Using artificial neural network to predict porosity

  • Ta Quoc Dung
  • Le The Ha
  • Pham Duy Khang
Keywords: Geostatistic, Variogram, Kriging, artificial neural network

Abstract

The study presents the traditional geostatistic method and the new method using artificial neural network (ANN) to predict porosity. In the traditional method, Kriging algorithm is applied to find the spatial relationship of porosity in the reservoir through 2D models. In the new method, the "nnstart" tool of the Matlab software is applied to build the artificial neural network which will then be used to predict the porosity of the well being studied.
The results are compared with each other and prove that ANN has optimised the porosity prediction for the studied well.

References

1. Vũ Hữu Tiệp. Machine learning cơ bản. Nhà xuất bản Khoa học và Kỹ thuật. 2018.
2. Trương Xuân Luận. Lý thuyết địa thống kê. Đại học Mỏ - Địa chất.
3. Ridha B.C.Gharbi. An expert system for selecting and designing EOR processes. Journal of Petroleum Science and Engineering. 2000; 27(1 - 2): p. 33 - 47.
4. Elradi Abass, Cheng Lin Song. Artificial Intelligence selection with capability of editing a new parameter for EOR screening criteria. Journal of Engineering Science and Technology. 2011; 6(5): p. 628 - 638.
5. VietAI. Bài giảng mô hình Neural Network. 2018.
6. Geraldo A.R.Ramos, Lateef Akanji. Application of artificial intelligence for technical screening of enhanced oil recovery methods. Journal of Oil, Gas and Petrochemical Sciences. 2017.
7. Mohamed Sidahmed, Atish Roy, Anjum Sayed. Steamline rock facies classification with deep learning cognitive process. SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA. 9 - 11 October, 2017.
8. Nguyễn Hoàng Thiên. Ứng dụng mô hình địa thống kê dự đoán phân bố đặc tính vỉa. Đại học Bách khoa Tp. Hồ Chí Minh. 2012.
9. Phan Đăng Võ. Xác định độ rỗng và độ thấm thành hệ từ tài liệu địa vật lý giếng khoan sử dụng mạng nơ ron nhân tạo. 2018.
10. Tạ Quốc Dũng, Nguyễn Văn Thuận. Địa thống kê và ứng dụng trong dự báo các thông số địa cơ học. 2016.
11. Kelkar Mohan, Godofredo Perez, Anil Chopra. Applied geostatistics for reservoir characterization. Society of Petroleum Engineers. 2002.
12. Edward H.Isaaks, R.Mohan Srivastava. An introduction to applied geostatistics (1st edition). Oxford University Press. 1990.
Hình 2. Các phương pháp học của mạng neuron [4]
Published
2019-07-31
How to Cite
Ta Quoc Dung, Le The Ha, & Pham Duy Khang. (2019). Using artificial neural network to predict porosity . Petrovietnam Journal, 7, 18-27. Retrieved from https://pvj.vn/index.php/TCDK/article/view/166
Issue
Vol 7 (2019)
Section
Articles

1. The Author assigns all copyright in and to the article (the Work) to the Petrovietnam Journal, including the right to publish, republish, transmit, sell and distribute the Work in whole or in part in electronic and print editions of the Journal, in all media of expression now known or later developed.

2. By this assignment of copyright to the Petrovietnam Journal, reproduction, posting, transmission, distribution or other use of the Work in whole or in part in any medium by the Author requires a full citation to the Journal, suitable in form and content as follows: title of article, authors’ names, journal title, volume, issue, year, copyright owner as specified in the Journal, DOI number. Links to the final article published on the website of the Journal are encouraged.