Artificial Neural Network Approach to Selection of Enhanced Oil Recovery Method

doi:None

All Issue

2008 Vol.45, Issue 6 Preview Page Next Page

Artificial Neural Network Approach to Selection of Enhanced Oil Recovery Method 인공신경망을 이용한 석유 회수증진 기법 선정 연구: 이종용, 임종세
Jong-Yong Lee and Jong-Se Lim

31 December 2008. pp. 719-726

PDF

Abstract

Enhanced Oil Recovery (EOR) has gained more attention as the results of increasing oil prices and higher oil demands. A large number of EOR methods have been developed, but the selection of EOR method is still a challenging task to the petroleum engineer. One of the main tools used to predict the EOR efficiency is a reservoir simulation which requires detailed reservoir description that may not be available or unreliable at the initial evaluation stage, and also needs extensive time. The other method is to use an expert opinion, but it tends to be biased by the expert\\\'s operational experience. This paper proposes an Artificial Neural Network (ANN) approach to enable the petroleum engineer to select the appropriate EOR method based on the reservoir characteristics. The structure of the ANN is optimized to be consisting of four layers by the repeated trial and error during the training. After being trained successfully with the successful EOR field data, the ANN is tested against the new data which are not used for the training, and contain the certain level of error, respectively. The results showed that the ANN model developed in this study can be used to select the most appropriate EOR process in a time and cost effective way.

Keywords

Enhanced Oil Recovery

Artificial Neural Network

Reservoir properties

Selection of method

비재래형 탄화수소와 기생산유전의 잔존 석유에 대한 관심이 증가하면서 석유 회수증진 기법이 활발하게 적용되고 있으나 대상 저류층의 특성에 따라 적절한 기법을 선정하는 일은 여전히 어려운 문제이다. 회수증진기법 적용에 따른 저류층 거동 예측방법으로는 저류층 시뮬레이션과 전문가 의견에 의한 방법이 있으나, 저류층 시뮬레이션은 사업초기 신뢰하기 어려운 방대한 양의 자료와 연산시간을 필요로 하며, 전문가 의견은 전문가의 경험에 치우칠 우려가 있다. 이 연구에서는 사업초기 가용한 기초 저류층 물성을 이용하여 저류층 조건에 적합한 최적 회수증진기법을 선정하는 인공신경망 모델을 개발하였다. 인공신경망 모델은 반복된 학습에 의해 4계층으로 설계되었으며 회수증진기법이 성공적으로 적용된 현장자료에 의해 학습되었다. 학습이 종료된 인공신경망 모델의 적용성을 평가하기 위하여 오차시험 및 적용성 평가를 수행하였다. 이 연구에서 개발한 인공신경망 모델은 저류층 특성에 따른 적합한 석유 회수증진기법 선정 시 유용한 도구로 활용될 수 있을 것이다.

키워드

석유회수증진

인공신경망

저류층 물성

기법 선정

References

한국석유공사, 텍사스대, 2007, CO2 및 폴리머를 이용한 회수증진 연구(I), 한국석유공사, 경기, p.187.
Alvarado, V., Ranson, A., Hernández, K., Manrique, E., Matheus, J., Liscano, T., and Prosperi, N., 2002, “Selection of EOR/IOR Opportunities Based on Machine Learning,”
paper SPE 78332 presented at the SPE 13th European Petroleum Conference, Aberdeen, Scotland, UK, October 29-31.
Elemo, R.O. and Elmtalab, J., 1993, “A Practical Artificial Intelligence Application in EOR Projects,” SPE Computer Applications, Vol. 4, No. 2, pp. 17-21.
Goodlett, G.O., Honarpour, F.T., Chung, F.T., and Sarathi, P.S., 1986, “The Role of Screening and Laboratory Flow Studies in EOR Process Evaluation,” paper SPE 15172 presented at the Rocky Mountain Regional Meeting, Billings, Montana, USA, May 19-21.
Guerillot, D.R., 1988, “EOR Screening With an Expert System,” paper SPE 17791 presented at the Symposium on Petroleum Industry Applications of Microcomputers, San Jose, California, USA, June 27-29.
Moritis, G., 2006, “Worldwide EOR Survey,” Oil & Gas Journal, Vol. 104, Issue 15, pp. 45-57.
Shokir, E.M., Goda, H.M., Sayyouh, M.H., and Fattah, Kh.A., 2002, “Selection and Evaluation EOR Method Using Artificial Intelligence,” paper SPE 79163 presented at the 26rd Annual International Technical Conference and Exhibition, Abuja, Nigeria, August 5-7.
Surguchev, L. and Li, L., 2000, “IOR Evaluation and Applicability Screening Using Artificial Neural Networks,” paper SPE 59308 presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA, April 3-5.
Taber, J.J. and Martin, F.D., 1983, “Technical Screening Guides for the Enhanced Recovery of Oil,” paper SPE 12069 presented at the 58th Annual Technical Conference and Exhibition, San Francisco, California, USA, October 5-8.
Taber, J.J., Martin, F.D., and Seright, R.S., 1996a, “EOR Screening Criteria Revisited - Part1: Introduction to Screening Criteria and Enhanced Recovery Field Projects,” paper SPE 35385 presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA, April 21-24.
Taber, J.J., Martin, F.D., and Seright, R.S., 1996b, “EOR Screening Criteria Revisited - Part2: Application and Impact of Oil Prices,” paper SPE 39234 presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA, April 21-24.

