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2009 Vol.46, Issue 2 Preview Page
30 April 2009. pp. 143-150
Abstract
SAGD has been widely used to extract oil from heavy oil and bitumen reservoir as in-situ recoverable technology. Effective steam injection was important because SAGD depended on the combustion of natural gas to generate steam of which cost was dominant. Numerical approach based on ANN (Artificial Neural Network) scheme was developed to obtain an optimized schedule of variable injection pressure in SAGD. ANN model predicts cSOR in SAGD while it does a production benefit in ES-SAGD. The model was verified by showing accurate prediction error, less than 5.5% compared with typical reservoir simulator. Optimized schedule decreased 26% of cSOR in SAGD while improved an economical value up to 34% in ES-SAGD rather than that of conventional approach, constant pressure scheduling.
SAGD공법(증기배유공법)은 중유나 비튜멘의 생산에 효과적이지만 증기 생성을 위해 다량의 담수와 천연가스를 사용하는 한계를 가지고 있다. 천연가스 사용이 생산 비용의 큰 부분을 차지하므로 적절한 압력의 증기주입은 SAGD공법의 경제성에 매우 중요하다. 이 연구에서는 SAGD공법의 에너지 효율을 향상시키고 다수의 증기 주입압력 시나리오의 신속한 평가를 위하여 인공신경망을 이용한 증기주입압력 최적화 모델을 개발하였다. 개발한 인공신경망 모델로 SAGD공법의 생산기간별 증기주입압력에 따른 누적 증기-오일비와 ES-SAGD공법의 생산이윤을 예측하였다. 개발한 모델을 대상 저류층에 적용한 결과, 저류층 시뮬레이션 결과와 개발 모델의 예측값이 상관계수 0.9 이상, 예측 오차 5.5% 이하로 인공신경망 모델의 예측 성능을 확인하였다. 생산기간동안 증기주입압력을 조절한 SAGD공법의 최적 시나리오는 일정 압력으로 증기를 주입하는 기존의 경우에 비해 26% 낮아진 누적 증기-오일비를 나타내었고, ES-SAGD도 34% 향상된 생산이윤을 얻었다.
References
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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 : 46
  • No :2
  • Pages :143-150