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One of the challenging topics in inverse modeling is numerical efficiency. For that, two techniques are combined in this study. As a forward model, streamline simulation technique is used. A probabilistic sensitivity analysis is proposed in the optimization procedure. From the sensitivity of the flow variables on the objective function, cumulative probability distribution functions (CDFs) are obtained. The CDFs are used to generate input flow variable fields. From the applications to various synthetic field cases, we conclude that the developed scheme improves the efficiency in inverse problems. The probabilistic sensitivity analysis makes it possible to generate the flow variable fields with consideration of the observed dynamic data. Reliable optimized results are also obtained efficiently by finding the hidden abnormal values although given samples poorly represent the characteristics of the reference field.
역산모델링의 수치적 효율성 향상을 위해서 본 연구에서는 전위모델로 유선시뮬레이션 기법을 사용하였다. 최적화 과정에서는 저류층 변수의 변화에 대한 민감도분석을 수행하여, 이를 확률적으로 모델링하였다. 이로부터 얻은 누적확률함수는 유동변수 교란의 지표로 사용된다. 다양한 참조필드에 적용해 본 결과 개발한 기법들은 역산모델링의 효율성을 높일 수 있다. 확률적 민감도분석을 통해 추계학적 필드생성 과정에서 동적자료 관측값을 고려한 교란을 주어 샘플링이 되지 않은 지역에서 이상값들의 분포를 찾아낼 수 있었다. 이를 바탕으로 주어진 정적자료가 필드의 공간분포를 대표하지 못하거나 동적자료 관측값을 반영하지 못하는 경우에도 역산모델링을 통해 물성분포를 신뢰성 있게 파악할 수 있다.
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- 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 :1
- Pages :1-10
Journal of the Korean Society of Mineral and Energy Resources Engineers