Information

Publisher :The Korean Society of Mineral and Energy Resources Engineers
Publisher(Ko) :한국자원공학회
Journal Title :Journal of the Korean Society for Geosystem Engineering
Journal Title(Ko) :한국지구시스템공학회지
Volume : 45
No :6
Pages :719-726

[1] 한국석유공사, 텍사스대, 2007, CO2 및 폴리머를 이용한 회수증진 연구(I), 한국석유공사, 경기, p.187.

[2] Alvarado, V., Ranson, A., Hernández, K., Manrique, E., Matheus, J., Liscano, T., and Prosperi, N., 2002, “Selection of EOR/IOR Opportunities Based on Machine Learning,”

[3] paper SPE 78332 presented at the SPE 13th European Petroleum Conference, Aberdeen, Scotland, UK, October 29-31.

[4] Elemo, R.O. and Elmtalab, J., 1993, “A Practical Artificial Intelligence Application in EOR Projects,” SPE Computer Applications, Vol. 4, No. 2, pp. 17-21.

[5] Goodlett, G.O., Honarpour, F.T., Chung, F.T., and Sarathi, P.S., 1986, “The Role of Screening and Laboratory Flow Studies in EOR Process Evaluation,” paper SPE 15172 presented at the Rocky Mountain Regional Meeting, Billings, Montana, USA, May 19-21.

[6] Guerillot, D.R., 1988, “EOR Screening With an Expert System,” paper SPE 17791 presented at the Symposium on Petroleum Industry Applications of Microcomputers, San Jose, California, USA, June 27-29.

[7] Moritis, G., 2006, “Worldwide EOR Survey,” Oil & Gas Journal, Vol. 104, Issue 15, pp. 45-57.

[8] Shokir, E.M., Goda, H.M., Sayyouh, M.H., and Fattah, Kh.A., 2002, “Selection and Evaluation EOR Method Using Artificial Intelligence,” paper SPE 79163 presented at the 26rd Annual International Technical Conference and Exhibition, Abuja, Nigeria, August 5-7.

[9] Surguchev, L. and Li, L., 2000, “IOR Evaluation and Applicability Screening Using Artificial Neural Networks,” paper SPE 59308 presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA, April 3-5.

[10] Taber, J.J. and Martin, F.D., 1983, “Technical Screening Guides for the Enhanced Recovery of Oil,” paper SPE 12069 presented at the 58th Annual Technical Conference and Exhibition, San Francisco, California, USA, October 5-8.

[11] Taber, J.J., Martin, F.D., and Seright, R.S., 1996a, “EOR Screening Criteria Revisited - Part1: Introduction to Screening Criteria and Enhanced Recovery Field Projects,” paper SPE 35385 presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA, April 21-24.

[12] Taber, J.J., Martin, F.D., and Seright, R.S., 1996b, “EOR Screening Criteria Revisited - Part2: Application and Impact of Oil Prices,” paper SPE 39234 presented at the SPE/DOE Improved Oil Recovery Symposium, Tulsa, Oklahoma, USA, April 21-24.

Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

All Issue