/10.1016/j.petrol.2019.06.014
13. Wang, H., Wu, W., Chen, T., Dong, X., & Wang, G. (2019). An improved neural network for TOC, S1 and S2 estimation based on conventional well logs. Journal of Petroleum Science and Engineering, 176(January), 664–678. https://doi.org/10.1016/j.petrol.2019.01.096
14. Zhu, L., Zhang, C., Zhang, C., Zhang, Z., Zhou, X., Liu, W., & Zhu, B. (2020). A new and reliable dual model- and data-driven TOC prediction concept: A TOC logging evaluation method using multiple overlapping methods integrated with semi-supervised deep learning. Journal of Petroleum Science and Engineering, 188(November 2019), 106944. https://doi.org/10.1016/j.petrol.2020.106944
15. Zheng, D., Wu, S., & Hou, M. (2021). Fully connected deep network: An improved method to predict TOC of shale reservoirs from well logs. Marine and Petroleum Geology, 132(December 2020), 105205. https://doi.org/10.1016/j.marpetgeo.2021.105205
16. Amosu, A., Imsalem, M., & Sun, Y. (2021). Effective machine learning identification of TOC-rich zones in the Eagle Ford Shale. Journal of Applied Geophysics, 188, 104311. https://doi.org/10.1016/j.jappgeo.2021.104311
17. Wang, H., Lu, S., Qiao, L., Chen, F., He, X., Gao, Y., & Mei, J. (2022). Unsupervised contrastive learning for few-shot TOC prediction and application. International Journal of Coal Geology, 259(December 2021), 104046. https://doi.org/10.1016/j.coal.2022.104046
18. Zhang, W., Shan, X., Fu, B., Zou, X., & Fu, L. Y. (2022). A deep encoder-decoder neural network model for total organic carbon content prediction from well logs. Journal of Asian Earth Sciences, 240(May), 105437. https://doi.org/10.1016/j.jseaes.2022.105437
19. Zhao, P., Mao, Z., Huang, Z., & Zhang, C. (2016). A new method for estimating total organic carbon content from well logs. AAPG Bulletin, 100(8), 1311–1327. https://doi.org/10.1306/02221615104
20. Rui, J., Zhang, H., Ren, Q., Yan, L., Guo, Q., & Zhang, D. (2020). TOC content prediction based on a combined Gaussian process regression model. Marine and Petroleum Geology, 118(May), 104429. https://doi.org/10.1016/j.marpetgeo.2020.104429
21. Yu, H., Rezaee, R., Wang, Z., Han, T., Zhang, Y., Arif, M., & Johnson, L. (2017). A new method for TOC estimation in tight shale gas reservoirs. International Journal of Coal Geology, 179(May), 269–277. https://doi.org/10.1016/j.coal.2017.06.011
22. Zeng, B., Li, M., Zhu, J., Wang, X., Shi, Y., Zhu, Z., Guo, H., & Wang, F. (2021). Selective methods of TOC content estimation for organic-rich interbedded mudstone source rocks. Journal of Natural Gas Science and Engineering, 93(January), 104064. https://doi.org/10.1016/j.jngse.2021.104064
23. Khoshnoodkia, M., Mohseni, H., Rahmani, O., & Mohammadi, A. (2011). TOC determination of Gadvan Formation in South Pars Gas field, using artificial intelligent systems and geochemical data. Journal of Petroleum Science and Engineering, 78(1), 119–130. https://doi.org/10.1016/j.petrol.2011.05.010
24. Dellenbach, J., Espitalie, J. and Lebreton, F. (1983) Source rock logging. Transactions of the 8th European SPWLA Symposium, paper D.
25. Nixon, R. P. (1973). Oil Source Beds in Cretaceous Mowry Shale of Northwestern Interior United States. American Association of Petroleum Geologists Bulletin, 57(1), 136–161. https://doi.org/10.1306/819a4250-16c5-11d7-8645000102c1865d
26. Meissner, F. F. (1978) Petroleum geology of the Bakken Formation Williston basin, North Dakota, and Montana. In: The Economic Geology of the Williston Basin: Montana Geological Society, 1987 Williston Basin Symposium, 207-227.
27. Schmoker, J. W., & Hester, T. C. (1983). Organic Carbon in Bakken Formation, United States Portion of Williston Basin. American Association of Petroleum Geologists Bulletin, 67(12), 2165–2174. https://doi.org/10.1306/ad460931-16f7-11d7-8645000102c1865d
28. Schmoker, J. W. (1979). Determination of Organic Content of Appalachian Devonian Shales from Formation-Density Logs: GEOLOGIC NOTES. AAPG Bulletin, 63(9), 1504–1509. https://doi.org/10.1306/2f9185d1-16ce-11d7-8645000102c1865d
29. Mathworks (2017). Neural Network Toolbox. MathWorks. (Online).
30. Andrian, D., Rosid, M. S., & Septyandy, M. R. (2020). Pore Pressure Prediction using Anfis Method on Well and Seismic Data Field “ayah.” IOP Conference Series: Materials Science and Engineering, 854(1). https://doi.org/10.1088/1757-899X/854/1/012041
This work is licensed under a Creative Commons Attribution 4.0 International License.
Penulis mengirimkan naskah dan pengertian bahwa jika diterima untuk proses dipublikasi, hak cipta dari artikel tersebut akan diberikan kepada jurnal ilmiah teknologi informasi asia. Jurnal ilmiah teknologi informasi asia (Jitika) dan Lp2m Stmik Asia Malang sebagai penerbit jurnal, komponen Hak cipta mencakup hak untuk mereproduksi dan mengirimkan artikel dalam semua bentuk dan media, termasuk cetak ulang, foto, mikrofilm, dan reproduksi serupa lainnya, serta terjemahannya.
Jurnal ilmiah teknologi infomasi asia, dan Lp2m ITB Asia Malang, beserta jajaran para redaksi berusaha keras untuk memastikan bahwa tidak ada data, opini, atau pernyataan yang salah atau menyesatkan ketika dipublikasikan di jurnal Jitika, dengan kondisi apapun, isi artikel dan iklan yang diterbitkan di Jurnal ilmiah teknologi infomasi asia adalah murni merupakan tanggung jawab masing-masing penulis dan pengiklan. Pengguna situs web ini akan dilisensikan dengan menggunakan materi dari situs web ini setelah Lisensi Internasional Creative Commons Attribution 4.0. Tidak ada biaya yang dibebankan. Silakan gunakan materi yang sesuai.
Anda bebas untuk:
Bagikan - salin dan sebarkan materi dalam media atau format apa pun.
Adaptasi - remix, transformasikan, dan bangun berdasarkan materi untuk tujuan apa pun, bahkan secara komersial.
Pemberi lisensi tidak dapat mencabut kebebasan ini selama Anda mengikuti ketentuan lisensi
